Results for class matrix

Found 152556 results in 8696 files, showing top 50 files (show more).

RNAmodR:R/SequenceData-class.R: [ ]
194: S4Vectors::setValidity2(Class = "SequenceData", .valid.SequenceData)
121: sequenceDataClass <- function(dataType){
219: classes <- unlist(lapply(from,class))
239: className <- sequenceDataClass(type)
306: classes <- lapply(args,class)
330: classes <- lapply(args,class)
584: className <- sequenceDataClass(dataType)
3: #' @include SequenceDataFrame-class.R
7: #' @name SequenceData-class
10: #' @title The SequenceData class
15: #' The \code{SequenceData} class is implemented to contain data on each position
19: #' \code{SequenceData} class is a virtual class, from which specific classes can
23: #' \item{\code{\link[=CoverageSequenceData-class]{CoverageSequenceData}}}
24: #' \item{\code{\link[=EndSequenceData-class]{End5SequenceData}},
25: #' \code{\link[=EndSequenceData-class]{End3SequenceData}},
26: #' \code{\link[=EndSequenceData-class]{EndSequenceData}}}
27: #' \item{\code{\link[=NormEndSequenceData-class]{NormEnd5SequenceData}},
28: #' \code{\link[=NormEndSequenceData-class]{NormEnd5SequenceData}}}
29: #' \item{\code{\link[=PileupSequenceData-class]{PileupSequenceData}}}
30: #' \item{\code{\link[=ProtectedEndSequenceData-class]{ProtectedEndSequenceData}}}
39: #' The \code{SequenceData} class is derived from the
40: #' \code{\link[IRanges:DataFrameList-class]{CompressedSplitDataFrameList}} class
46: #' The \code{SequenceDataFrame} class is a virtual class and contains data for
48: #' elements from a \code{SequenceData} object, the SequenceDataFrame class is
50: #' \code{\link[=SequenceData-class]{SequenceData}} object. Therefore, a matching
51: #' \code{SequenceData} and \code{SequenceDataFrame} class must be implemented.
53: #' The \code{SequenceDataFrame} class is derived from the
54: #' \code{\link[S4Vectors:DataFrame-class]{DataFrame}} class.
60: #' @param dataType The prefix for construction the class name of the
84: #' \code{minQuality = 5L}, but this is class dependent).}
91: #' @slot sequencesType a \code{character} value for the class name of
120: # class names must be compatible with this class name generation function
125: stop("Class '",ans,"' not found: ",tmp)
131: function(x) class(x)
158: matrix(unlist(data_col_types, use.names = FALSE), nrow = 1,
163: cat("- ",class(seqinfo(object)), " object with ",
231: stop("Cannot coerce ",class(from)," to ",className,".")
296: #' @rdname SequenceData-class
320: #' @rdname SequenceData-class
520: .SequenceDataFrame(class = gsub("SequenceData","",className),
603: # create the class
667: #' @rdname SequenceData-class
676: #' @rdname SequenceData-class
684: #' @rdname SequenceData-class
692: #' @rdname SequenceData-class
700: #' @rdname SequenceData-class
708: #' @rdname SequenceData-class
716: #' @rdname SequenceData-class
724: #' @rdname SequenceData-class
732: #' @rdname SequenceData-class
740: #' @rdname SequenceData-class
748: #' @rdname SequenceData-class
756: #' @rdname SequenceData-class
764: #' @rdname SequenceData-class
783: stop("This functions needs to be implemented by '",class(x),"'.",
867: "genomic coordinates. Contact the maintainer of the class used.",
888: stop("This functions needs to be implemented by '",class(x),"'.",
20: #' be extended. Currently the following classes are implemented:
61: #' \code{SequenceData} subclass to be constructed.
76: #' \code{SequenceData} classes use all arguments. The arguments are:
102: setClass("SequenceData",
123: tmp <- try(getClass(ans))
130: setMethod("classNameForDisplay", "SequenceData",
143: cat(classNameForDisplay(object), " with ", k, " elements ",
155: paste0("<", classNameForDisplay(x)[1],">")
198: coerceSequenceDataToCompressedSplitDataFrameList <- function(className){
200: signature = c(from = className, to = "CompressedSplitDataFrameList"),
216: coerceToSequenceData <- function(className) {
220: from <- from[classes == paste0(className,"Frame")]
222: FUN <- match.fun(className)
226: if(is(from,className)){
228: } else if(is(from,paste0(className,"Frame"))) {
240: coerceSequenceDataToCompressedSplitDataFrameList(className)
241: setAs("ANY", className, coerceToSequenceData(className))
242: setAs("list", className, coerceToSequenceData(className))
307: if(length(unique(classes)) != 1L){
331: if(length(unique(classes)) != 1L){
458: .SequenceData <- function(className, bamfiles, ranges, sequences, seqinfo, args,
466: proto <- new(className)
474: stop("Minimum quality is not set for '", className ,"'.",
528: ans <- new(className,
586: bamfiles <- .norm_bamfiles(bamfiles, className)
588: annotation <- .norm_annotation(annotation, className)
589: sequences <- .norm_sequences(sequences, className)
591: seqinfo <- .norm_seqnames(bamfiles, annotation, sequences, seqinfo, className)
604: .SequenceData(className, bamfiles, grl, sequences, seqinfo, args)
861: # this needs to be implemented by each subclass
457: #' @importClassesFrom IRanges PartitioningByWidth PartitioningByEnd

ExploreModelMatrix:R/ExploreModelMatrix.R: [ ]
177: class = "fa fa-question-circle",
658: class = "fa fa-question-circle",
55: ExploreModelMatrix <- function(sampleData = NULL, designFormula = NULL) {
1: #' Explore model matrix
5: #' matrix graphically in an interactive application.
41: #' @importFrom stats model.matrix as.formula relevel cov2cor
90: title = paste0("Design matrix visualization (ExploreModelMatrix v",
199: class = "fa fa-question-circle",
215: id = "design_matrix_box",
219: "Design matrix",
221: shiny::div(id = "paneltour_design_matrix_box",
224: class = "fa fa-question-circle",
229: # title = "Design matrix",
232: inputId = "design_matrix_type",
237: shiny::uiOutput("design_matrix")
243: id = "design_matrix_rank_box",
249: shiny::div(id = "paneltour_design_matrix_rank_box",
252: class = "fa fa-question-circle",
259: "Rank of design matrix: ",
260: shiny::textOutput("design_matrix_rank"),
261: "Number of columns in design matrix: ",
262: shiny::textOutput("design_matrix_ncol"),
264: "observations - rank of design matrix): "),
265: shiny::textOutput("design_matrix_resdf")
274: id = "pinv_design_matrix_box",
278: "Pseudoinverse of design matrix",
280: shiny::div(id = "paneltour_pinv_design_matrix_box",
283: class = "fa fa-question-circle",
288: # title = "Pseudoinverse of design matrix",
291: shiny::uiOutput("pinv_design_matrix")
306: class = "fa fa-question-circle",
324: id = "cooccurrence_matrix_box",
330: shiny::div(id = "paneltour_cooccurrence_matrix_box",
333: class = "fa fa-question-circle",
340: shiny::uiOutput("cooccurrence_matrix")
346: id = "correlation_matrix_box",
352: shiny::div(id = "paneltour_correlation_matrix_box",
355: class = "fa fa-question-circle",
363: shiny::uiOutput("correlation_matrix")
447: label = "Text size, matrix entries",
465: label = "Text size, matrix entries",
486: label = "Text size, matrix entries",
498: label = "Text size, matrix entries",
561: # Define input to drop columns in design matrix -------------------------
569: mm <- stats::model.matrix(stats::as.formula(input$designformula),
678: # Generate design matrix ------------------------------------------------
679: output$design_matrix_R <- shiny::renderPrint({
691: output$design_matrix_DT <- DT::renderDataTable({
706: output$design_matrix <- shiny::renderUI({
707: if (input$design_matrix_type == "R output") {
708: shiny::verbatimTextOutput("design_matrix_R")
709: } else if (input$design_matrix_type == "data table") {
710: DT::dataTableOutput("design_matrix_DT")
712: stop("Unknown design matrix display type")
716: # Plot design matrix pseudoinverse --------------------------------------
717: output$pinv_design_matrix_plot <- shiny::renderPlot({
768: output$pinv_design_matrix <- shiny::renderUI({
782: shiny::plotOutput("pinv_design_matrix_plot",
788: output$correlation_matrix_plot <- shiny::renderPlot({
845: output$correlation_matrix <- shiny::renderUI({
859: shiny::plotOutput("correlation_matrix_plot",
908: # Check rank and number of columns of design matrix ---------------------
909: output$design_matrix_rank <- shiny::renderText({
925: output$design_matrix_ncol <- shiny::renderText({
941: output$design_matrix_resdf <- shiny::renderText({
974: msg <- paste0("The design matrix is not full rank.<br><br> ",
976: "in the design matrix can be obtained by ",
984: "and columns in the design matrix with all zero ",
1018: "in your design matrix as you have samples in ",
1044: # Generate design matrix plot -------------------------------------------
1075: # Plot cooccurrence matrix ----------------------------------------------
1076: output$cooccurrence_matrix_plot <- shiny::renderPlot({
1093: output$cooccurrence_matrix <- shiny::renderUI({
1101: shiny::plotOutput("cooccurrence_matrix_plot",
1123: "sample_table_summary_box", "design_matrix_box",
1124: "design_matrix_rank_box", "pinv_design_matrix_box",
1125: "vifs_box", "cooccurrence_matrix_box",
1126: "correlation_matrix_box"),
23: #' app <- ExploreModelMatrix(
91: utils::packageVersion("ExploreModelMatrix"), ")"),
612: designMatrix = NULL)
688: generated_output()$designmatrix
700: DT::datatable(data.frame(generated_output()$designmatrix,
797: if (is.null(generated_output()$designmatrix)) {
800: tmp <- generated_output()$designmatrix
918: if (is.null(generated_output()$designmatrix)) {
921: qr(generated_output()$designmatrix)$rank
934: if (is.null(generated_output()$designmatrix)) {
937: ncol(generated_output()$designmatrix)
950: if (is.null(generated_output()$designmatrix)) {
953: nrow(generated_output()$designmatrix) -
954: qr(generated_output()$designmatrix)$rank
966: if (is.null(generated_output()$designmatrix)) {
969: if (qr(generated_output()$designmatrix)$rank >=
970: ncol(generated_output()$designmatrix)) {
973: nonestim <- limma::nonEstimable(generated_output()$designmatrix)
1009: if (is.null(generated_output()$designmatrix)) {
1012: if (qr(generated_output()$designmatrix)$rank <
1013: nrow(generated_output()$designmatrix)) {
1109: package = "ExploreModelMatrix"),
1131: package = "ExploreModelMatrix"),

VariantAnnotation:R/methods-VCF-class.R: [ ]
20: class <- "CollapsedVCF"
503: .showVCFSubclass <- function(object)
447: SnpMatrixToVCF <- function(from, seqSource)
2: ### VCF class methods
29: class <- "ExpandedVCF"
39: new(class, SummarizedExperiment(assays=geno, rowRanges=rowRanges,
229: setReplaceMethod("geno", c("VCF", "character", "matrix"),
237: setReplaceMethod("geno", c("VCF", "numeric", "matrix"),
245: setReplaceMethod("geno", c("VCF", "missing", "matrix"),
384: if (!.compare(lapply(args, class)))
385: stop("'...' objects must be of the same VCF class")
422: if (!.compare(lapply(args, class)))
423: stop("'...' objects must be of the same VCF class")
482: GT <- matrix(GT, byrow=TRUE, ncol=nrowGT)
546: cat("class:", classNameForDisplay(object), "\n")
526: cat(margin, classNameForDisplay(x), " with ",
534: cat(margin, classNameForDisplay(x), " with ",
542: cat(margin, classNameForDisplay(x), " of length ", lo,

TENxIO:R/TENxFileList-class.R: [ ]
255: matrix <- grep("matrix.mtx", names(fdata), fixed = TRUE, value = TRUE)
40: validClasses <- vapply(object, function(ifile) {
3: #' @description This class was designed to mainly handle tarballs from
8: #' 1. `matrix.mtx.gz` - the counts matrix
24: #' @return A `TENxFileList` class object
28: Class = "TENxFileList",
46: "Some files are not of class 'TENxFile'"
66: #' 1. `matrix.mtx.gz` - the counts matrix
89: #' "extdata", "pbmc_granulocyte_sorted_3k_ff_bc_ex_matrix.tar.gz",
133: #' TSVFile: A class to represent 10x tab separated values files
135: #' This class is general purpose for reading in tabular data from the
136: #' 10x Genomics website with the `.tsv` file extension. The class also supports
139: #' @return A `TSVFile` class object; a `tibble` for the import method
142: .TSVFile <- setClass(Class = "TSVFile", contains = "TENxFile")
182: #' @describeIn TENxFileList-class Obtain file paths for all files in the object
192: #' @describeIn TENxFileList-class An intermediate method for decompressing
243: #' @describeIn TENxFileList-class Recursively import files within a
258: mat <- datalist[[matrix]]
26: #' @exportClass TENxFileList
27: .TENxFileList <- setClass(
43: if (all(validClasses))

RNAmodR:R/Modifier-class.R: [ ]
327: S4Vectors::setValidity2(Class = "Modifier", .valid_Modifier)
2: #' @include SequenceData-class.R
3: #' @include SequenceDataSet-class.R
4: #' @include SequenceDataList-class.R
13: #' @name Modifier-class
16: #' @title The Modifier class
19: #' The \code{Modifier} class is a virtual class, which provides the central
36: #' implemented to store additional arguments, which the base class does not
40: #' \code{Modifier()} with a \code{className} matching the specific class to be
80: #' @param className The name of the class which should be constructed.
86: #' requirements of specific \code{Modifier} class.}
99: #' @param seqinfo An optional \code{\link[GenomeInfoDb:Seqinfo-class]{Seqinfo}}
107: #' \item{additional parameters depending on the specific \code{Modifier} class}
119: #' \code{\link[Modstrings:ModDNAString]{ModDNAString}} class.
121: #' @slot dataType the class name(s) of the \code{SequenceData} class used
154: #' @param x,object a \code{Modifier} or \code{ModifierSet} class
166: #' \item{\code{modifierType}:} {a character vector with the appropriate class
167: #' Name of a \code{\link[=Modifier-class]{Modifier}}.}
169: #' the \code{Modifier} class.}
171: #' "RNA" or "DNA" modifications are detected by the \code{Modifier} class.}
192: #' modifierType(mi) # The class name of the Modifier object
212: #' @rdname Modifier-class
246: elementTypes <- vapply(data,class,character(1))
249: " ",class(x),". '",paste(dataType(x), collapse = "','"),"' are ",
255: class(x),". '",paste(dataType(x), collapse = "','"),"' are ",
352: as.matrix(format(as.data.frame(
356: matrix(unlist(lapply(settings[[i]], function(x) {
372: cat("A", class(object), "object containing",dataType(object),
471: definition = function(x){class(x)[[1L]]})
601: #' @param x a \code{Modifier} or \code{ModifierSet} class
745: "required SequenceData class names.",
750: "class names.",
753: bamfiles <- bamfiles[match(class,names(bamfiles))]
767: function(class){
768: do.call(class, c(list(bamfiles = bamfiles,
837: #' @rdname Modifier-class
846: #' @rdname Modifier-class
854: #' @rdname Modifier-class
862: #' @rdname Modifier-class
870: #' @rdname Modifier-class
879: #' @rdname Modifier-class
888: #' @rdname Modifier-class
907: #' \code{\link[=SequenceData-class]{SequenceData}} object and can be used
908: #' directly on a \code{\link[=SequenceData-class]{SequenceData}} object or
909: #' indirectly via a \code{\link[=Modifier-class]{Modifier}} object.
912: #' \code{\link[=SequenceData-class]{SequenceData}} object, the result summarized
913: #' as defined for the individual \code{Modifier} class and stored in the
919: #' \code{Modifier} class. The stored data from the \code{aggregate} slot can be
928: #' @param x a \code{\link[=SequenceData-class]{SequenceData}},
930: #' \code{\link[=Modifier-class]{Modifier}} or
931: #' \code{\link[=Modifier-class]{ModfierSet}} object.
957: #' \item{\code{\link[=SequenceData-class]{SequenceData}}} {a
959: #' \item{\code{\link[=SequenceDataSet-class]{SequenceDataSet}} or
960: #' \code{\link[=SequenceDataList-class]{SequenceDataList}}} {a \code{SimpleList}
962: #' \item{\code{\link[=Modifier-class]{Modifier}} or
963: #' \code{\link[=ModifierSet-class]{ModifierSet}}} {an updated \code{Modifier}
982: "Contact the maintainer of the class used.",
987: "maintainer of the class used.",
993: "genomic coordinates. Contact the maintainer of the class used.",
1024: '",class(x),"'.",call. = FALSE)
1058: #' \code{\link[=Modifier-class]{Modifier}} class. Usually this is done
1069: #' object and has to be implemented for each individual \code{Modifier} class.
1118: '",class(x),"'.",call. = FALSE)
1124: #' @rdname Modifier-class
1130: #' @rdname Modifier-class
23: #' Each subclass has to implement the following functions:
138: #' @return a \code{Modifier} object of type \code{className}
146: #' For the \code{Modifier} and \code{ModifierSet} classes a number of functions
214: setClass("Modifier",
216: slots = c(seqtype = "character", # this have to be populated by subclass,
217: mod = "character", # this have to be populated by subclass
218: score = "character", # this have to be populated by subclass
219: dataType = "list_OR_character", # this have to be populated by subclass
355: classinfo <-
357: paste0("<", classNameForDisplay(x)[1],
361: out <- rbind(classinfo, out)
697: ans <- getClass(ans)@prototype
708: .Modifier <- function(className, data){
709: proto <- new(className) # create prototype object for mod normalization only
720: new(className,
729: .load_SequenceData <- function(classes, bamfiles, annotation, sequences,
731: if(is.list(classes)){
738: if(length(classes) != length(bamfiles)){
739: stop("'x' has invalid length. '",paste(classes, collapse = "' and '"),
740: "' ",ifelse(length(classes) > 1L,"are","is")," required.",
748: if(all(classes %in% names(bamfiles))){
754: data <- bpmapply(.load_SequenceData, classes, bamfiles,
760: data <- bplapply(classes, .load_SequenceData, bamfiles,
765: } else if(is.character(classes)){
766: data <- lapply(classes,
781: .new_ModFromCharacter <- function(className, x, annotation, sequences, seqinfo,
783: # Check that external classes are implemented correctly
784: className <- .norm_modifiertype(className)
786: proto <- new(className)
789: return(new2(className, mod = .norm_mod(proto)))
791: bamfiles <- .norm_bamfiles(x, className) # check bam files
796: annotation <- .norm_annotation(annotation, className)
797: sequences <- .norm_sequences(sequences, className)
798: seqinfo <- .norm_seqnames(bamfiles, annotation, sequences, seqinfo, className)
806: .new_ModFromSequenceData(className, data, ...)
809: .new_ModFromSequenceData <- function(className, x, ...){
811: ans <- .Modifier(className, x)
842: def = function(className, x, annotation, sequences, seqinfo, ...)
850: function(className, x, annotation = NULL, sequences = NULL,
852: .new_ModFromSequenceData(className, x, ...)
858: function(className, x, annotation = NULL, sequences = NULL,
860: .new_ModFromSequenceData(className, x, ...)
866: function(className, x, annotation = NULL, sequences = NULL,
868: .new_ModFromSequenceData(className, x, ...)
874: function(className, x, annotation = NULL, sequences = NULL,
876: .new_ModFromCharacter(className, x, annotation, sequences, seqinfo,
883: function(className, x, annotation = NULL, sequences = NULL,
885: .new_ModFromCharacter(className, x, annotation, sequences, seqinfo,
892: function(className, x, annotation = NULL, sequences = NULL,
894: .new_ModFromCharacter(className, x, annotation, sequences, seqinfo,
926: #' classes wrapper of the \code{aggregate} function exist as well.
1126: setClass("RNAModifier",
1132: setClass("DNAModifier",
27: #' subclasses \code{RNAModifier} and \code{DNAModifier} are already available
206: setClassUnion("list_OR_character",
208: #' @importClassesFrom Rsamtools BamFileList PileupFiles
209: setClassUnion("list_OR_BamFileList",

scTHI:R/TME_classification.R: [ ]
132: Class <- apply(NES, 2, function(x) {
146: ClassLegend <- phenotype$Color
150: Classification <- list(Class, ClassLegend)
39: TME_classification <- function(expMat,
9: #' @param expMat Gene expression matrix where rows are genes
32: #' Class <- TME_classification(scExample)
33: #' @return A list with two items: Class (character) and ClassLegend
137: Class <- apply(NES, 2, function(x) {
141: Class[colSums(NES != 0) < nNES] <- "nc"
142: phenotype <- signaturesColors[Class, ]
143: rownames(phenotype) <- names(Class)
144: Class <- phenotype$ALLPhenotypeFinal
145: names(Class) <- rownames(phenotype)
149: #print(sort(table(Class), decreasing = TRUE))
151: names(Classification) <- c("Class", "ClassLegend")
1: #' TME_classification
3: #' The function allows the user to classify non-tumor cells in tumor
26: #' @param nNES Default is 0.58, so each cell is classified with
37: #' TME_classification
147: names(ClassLegend) <- phenotype$ALLPhenotypeFinal
148: ClassLegend <- ClassLegend[!duplicated(ClassLegend)]
152: return(Classification)

GenomicRanges:R/GPos-class.R: [ ]
189: Class <- sub("IPos$", "GPos", as.character(class(pos)))
395: .COL2CLASS <- c(
400: classinfo <- makeClassinfoRowForCompactPrinting(x, .COL2CLASS)
368: .from_GPos_to_naked_character_matrix_for_display <- function(x)
35: "Starting with BioC 3.10, the class attribute of all ",
43: if (class(x) == "GPos")
64: ### IRanges/R/IPos-class.R for what that means), they are also on the same
188: ## class name returned by class(pos).
191: new_GRanges(Class, seqnames=seqnames, ranges=pos, strand=strand,
220: class(from) <- "UnstitchedGPos" # temporarily broken instance!
228: class(from) <- "StitchedGPos" # temporarily broken instance!
252: ### FROM THE TARGET CLASS! (This is a serious flaw in as() current
264: class(from) <- "GRanges" # temporarily broken instance!
277: ### CTSS class in the CAGEr package) need to define a coercion method to
315: if (class(object) != "GPos")
330: message("[updateObject] ", class(object), " object ",
341: if (class(object) == "GPos") {
343: message("[updateObject] Settting class attribute of GPos ",
345: class(object) <- class(new("StitchedGPos"))
351: class(object), " object is current.\n",
376: .from_GPos_to_naked_character_matrix_for_display
384: stop(c(wmsg("This ", class(x), " object uses internal representation ",
7: setClass("GPos",
15: setClass("UnstitchedGPos",
22: setClass("StitchedGPos",
380: print.classinfo=FALSE, print.seqinfo=FALSE)
394: if (print.classinfo) {
402: stopifnot(identical(colnames(classinfo), colnames(out)))
403: out <- rbind(classinfo, out)
419: show_GPos(object, print.classinfo=TRUE, print.seqinfo=TRUE)
375: setMethod("makeNakedCharacterMatrixForDisplay", "GPos",
391: ## makePrettyMatrixForCompactPrinting() assumes that head() and tail()
393: out <- makePrettyMatrixForCompactPrinting(x)

QUBIC:src/matrix.h: [ ]
7: template<typename T> class Matrix {
12: Matrix(std::size_t reserved_count) {
2: #define MATRIX_H
1: #ifndef MATRIX_H

scde:R/functions.R: [ ]
6015: matrix <- gcl$vmap[rev(gcl$row.order), results$hvc$order, drop = FALSE]
6083: matrix <- results$rcm[rev(results$tvc$order), results$hvc$order]
6324: matrix <- results$rcm[rev(results$tvc$order), results$hvc$order]
1079: winsorize.matrix <- function(mat, trim) {
3405: calculate.joint.posterior.matrix <- function(lmatl, n.samples = 100, bootstrap = TRUE, n.cores = 15) {
3422: calculate.batch.joint.posterior.matrix <- function(lmatll, composition, n.samples = 100, n.cores = 15) {
3810: get.exp.posterior.matrix <- function(m1, counts, marginals, grid.weight = rep(1, nrow(marginals)), rescale = TRUE, n.cores =...(17 bytes skipped)...
3826: get.exp.logposterior.matrix <- function(m1, counts, marginals, grid.weight = rep(1, nrow(marginals)), rescale = TRUE, n.cores =...(6 bytes skipped)...
109: ##' Filter counts matrix
111: ##' Filter counts matrix based on gene and cell requirements
113: ##' @param counts read count matrix. The rows correspond to genes, columns correspond to individual cells
118: ##' @return a filtered read count matrix
145: ##' @param counts read count matrix. The rows correspond to genes (should be named), columns correspond to individual cells. The matrix should contain integer counts
163: ##' @return a model matrix, with rows corresponding to different cells, and columns representing different parameters of the d...(16 bytes skipped)...
184: ...(114 bytes skipped)...thod is designed to work on read counts - do not pass normalized read counts (e.g. FPKM values). If matrix contains read counts, but they are stored as numeric values, use counts<-apply(counts,2,function(x)...(49 bytes skipped)...
208: ##' @param counts count matrix
228: fpkm <- log10(exp(as.matrix(fpkm))+1)
229: wts <- as.numeric(as.matrix(1-fail[, colnames(fpkm)]))
262: ##' @param counts read count matrix
264: ...(41 bytes skipped)...e two groups of cells being compared. The factor entries should correspond to the rows of the model matrix. The factor should have two levels. NAs are allowed (cells will be omitted from comparison).
265: ##' @param batch a factor (corresponding to rows of the model matrix) specifying batch assignment of each cell, to perform batch correction
284: ##' \code{difference.posterior} returns a matrix of estimated expression difference posteriors (rows - genes, columns correspond to different magnit...(64 bytes skipped)...
305: stop("ERROR: provided count data does not cover all of the cells specified in the model matrix")
309: counts <- as.matrix(counts[, ci])
416: ##' @param models model matrix
417: ##' @param counts count matrix
513: ##' @param counts read count matrix
516: ##' @param batch a factor describing which batch group each cell (i.e. each row of \code{models} matrix) belongs to
523: ##' @return \subsection{default}{ a posterior probability matrix, with rows corresponding to genes, and columns to expression levels (as defined by \code{prior$x})
525: ...(24 bytes skipped)...ndividual.posterior.modes}{ a list is returned, with the \code{$jp} slot giving the joint posterior matrix, as described above. The \code{$modes} slot gives a matrix of individual expression posterior mode values on log scale (rows - genes, columns -cells)}
526: ...(85 bytes skipped)...st} slot giving a list of individual posterior matrices, in a form analogous to the joint posterior matrix, but reported on log scale }
538: ...(43 bytes skipped)...counts))) { stop("ERROR: provided count data does not cover all of the cells specified in the model matrix") }
545: counts <- as.matrix(counts[, ci, drop = FALSE])
571: # prepare matrix models
574: mm <- matrix(NA, nrow(models), length(mn))
575: mm[, which(!is.na(mc))] <- as.matrix(models[, mc[!is.na(mc)], drop = FALSE])
645: # models - entire model matrix, or a subset of cells (i.e. select rows) of the model matrix for which the estimates should be obtained
647: # return - a matrix of log(FPM) estimates with genes as rows and cells as columns (in the model matrix order).
653: ##' @param counts count matrix
655: ##' @return a matrix of expression magnitudes on a log scale (rows - genes, columns - cells)
666: ...(43 bytes skipped)...counts))) { stop("ERROR: provided count data does not cover all of the cells specified in the model matrix") }
672: # magnitudes can either be a per-cell matrix or a single vector of values which will be evaluated for each cell
676: ##' Returns estimated drop-out probability for each cell (row of \code{models} matrix), given either an expression magnitude
678: ##' @param magnitudes a vector (\code{length(counts) == nrows(models)}) or a matrix (columns correspond to cells) of expression magnitudes, given on a log scale
679: ##' @param counts a vector (\code{length(counts) == nrows(models)}) or a matrix (columns correspond to cells) of read counts from which the expression magnitude should be estimate...(1 bytes skipped)...
681: ##' @return a vector or a matrix of drop-out probabilities
704: if(is.matrix(magnitudes)) { # a different vector for every cell
705: ...(55 bytes skipped)...es))) { stop("ERROR: provided magnitude data does not cover all of the cells specified in the model matrix") }
730: ##' @param counts read count matrix (must contain the row corresponding to the specified gene)
732: ##' @param groups a two-level factor specifying between which cells (rows of the models matrix) the comparison should be made
733: ##' @param batch optional multi-level factor assigning the cells (rows of the model matrix) to different batches that should be controlled for (e.g. two or more biological replicates). The e...(224 bytes skipped)...
759: counts <- as.matrix(counts[gene, ci, drop = FALSE])
819: layout(matrix(c(1:3), 3, 1, byrow = TRUE), heights = c(2, 1, 2), widths = c(1), FALSE)
926: ##' @param counts count matrix
927: ...(8 bytes skipped)...am reference a vector of expression magnitudes (read counts) corresponding to the rows of the count matrix
936: ##' @return matrix of scde models
977: if(class(ml[[i]]) == "try-error") {
988: #l <- layout(matrix(seq(1, 4*length(ids)), nrow = length(ids), byrow = TRUE), rep(c(1, 1, 1, 0.5), length(ids)), rep(1,...(23 bytes skipped)...
989: l <- layout(matrix(seq(1, 4), nrow = 1, byrow = TRUE), rep(c(1, 1, 1, 0.5), 1), rep(1, 4), FALSE)
1000: # make a joint model matrix
1011: ##' Determine principal components of a matrix using per-observation/per-variable weights
1015: ##' @param mat matrix of variables (columns) and observations (rows)
1026: ##' @return a list containing eigenvector matrix ($rotation), projections ($scores), variance (weighted) explained by each component ($var), total (...(45 bytes skipped)...
1030: ##' mat <- matrix( c(rnorm(510,mean=0,sd=1), rnorm(510,mean=5,sd=1)), 10, 10) # random matrix
1032: ##' matw <- matrix( c(rnorm(510,mean=0,sd=1), rnorm(510,mean=5,sd=1)), 10, 10) # random weight matrix
1040: stop("bwpca: weight matrix contains NaN values")
1043: stop("bwpca: value matrix contains NaN values")
1046: matw <- matrix(1, nrow(mat), ncol(mat))
1061: ##' Winsorize matrix
1065: ##' @param mat matrix
1068: ##' @return Winsorized matrix
1072: ##' mat <- matrix( c(rnorm(510,mean=0,sd=1), rnorm(510,mean=5,sd=1)), 10, 10) # random matrix
1075: ##' win.mat <- winsorize.matrix(mat, 0.1)
1100: ##' @param counts count matrix (integer matrix, rows- genes, columns- cells)
1133: ...(114 bytes skipped)...thod is designed to work on read counts - do not pass normalized read counts (e.g. FPKM values). If matrix contains read counts, but they are stored as numeric values, use counts<-apply(counts,2,function(x)...(49 bytes skipped)...
1162: #celld <- WGCNA::cor(log10(matrix(as.numeric(as.matrix(ca)), nrow = nrow(ca), ncol = ncol(ca))+1), method = cor.method, use = "p", nThreads = n.cores)
1164: celld <- WGCNA::cor(sqrt(matrix(as.numeric(as.matrix(ca[, ids])), nrow = nrow(ca), ncol = length(ids))), method = cor.method, use = "p", nThreads = n.co...(4 bytes skipped)...
1166: celld <- stats::cor(sqrt(matrix(as.numeric(as.matrix(ca[, ids])), nrow = nrow(ca), ncol = length(ids))), method = cor.method, use = "p")
1174: # TODO: correct for batch effect in cell-cell similarity matrix
1176: # number batches 10^(seq(0, n)) compute matrix of id sums, NA the diagonal,
1178: bm <- matrix(bid, byrow = TRUE, nrow = length(bid), ncol = length(bid))+bid
1181: # use tapply to calculate means shifts per combination reconstruct shift vector, matrix, subtract
1221: if(class(ml[[i]]) == "try-error") {
1239: l <- layout(matrix(seq(1, 4), nrow = 1, byrow = TRUE), rep(c(1, 1, 1, ifelse(local.theta.fit, 1, 0.5)), 1), rep(1, 4),...(7 bytes skipped)...
1263: # make a joint model matrix
1278: ##' @param models model matrix (select a subset of rows to normalize variance within a subset of cells)
1279: ##' @param counts read count matrix
1288: ##' @param weight.k k value to use in the final weight matrix
1294: ##' @param gene.length optional vector of gene lengths (corresponding to the rows of counts matrix)
1307: ##' \item{matw} { weight matrix corresponding to the expression matrix}
1334: stop(paste("supplied count matrix (cd) is missing data for the following cells:[", paste(rownames(models)[!rownames(models) %in% coln...(44 bytes skipped)...
1343: if(verbose) { cat("Winsorizing count matrix ... ") }
1345: #tfpm <- log(winsorize.matrix(exp(fpm), trim = trim))
1346: tfpm <- winsorize.matrix(fpm, trim)
1430: if(verbose) { cat("calculating weight matrix ... ") }
1450: # calculate batch-specific version of the weight matrix if needed
1695: # use milder weight matrix
1787: ##' (weighted) projection of the expression matrix onto a specified aspect (some pattern
1794: ##' @param center whether the matrix should be re-centered following pattern subtraction
1796: ##' @return a modified varinfo object with adjusted expression matrix (varinfo$mat)
1850: ##' @param center whether the expression matrix should be recentered
1989: ##' Determine de-novo gene clusters, their weighted PCA lambda1 values, and random matrix expectation.
1995: ##' @param n.samples number of randomly generated matrix samples to test the background distribution of lambda1 on
2004: ...(10 bytes skipped)... secondary.correlation whether clustering should be performed on the correlation of the correlation matrix instead
2014: ##' \item{varm} {standardized lambda1 values for each randomly generated matrix cluster}
2036: mat <- winsorize.matrix(mat, trim = trim)
2060: gd <- as.dist(1-WGCNA::cor(as.matrix(gd), method = "p", nThreads = n.cores))
2062: gd <- as.dist(1-cor(as.matrix(gd), method = "p"))
2103: # generate random normal matrix
2105: m <- matrix(rnorm(nrow(mat)*n.cells), nrow = nrow(mat), ncol = n.cells)
2113: m <- winsorize.matrix(m, trim = trim)
2124: gd <- as.dist(1-WGCNA::cor(as.matrix(gd), method = "p", nThreads = 1))
2126: gd <- as.dist(1-cor(as.matrix(gd), method = "p"))
2230: ##' \item{xv} {a matrix of normalized aspect patterns (rows- significant aspects, columns- cells}
2231: ##' \item{xvw} { corresponding weight matrix }
2505: ##' @param distance distance matrix
2569: if(trim > 0) { xvl$d <- winsorize.matrix(xvl$d, trim) } # trim prior to determining the top sets
2593: ...(35 bytes skipped)...f whether to return just the hclust result or a list containing the hclust result plus the distance matrix and gene values
2690: ##' @param mat Numeric matrix
2694: ##' @param row.cols Matrix of row colors.
2695: ##' @param col.cols Matrix of column colors. Useful for visualizing cell annotations such as batch labels.
2708: if(class(row.clustering) == "hclust") { row.clustering <- as.dendrogram(row.clustering) }
2709: if(class(cell.clustering) == "hclust") { cell.clustering <- as.dendrogram(cell.clustering) }
2730: ##' @param col.cols Matrix of column colors. Useful for visualizing cell annotations such as batch labels. Default NULL.
2840: layout(matrix(c(1:3), 3, 1, byrow = TRUE), heights = c(2, 1, 2), widths = c(1), FALSE)
2953: m1@concomitant@x <- matrix()
2955: mod@x <- matrix()
2956: mod@y <- matrix()
3083: # vil - optional binary matrix (corresponding to counts) with 0s marking likely drop-out observations
3118: f <- calcNormFactors(as.matrix(counts[gis, !is.na(groups)]), ...)
3127: fpkm <- log10(exp(as.matrix(fpkm))+1)
3128: wts <- as.numeric(as.matrix(1-fail[, colnames(fpkm)]))
3218: # pair cell name matrix
3267: m1@concomitant@x <- matrix()
3269: mod@x <- matrix()
3270: mod@y <- matrix()
3309: if(class(ml[[i]]) == "try-error") {
3327: #l <- layout(matrix(seq(1, 4*length(ids)), nrow = length(ids), byrow = TRUE), rep(c(1, 1, 1, 0.5), length(ids)), rep(1,...(23 bytes skipped)...
3328: l <- layout(matrix(seq(1, 4), nrow = 1, byrow = TRUE), rep(c(1, 1, 1, ifelse(linear.fit, 1, 0.5)), 1), rep(1, 4), FALS...(2 bytes skipped)...
3344: # make a joint model matrix
3367: if(class(m1@model[[2]]) == "FLXMRnb2gthC") { # linear fit model
3386: df <- get.exp.logposterior.matrix(group.ifm[[nam]], dat[, nam], marginals, n.cores = inner.cores, grid.weight = prior$grid.weight)
3403: # calculate joint posterior matrix for a given group of experiments
3420: # lmatll - list of posterior matrix lists (as obtained from calculate.posterior.matrices)
3467: # calculate a joint posterior matrix with bootstrap
3502: if(class(m1@components[[2]][[2]]) == "FLXcomponentE") {
3589: #matrix(cbind(ifelse(rdf$count<= zero.count.threshold, 0.95, 0.05), ifelse(rdf$count > zero.count.threshold...(15 bytes skipped)...
3604: l <- layout(matrix(c(1:4), 1, 4, byrow = TRUE), c(1, 1, 1, 0.5), rep(1, 4), FALSE)
3654: fdf <- data.frame(y = rowMeans(matrix(log10(rdf$fpm[1:(n.zero.windowsbw)]+1), ncol = bw, byrow = TRUE)), zf = rowMeans(matrix(as.integer(rdf$cluster[1:(n.zero.windowsbw)] == 1), ncol = bw, byrow = TRUE)))
3663: cm0 <- exp(model.matrix(mt, data = mf) %*% m1@concomitant@coef)
3716: # df: count matrix
3717: # xr: expression level for each row in the matrix
3740: # counts - observed count matrix corresponding to the models
3780: cm0 <- exp(model.matrix(mt, data = mf) %*% m1@concomitant@coef)
3797: cm0 <- model.matrix(mt, data = mf) %*% m1@concomitant@coef
3805: # returns a matrix of posterior values, with rows corresponding to genes, and
3812: #message(paste("get.exp.posterior.matrix() :", round((1-length(uc)/length(counts))*100, 3), "% savings"))
3828: #message(paste("get.exp.logposterior.matrix() :", round((1-length(uc)/length(counts))*100, 3), "% savings"))
3841: # similar to get.exp.posterior.matrix(), but returns inverse ecdf list
3848: # similar to get.exp.posterior.matrix(), but returns inverse ecdf list
3871: cm0 <- exp(model.matrix(mt, data = mf) %*% m1@concomitant@coef)
4138: m <- matrix(sapply(components, function(x) x@logLik(model@x, model@y)), nrow = nrow(model@y))
4141: m <- matrix(do.call(cbind, lapply(seq_along(components), function(i) {
4175: m <- matrix(sapply(components, function(x) x@logLik(model@x, model@y)), nrow = nrow(model@y))
4178: m <- matrix(do.call(cbind, lapply(seq_along(components), function(i) {
4223: m <- matrix(sapply(components, function(x) x@logLik(model@x, model@y)), nrow = nrow(model@y))
4226: m <- matrix(do.call(cbind, lapply(seq_along(components), function(i) {
4363: class = "family")
4403: class(fit) <- c("glm", "lm")
4467: class(fit) <- c("negbin.th", "glm", "lm")
4488: x <- as.matrix(x)
4490: ynames <- if (is.matrix(y))
4611: stop(gettextf("X matrix has rank %d, but only %d observations",
4711: fit$qr <- as.matrix(fit$qr)
4718: Rmat <- as.matrix(Rmat)
4749: "qraux", "pivot", "tol")], class = "qr"), family = family,
4856: if (!is.matrix(x)) {
5008: # weight matrix should have the same dimensions as the data matrix
5154: stop("'x' must be a numeric matrix")
5259: if(is.matrix(ColSideColors)) {
5261: stop("'ColSideColors' matrix must have the same number of columns as length ncol(x)")
5306: if(is.matrix(ColSideColors)) {
5307: image(t(matrix(1:length(ColSideColors), byrow = TRUE, nrow = nrow(ColSideColors), ncol = ncol(ColSideColors))), co...(70 bytes skipped)...
5357: # rook class for browsing differential expression results
5572: mat <- winsorize.matrix(mat, trim = trim)
5583: dd <- as.dist(1-abs(cor(t(as.matrix(d)))))
5599: vd <- as.dist(1-cor(as.matrix(d)))
5669: ##' @param colcols optional column color matrix
5678: ##' @param box whether to draw a box around the plotted matrix
5680: ##' @param return.details whether the function should return the matrix as well as full PCA info instead of just PC1 vector
5726: mat <- winsorize.matrix(mat, trim = trim)
5766: mat <- winsorize.matrix(mat, trim = trim)
5791: dd <- as.dist(1-abs(cor(t(as.matrix(d)))))
5807: vd <- as.dist(1-cor(as.matrix(d)))
5965: ##' A Reference Class to represent the PAGODA application
5967: ##' This ROOK application class enables communication with the client-side ExtJS framework and Inchlib HTML5 canvas libraries to cr...(44 bytes skipped)...
5973: ##' @field mat Matrix of posterior mode count estimates
5974: ##' @field matw Matrix of weights associated with each estimate in \code{mat}
6016: matrix <- list(data = as.numeric(t(matrix)),
6017: dim = dim(matrix),
6018: rows = rownames(matrix),
6019: cols = colnames(matrix),
6024: ol <- list(matrix = matrix)
6026: rcmvar <- matrix(gcl$rotation[rev(gcl$row.order), , drop = FALSE], ncol = 1)
6033: colcols <- matrix(gcl$oc[results$hvc$order], nrow = 1)
6084: matrix <- list(data = as.numeric(t(matrix)),
6085: dim = dim(matrix),
6086: rows = rownames(matrix),
6087: cols = colnames(matrix),
6090: range = range(matrix)
6095: rcmvar <- matrix(apply(results$rcm[rev(results$tvc$order), , drop = FALSE], 1, var), ncol = 1)
6105: ol <- list(matrix = matrix, rowcols = rowcols, colcols = colcols, coldend = treeg, trim = trim)
6159: patc <- .Call("matCorr", as.matrix(t(mat)), as.matrix(pat, ncol = 1) , PACKAGE = "scde")
6325: body <- paste(capture.output(write.table(round(matrix, 1), sep = "\t")), collapse = "\n")
1081: wm <- .Call("winsorizeMatrix", mat, trim, PACKAGE = "scde")
1755: ## # construct mat multiplier submatrix
3723: x <- FLXgetModelmatrix(m1@model[[1]], edf, m1@model[[1]]@formula)
3724: #cx <- FLXgetModelmatrix(m1@concomitant, edf, m1@concomitant@formula)
3889: setClass("FLXMRnb2glm", contains = "FLXMRglm", package = "flexmix")
3951: setClass("FLXMRnb2glmC", representation(vci = "ANY"), contains = "FLXMRnb2glm", package = "flexmix")
3962: setClass("FLXMRnb2gam", contains = "FLXMRglm", package = "flexmix")
3964: setClass("FLXcomponentE",
3970: setClass("FLXMRnb2gth", contains = "FLXMRglm", package = "flexmix")
4125: setClass("FLXMRnb2gthC", representation(vci = "ANY"), contains = "FLXMRnb2gth", package = "flexmix")
4210: setClass("FLXMRglmC", representation(vci = "ANY"), contains = "FLXMRglm", package = "flexmix")
4261: setClass("FLXMRglmCf", representation(mu = "numeric"), contains = "FLXMRglmC", package = "flexmix")
4286: setClass("FLXPmultinomW", contains = "FLXPmultinom")
4862: if(!is.numeric(unclass(x[[i]]))) {
5359: ViewDiff <- setRefClass(
5981: ViewPagodaApp <- setRefClass(

messina:R/plot-methods.R: [ ]
172: Sample = Value = Class = NULL # To shut up an R CMD check note for the later use of these in ggplot
170: messinaClassPlot = function(object, indices = c(1), sort_features = TRUE, plot_type = "bar")
41: #' @seealso \code{\link{MessinaClassResult-class}}
136: #' @seealso \code{\link{MessinaSurvResult-class}}
211: data = data.frame(Sample = samples, Value = x, Class = ordered(y*1))
213: theplot = ggplot(data, aes(x = reorder(Sample, Value), y = Value, fill = Class, colour = Class)) +
467: ests_at_time = matrix(ests_at_time, nrow = 1)
10: #' Plot the results of a Messina analysis on a classification / differential expression problem.
27: #' the second best classifier margin will be plotted.}
59: #' ## Run Messina to rank probesets on their classification ability, with
60: #' ## classifiers needing to meet a minimum sensitivity of 0.95, and minimum
112: #' the second best classifier margin will be plotted.}
215: ggtitle(sprintf("MessinaClass Fit: Feature %s", feature)) +
37: #' @aliases plot,MessinaClassResult-method
38: #' @aliases plot,MessinaClassResult,missing-method
69: setMethod("plot", signature = signature(x = "MessinaClassResult", y = "missing"), definition = function(x, y, ...) messinaClassPlot(object = x, ...))

discordant:R/discordant.R: [ ]
114: class <- cbind(.assignClass(v1, param1, components),
236: classMatrix <- discordPPMatrix
128: sub.class <- cbind(.assignClass(subSamples$v1, param1, components),
163: classVector <- finalResult$class
227: discordClass <- c(2,3,4,6,7,8)
235: discordPPMatrix <- matrix(NA, nrow = featureSize, ncol = featureSize)
269: .assignClass <- function(x, param, components) {
34: #' \item{discordPPMatrix}{Matrix of differentially correlated posterior
38: #' \item{classMatrix}{Matrix of classes that have hte highest posterior
40: #' \item{probMatrix}{Matrix of posterior probabilities where rows are each
54: #' The posterior probabilities for each class are generated and outputted into
55: #' the value probMatrix. The value probMatrix is a matrix where each column is a
56: #' class and each row is a feature pair. The values discordPPVector and
58: #' probability for each feature pair. The values classVector and classMatrix
59: #' are the class with the highest posterior probability for each feature pair.
131: pd <- tryCatch({em.normal.partial.concordant(sub.pdata, sub.class,
160: finalResult <- .subSampleData(pdata, class, mu, sigma, nu, tau, pi,
165: pd <- tryCatch({em.normal.partial.concordant(pdata, class, components)},
172: classVector <- pd$class
178: em.normal.partial.concordant <- function(data, class, components) {
184: g <- as.integer(nlevels(as.factor(class)))
190: zx <- .unmap(class[,1], components = components)
191: zy <- .unmap(class[,2], components = components)
219: class = apply(array(results[[3]], dim = c(n,g*g)),
239: classMatrix[diag] <- classVector
241: colnames(classMatrix) <- rownames(x)
244: discordPPMatrix <- matrix(discordPPV, nrow = featureSize,
246: classMatrix <- matrix(classVector, nrow = featureSize, byrow = FALSE)
248: colnames(classMatrix) <- rownames(y)
253: rownames(classMatrix) <- rownames(x)
261: classMatrix = classMatrix, classVector = classVector,
266: # Internal function to assign class to vector based on number of components and
294: mat1 <- matrix(v1, nrow = nrow(x), byrow = FALSE)
295: mat2 <- matrix(v2, nrow = nrow(x), byrow = FALSE)
36: #' \item{classVector}{Vector of classes that have the highest posterior
41: #' molecular feature pair and columns are nine different classes}
48: #' are five components, then there are two more classes for very negative
50: #' combinations for these components are made into classes. If there are three
51: #' components, there are 9 classes. If there are five components, there are 25
52: #' classes.
57: #' discordPPMatrix are the summed differential correlation posterior
115: .assignClass(v2, param2, components))
129: .assignClass(subSamples$v2, param2, components))
175: rtn <- .prepareOutput(x, y, pd, zTable, classVector, components)
224: .prepareOutput <- function(x, y, pd, zTable, classVector, components) {
229: discordClass <- setdiff(1:25, c(1, 7, 13, 19, 25))
232: discordPPV <- apply(zTable, 1, function(x) sum(x[discordClass]) / sum(x))
237: diag <- lower.tri(discordPPMatrix, diag = FALSE)
238: discordPPMatrix[diag] <- discordPPV
240: colnames(discordPPMatrix) <- rownames(x)
247: colnames(discordPPMatrix) <- rownames(y)
252: rownames(discordPPMatrix) <- rownames(x)
255: names(classVector) <- vector_names
260: return(list(discordPPMatrix = discordPPMatrix, discordPPVector = discordPPV,
262: probMatrix = zTable, loglik = pd$loglik))

spatzie:R/find_ep_coenrichment.R: [ ]
255: matrixClass = jaspar_matrix_class)
247: jaspar_matrix_class <- "PFM"
40: #' matrix file containing multiple motifs to scan for, gz-zipped files allowed
41: #' @param motifs_file_matrix_format type of position-specific scoring matrices
44: #' \code{pfm}: \tab position frequency matrix, elements are absolute
46: #' \code{ppm}: \tab position probability matrix, elements are probabilities,
48: #' \code{pwm}: \tab position weight matrix, elements are log likelihoods
99: #' motifs_file_matrix_format = "pfm",
125: motifs_file_matrix_format = c("pfm", "ppm",
133: motifs_file_matrix_format <- match.arg(motifs_file_matrix_format,
158: stop("'int_raw_data' data type unsupported: ", class(int_raw_data))
221: promoter_left <- S4Vectors::elementMetadata(anchor1)[, "node.class"] == "promoter"
222: promoter_right <- S4Vectors::elementMetadata(anchor2)[, "node.class"] == "promoter"
246: if (motifs_file_matrix_format == "pfm") {
248: } else if (motifs_file_matrix_format == "ppm") {
249: jaspar_matrix_class <- "PWMProb"
250: } else if (motifs_file_matrix_format == "pwm") {
251: jaspar_matrix_class <- "PWM"
118: #' @importFrom TFBSTools readJASPARMatrix
254: motifs <- TFBSTools::readJASPARMatrix(motifs_file,

mdp:R/mdp.R: [ ]
747: for(class in unique(sort(x_temp[, colClass]))) {
579: class_means <- vector()
743: compute_zscore_classes <- function(x, colScore = 2, colClass = 3) {
9: #' @param pdata \code{data frame} of phenodata with a column headed Class and the
11: #' @param control_lab character \code{vector} specifying the control class
31: #' matrix is taken and values less than the std are set to zero.
33: #' \item Gene scores - mean z-score value for each gene in each class
35: #' value in each class
92: if (!("Sample" %in% names(pdata) && "Class" %in% names(pdata))) {
93: stop("Please label phenodata columns as 'Sample' and 'Class'")
96: } else if (sum(control_lab %in% pdata$Class) == 0) {
97: stop("Please provide a control label that matches a class in the phenodata")
98: } else if (sum(pdata$Class %in% control_lab) < 2) {
138: control_samples <- as.character(pdata$Sample[pdata$Class == control_lab])
139: test_samples <- as.character(pdata$Sample[pdata$Class != control_lab])
146: # find gene scores and gene frequency for each class
268: #' control_samples <- example_pheno$Sample[example_pheno$Class == 'baseline']
317: #' control_samples <- example_pheno$Sample[example_pheno$Class == 'baseline']
354: #' Computes gene scores for each gene within each class and perturbation freq
356: #' @param pdata phenotypic data with Class and Sample columns
357: #' @param control_lab character specifying control class
365: all_groups <- unique(pdata$Class) # find all groups
373: # find average expression of gene in each class
374: gene_average <- rowMeans(zscore[, as.character(pdata$Sample[pdata$Class == group])])
378: # find frequency that gene is perturbed in each class
379: gene_average <- rowMeans(zscore[, as.character(pdata$Sample[pdata$Class == group])] > 0)
397: #' @param control_lab label specificying control class
433: #' @param pdata phenotypic data with Sample and Class columns
452: Class = pdata[names(sample_scores), "Class"])
463: #' Data frame must have Score, Sample and Class columns
466: #' Must have columns 'Sample', 'Score' and 'Class'
473: #' class as light blue as a default
484: !("Class" %in% names(sample_data)) \|
486: stop("Sample data must be data frame with colnames 'Class' 'Sample' 'Score'")
512: # make color for each class, with control class as light blue
513: groups <- unique(sample_data$Class)
522: if (!(control_lab %in% sample_data$Class)) {
544: fill = "Class")) +
580: for (j in unique(sample_data$Class)) {
581: class_means = c(class_means,
582: mean(sample_data[sample_data$Class == j,
585: names(class_means) <- unique(sample_data$Class)
586: class_means <- class_means[order(class_means)]
592: x = "Class",
593: fill = "Class")) +
600: x = "Class",
603: ggplot2::scale_x_discrete(limits = names(class_means)) +
647: # A matrix specifying the layout.
649: # something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE), then
667: layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
684: # Get the i,j matrix positions of the regions
702: #' @param control_lab label that specifies control class
742: #' Computes the Z score of each class
748: mdp_score_temp <- x_temp[x_temp[, colClass] == class,]
749: mdp_score_temp$zscore_class <- scale(mdp_score_temp[, colScore])
758: #' Calculate the zscore inside specific class to identify pontential outliers
764: stop("Control variable: ", control, " didn't identified like as class!")
766: x_temp$outlier <- ifelse(x_temp[, colClass] == control & x_temp$zscore_class > threshold, 1, ifelse(x_temp[, colClass] != control & x_temp$zscore_class < -threshold, 1, 0))
40: #' higher gene scores in the test classes compared to the control.
170: sample_results <- check_outlier_samples(x = compute_zscore_classes(x = sample_results), control = control_lab)
760: check_outlier_samples <- function(x, colScore = 2, colClass = 3, control = "healthy", threshold = 2) {
763: if(!control %in% unique(sort(x_temp[, colClass]))) {

psichomics:R/analysis.R: [ ]
888: warn <- tags$div(class="alert alert-warning", role="alert",
969: error <- tagList(h4("t-test"), tags$div(class="alert alert-danger",
116: #' @param data One-row data frame/matrix or vector: values per sample for a
118: #' @param match Matrix: match between samples and subjects
119: #' @param clinical Data frame or matrix: clinical dataset (only required if the
254: class(survTime) <- c("data.frame", "survTime")
407: class(res) <- c("survTerms", class(res))
556: if ("simpleError" %in% class(survTerms)) {
686: if ("simpleError" %in% class(survTerms)) return(NA)
831: #' @param stat Data frame or matrix: values of the analyses to be performed (if
975: warn <- tags$div(class="alert alert-warning", role="alert",
1220: type <- sapply(cols, function(i) class(df[[i]]))
1861: #' @param data Numeric, data frame or matrix: gene expression data or
2005: #' @param data Data frame or matrix
2026: # Remove matrix rownames from melted data
2064: #' @return A list with class \code{"htest"} containing the following components:
2103: class(rval) <- "htest"
2373: if (!is.matrix(num)) {
2374: num <- t(as.matrix(num))
2406: #' @param data Data frame or matrix: gene expression or alternative splicing
2527: if (!is.matrix(adjust)) adjust <- t(as.matrix(adjust))
2710: type <- sapply(cols, function(i) class(stats[[i]]))
2841: #' @param psi Data frame or matrix: alternative splicing quantification
3178: div(class="col-sm-6 col-md-4",
3179: div(class="thumbnail", style="background:#eee;",
3180: div(class="caption", uiOutput(ns(id)))))
3193: class="btn-info btn-md btn-block", class="visible-lg visible-md"),
3196: class="btn-info btn-xs btn-block", class="visible-sm visible-xs"))
3214: actionButton(ns("analyse"), "Perform analyses", class="btn-primary"),
3224: div(class="row", card("ttest"), card("levene")),
3226: div(class="row", card("wilcox"), card("kruskal"), card("fligner")))
3307: event, class=NULL, showPath=FALSE, showText=FALSE,
3315: event, class=NULL, showPath=FALSE, showText=FALSE,

Pi:R/xMLcaret.r: [ ]
88: class <- as.factor(gs_targets[!is.na(ind)])
87: df_predictor_class <- as.data.frame(df_predictor[ind[!is.na(ind)],])
102: ...(4 bytes skipped)...Control <- caret::trainControl(method=c("repeatedcv","cv","oob")[1], number=nfold, repeats=nrepeat, classProbs=TRUE, summaryFunction=caret::twoClassSummary, allowParallel=FALSE)
830: vec_ap <- dnet::dPvalAggregate(pmatrix=df_pval, method=aggregateBy)
1: #' Function to integrate predictor matrix in a supervised manner via machine learning algorithms using caret.
3: #' \code{xMLcaret} is supposed to integrate predictor matrix...(165 bytes skipped)...s: 1) Gold Standard Positive (GSP) targets; 2) Gold Standard Negative (GSN) targets; 3) a predictor matrix...(17 bytes skipped)... in rows and predictors in columns, with their predictive scores inside it. It returns an object of class 'sTarget'.
10: ...(71 bytes skipped)...idataion. Per fold creates balanced splits of the data preserving the overall distribution for each class (GSP and GSN), therefore generating balanced cross-vallidation train sets and testing sets. By defa...(44 bytes skipped)...
18: #' an object of class "sTarget", a list with following components:
20: #' \item{\code{model}: an object of class "train" as a best model}
29: #' \item{\code{evidence}: an object of the class "eTarget", a list with following components "evidence" and "metag"}
85: ## predictors + class
89: levels(class) <- c("GSN","GSP")
90: df_predictor_class$class <- class
94: ...(40 bytes skipped)...ds (%d in GSP, %d in GSN) are used for supervised integration of %d predictors/features (%s).", sum(class=="GSP"), sum(class=="GSN"), ncol(df_predictor), as.character(now)), appendLF=TRUE)
119: fit_gbm <- caret::train(class ~ .,
120: data = df_predictor_class,
158: fit_svm <- caret::train(class ~ .,
159: data = df_predictor_class,
195: fit_rda <- caret::train(class ~ .,
196: data = df_predictor_class,
231: fit_knn <- caret::train(class ~ .,
232: data = df_predictor_class,
267: fit_pls <- caret::train(class ~ .,
268: data = df_predictor_class,
305: suppressMessages(fit_nnet <- caret::train(class ~ .,
306: data = df_predictor_class,
346: fit_rf <- caret::train(class ~ .,
347: data = df_predictor_class,
384: class = c("numeric", 'numeric'),
446: fit_myrf <- caret::train(class ~ .,
447: data = df_predictor_class,
486: fit_crf <- caret::train(class ~ .,
487: data = df_predictor_class,
524: fit_glmnet <- caret::train(class ~ .,
525: data = df_predictor_class,
556: fit_glm <- caret::train(class ~ .,
557: data = df_predictor_class,
589: fit_bglm <- caret::train(class ~ .,
590: data = df_predictor_class,
627: fit_blr <- caret::train(class ~ .,
628: data = df_predictor_class,
669: fit_xgbl <- caret::train(class ~ .,
670: data = df_predictor_class,
712: fit_xgbt <- caret::train(class ~ .,
713: data = df_predictor_class,
747: message(sprintf("Extract the performance matrix of %d rows/repeatsfolds X 2 (AUC and F-max) (%s).", nfoldnrepeat, as.character(now)), appendLF=TR...(3 bytes skipped)...
792: message(sprintf("Extract the full prediction matrix of %d rows/genes X %d columns/repeatsfolds, aggregated via '%s' (%s) ...", nrow(df_predictor_class), nfoldnrepeat, aggregateBy, as.character(now)), appendLF=TRUE)
818: df_full <- as.matrix(xSparseMatrix(df_full, verbose=FALSE))
917: class(sTarget) <- "sTarget"
34: #' @seealso \code{\link{xPierMatrix}}, \code{\link{xPredictROCR}}, \code{\link{xPredictCompare}}, \code{\link{xSparseMatrix}}, \code{\link{xSymbol2GeneID}}
57: df_predictor <- xPierMatrix(list_pNode, displayBy="score", combineBy="union", aggregateBy="none", RData.location=RData.location...(12 bytes skipped)...
61: eTarget <- xPierMatrix(list_pNode, displayBy="evidence", combineBy="union", aggregateBy="none", verbose=FALSE, RData.locat...(30 bytes skipped)...
103: fitControl_withoutParameters <- caret::trainControl(method="none", classProbs=TRUE, allowParallel=FALSE)
381: type = "Classification",
394: fit = function(x, y, wts, param, lev, last, classProbs, ...) {
432: levels = function(x) x$classes,

BioNERO:R/gcn_inference.R: [ ]
450: Matrix = modtraitcor, yLabels = yLabels, xLabels = xLabels,
1039: matrix <- list_mat[[x]]
150: cor_matrix <- calculate_cor_adj(cor_method, norm.exp, SFTpower, net_type)$cor
151: adj_matrix <- calculate_cor_adj(cor_method, norm.exp, SFTpower, net_type)$adj
1005: cor_matrix <- net$correlation_matrix
428: textMatrix <- paste(signif(modtraitcor, 2), modtraitsymbol, sep = "")
112: #' \item Adjacency matrix
116: #' \item Correlation matrix
149: if(verbose) { message("Calculating adjacency matrix...") }
153: #Convert to matrix
154: gene_ids <- rownames(adj_matrix)
155: adj_matrix <- matrix(adj_matrix, nrow=nrow(adj_matrix))
156: rownames(adj_matrix) <- gene_ids
157: colnames(adj_matrix) <- gene_ids
159: #Calculate TOM from adjacency matrix
160: if(verbose) { message("Calculating topological overlap matrix (TOM)...") }
162: TOM <- WGCNA::TOMsimilarity(adj_matrix, TOMType = tomtype)
228: kwithin <- WGCNA::intramodularConnectivity(adj_matrix, new.module_colors)
231: adjacency_matrix = adj_matrix,
235: correlation_matrix = cor_matrix,
320: expr <- as.matrix(t(norm.exp))
337: # Define a matrix of labels for the original and all resampling runs
338: labels <- matrix(0, nGenes, nRuns + 1)
381: #' @param cex.text Font size for numbers inside matrix. Default: 0.6.
416: modtraitcor <- cor(as.matrix(MEs), trait, use = "p", method = cor_method)
482: #' \item{filtered_corandp}{Filtered matrix of correlation and p-values}
483: #' \item{raw_GS}{Raw matrix of gene significances}
512: GS <- cor(as.matrix(t(final_exp)), trait, use = "p")
534: as.matrix(GS), border_color = NA, color = cols,
641: fmat <- matrix(
776: # Create a data frame containing annotations and the annotation class
786: # Add column containing the annotation class
914: edges <- net$correlation_matrix
937: #' Get edge list from an adjacency matrix for a group of genes
959: #' the correlation matrix was calculated. Only required
968: #' edge lists by filtering the original correlation matrix by the thresholds
1004: # Define objects containing correlation matrix and data frame of genes and modules
1011: cor_matrix <- cor_matrix[keep, keep]
1016: cor_matrix <- cor_matrix[genes, genes]
1019: # Should we filter the matrix?
1021: # Create edge list from correlation matrix
1022: edges <- cormat_to_edgelist(cor_matrix)
1035: list_mat <- replicate(length(cutoff), cor_matrix, simplify = FALSE)
1040: matrix[matrix < cutoff[x] ] <- NA
1041: diag(matrix) <- 0
1044: degree <- rowSums(matrix, na.rm=TRUE)
1047: matrix[lower.tri(matrix, diag=TRUE)] <- NA
1049: # Convert symmetrical matrix to edge list (Gene1, Gene2, Weight)
1050: matrix <- na.omit(data.frame(as.table(matrix)))
1051: result <- list(matrix = matrix, degree = degree)
1080: stop("Please, specify the number of samples used to calculate the correlation matrix")
1101: # Create edge list from correlation matrix without filtering
1102: edgelist <- cormat_to_edgelist(cor_matrix)
429: dim(textMatrix) <- dim(modtraitcor)
433: textMatrix <- t(textMatrix)
452: colors = cols, textMatrix = textMatrix, setStdMargins = FALSE,

cTRAP:R/shinyInterface.R: [ ]
27: alert <- tags$div(class=paste0("alert alert-", type), role="alert", ...)
115: tags$div(class="container-fluid", style="padding-top: 15px;")
274: class <- class(dataset)[[1]]
1153: class <- "danger"
1584: tags$span(class="help-block", style="margin-top: -10px;",
11: .filterDatasetsByClass <- function(data, class, expected=FALSE) {
1388: corMatrix <- input$corMatrix
1404: selectedCorMatrix <- loadExpressionDrugSensitivityAssociation(
1742: elemClasses <- sapply(lapply(list(...), class), "[[", 1)
12: selected <- sapply(data, is, class)
14: # Return values that are expected to turn into the given class
15: expected <- sapply(data, is, paste0("expected", capitalize(class)))
34: class(data) <- c("diffExpr", class(data))
51: title <- div(class="panel-heading", h3(class="panel-title", title))
55: if (length(body) > 0) body <- div(class="panel-body", ...)
57: body <- div(id="collapseOne", class="panel-collapse collapse in",
60: if (!is.null(footer)) footer <- div(class="panel-footer", footer)
61: div(class=paste0("panel panel-", type), title, body, footer)
132: cols <- cols[!sapply(table, class)[cols] %in% c("character", "logical")]
162: .prepareReferenceComparisonDT <- function(data, class) {
163: data <- .filterDatasetsByClass(req(data), class, expected=TRUE)
276: tags <- paste0("#", c(class, source), collapse=" ")
287: # Update dataset choices (optionally, filter datasets by class)
288: .updateDatasetChoices <- function(session, id, data, class=NULL) {
289: if (!is.null(class)) data <- .filterDatasetsByClass(data, class)
332: actionButton(ns("load"), "Load data", class="btn-primary"))
409: actionButton(ns("load"), "Load data", class="btn-primary"))
573: actionButton(ns("load"), "Load data", class="btn-primary"),
925: class="compact hover stripe")
1158: class <- "danger"
1163: class <- "default"
1168: class <- "warning"
1173: class <- "success"
1180: class <- NULL
1183: class <- paste0(c("label label-"), class)
1185: html <- tags$span(style=colour, icon, state, ..., class=class)
1222: class(ranking) <- c(paste0("expected", capitalize(mode)), "expected",
1223: class(ranking))
1260: actionButton(ns("analyse"), "Rank by similarity", class="btn-primary"))
1370: class="btn-primary"))
1587: class="btn-primary"))
100: rapply(tag, FUN, how="replace", classes="character")
1207: selectedDiffExpr, selectedCorMatrix, method,
1355: selectizeInput(ns("corMatrix"),
1389: if (!is.null(corMatrix) \|\| corMatrix != "") {
1390: dataset <- paste(corMatrix, tolower(dataset))
1397: corMatrix <- req(input$corMatrix)
1405: corMatrix, path=path)
1409: selectedDiffExpr, selectedCorMatrix, method,
1415: selectedDiffExpr, selectedCorMatrix, method,
1424: "Gene expression and drug sensitivity association"=corMatrix,
1484: sets <- .filterDatasetsByClass(x(), "drugSets")
1510: drugSets <- .filterDatasetsByClass(x(), "drugSets")
1743: hasSimilarPerts <- "similarPerturbations" %in% elemClasses
1744: hasTargetingDrugs <- "targetingDrugs" %in% elemClasses

pRolocGUI:R/pRolocVis_compare.R: [ ]
596: ui <- tags$body(class="skin-blue right-sidebar-mini control-sidebar-open",
1037: profByClass1 <- plotFacetProfiles(profs[[1]], fcol[1],
1040: profByClass2 <- plotFacetProfiles(profs[[2]], fcol[2],
195: myclasses <- unique(unlist(lapply(pmarkers, colnames)))
18: ##' individual class profile plots should be displayed. Default is \code{FALSE}.
48: if (all(sapply(object, is.matrix))) {
61: if (is.null(myargs$method)) stop(paste("method must be set to method = 'none' if a matrix is passed"))
62: if (myargs$method != "none") stop(paste("method must be set to method = 'none' if a matrix is passed"))
71: else stop(paste("Object must be of class MSnSet or matrix"))
101: m <- matrix(0, ncol = 1, nrow = nrow(object[[i]]))
111: stop("Your fcol (markers) are neither vector nor matrix. See ?markers for details.")
150: chk[j] <- is.matrix(fData(object[[i]])[, j])
173: pmarkers <- lapply(pmarkers_msnset, fData) # marker matrix
177: ## Check pmarkers, if not a matrix convert to a matrix
179: if (!inherits(pmarkers[[i]], "matrix")) {
245: or add the class labels on the spatial map click
247: name. All class labels can be added back to the plot
352: tabPanel("Profiles (by class)", value = "profilesPanel2",
491: # tabPanel("Profiles (by class)", value = "profilesPanel2",
647: ## Get coords for proteins according to selectized marker class(es)
1133: # addClass(selector = "body", class = "sidebar-collapse")
1134: # removeClass(selector = "body", class = "control-sidebar-open")
1136: # removeClass(selector = "body", class = "sidebar-collapse")
1137: # addClass(selector = "body", class = "control-sidebar-open")
1140: # observeEvent(input$openright, {addClass(selector = "body", class = "control-sidebar-open")})
17: ##' @param classProfiles A \code{logical} indicating if a tab displaying
21: classProfiles = FALSE,
236: p(strong("Subcellular classes")),
244: belong to pre-defined subcellular classes. To remove
268: if (classProfiles) {
357: plotOutput("classProfiles1",
362: plotOutput("classProfiles2",
496: # plotOutput("classProfiles1",
501: # plotOutput("classProfiles2",
790: ## get quantiles for subcellular classes
791: ...(21 bytes skipped)...ply(indMrk, function(z) profs[[indData]][z, , drop = FALSE]) # 5% and 95% quantiles for all other classes
853: if (classProfiles) {
854: output$classProfiles1 <- renderPlot({
859: output$classProfiles2 <- renderPlot({
1043: ggsave(filename = file, plot = profByClass1, device = "pdf", width = 12, height = 5)
1044: ggsave(filename = file, plot = profByClass2, device = "pdf", width = 12, height = 5)
1056: # profByClass <- plotFacetProfiles(df = calcData[[1]], col = mycol, reps = FALSE)
1057: # ggsave(filename = file, plot = profByClass, device = "pdf", width = w, height = h)
117: origCl <- getMarkerClasses(object[[i]], fcol = fcol[i])
208: cols <- cols[1:length(myclasses)]
209: names(cols) <- myclasses
210: col_ids <- paste0("col", seq(myclasses))
211: colPicker <- function(x) {colourpicker::colourInput(col_ids[x], myclasses[x],
254: choices = myclasses,
255: selected = myclasses,
691: names(cols_user) <- myclasses
703: which(myclasses == z))]
717: which(myclasses == z))]
1089: choices = myclasses,
1090: selected = myclasses,
1108: choices = myclasses,

CHETAH:R/Utils_CHETAH.R: [ ]
877: class <- class(toplot[,1])
680: Classify <- function(input, thresh = 0.1, return_clas = FALSE) {
695: classification <- nodeDown(conf = conf, prof = prof, node = 1,
984: classification <- input$celltype_CHETAH
1143: ClassifyReference <- function(ref_cells, ref_ct = "celltypes",
1277: classification <- input$celltype_CHETAH
118: CHETAHclassifier <- function (input,
20: #' an expression matrix with one (average) reference expression profile
147: is(clust_dist(matrix(seq_len(4), nrow = 2)), "dist"),
241: input = input, # the input expression matrix
242: ref_cells = ref_cells, ## matrix of all reference cells
243: ref_types = ref_types, ## types of the ref_cells matrix
244: ref_profiles = ref_profiles) ## matrix of the average reference profiles per cell type
432: ## Add the profile scores and correlations of all types to one matrix
624: ## as.matrix: in cases were a sparse Matrix is used
626: cor_i <- suppressWarnings(cor(as.matrix(SummarizedExperiment::assay(input, Env$input_c)[genes, , drop = FALSE]),
627: as.matrix(ref_profiles[genes, type, drop = FALSE]),
633: cor_t <- suppressWarnings(cor(as.matrix(SummarizedExperiment::assay(ref_cells, Env$ref_c)[genes , current_cells, drop = FALSE]),
634: as.matrix(ref_profiles[genes, type, drop = FALSE]),
636: cor_ob <- suppressWarnings(cor(as.matrix(SummarizedExperiment::assay(ref_cells, Env$ref_c)[genes , otherbranch_c, drop = FALSE]),
637: as.matrix(ref_profiles[genes, type, drop = FALSE]),
703: # To make a profile_matrix from a list of reference matrices
880: if(class == "factor") {
888: if(class == "numeric" \| class == 'integer') {
909: # Plot boxplots grouped by a class variable
910: PlotBox <- function(toplot, class, col = NULL, grad_col = NULL,
914: class <- data.frame(class, stringsAsFactors = TRUE)
915: col <- col[levels(class[,1])]
920: data <- cbind.data.frame(toplot, class)
921: colnames(data) <- c("score", "class")
924: plot <- ggplot(data, aes_string(x = 'class', y = 'score')) +
937: plot <- plot + geom_jitter(aes_string(color = 'class'), size = 0.1)
945: plot <- plot + geom_boxplot(aes_string(color = 'class'),
1050: #' @param return return the matrix that was used to produce the plot
1081: ## Make empty correlation matrix
1082: cors <- matrix(NA, nrow = ncol(ref_profiles), ncol = ncol(ref_profiles))
1123: #' @param return return the matrix that was used to produce the plot
1154: ## Make the correlation matrix
1162: cors <- matrix(NA, nrow = lngt, ncol = lngt+1)
1195: prof_df <- as.matrix(prof[[node]]) ## approximately 400x faster than on DataFrame
1196: conf_df <- as.matrix(conf[[node]])
9: #' CHETAH classifies an input dataset by comparing it to
27: #' by \code{\link{Classify}})
60: #' @param plot.tree Plot the classification tree.
74: #' \item \strong{classification} a named vector: the classified types
76: #' \item \strong{tree} the hclust object of the classification tree
78: #' \item \strong{nodecoor} the coordinates of the nodes of the classification tree
93: #' to produce a classification tree (ct).
99: #' If this is not the case, classification for the cell will stop in the current node.
101: #' these classifications are called \strong{final types}
102: #' For other cells, assignment will stop in a node. These classifications
174: message("Running without reference cells: classification will only be based on correlations \n")
212: ## Make an environment to store the classification information and variables in
221: tree = NULL, # will be filled with the classification tree
246: ## For plotting purposes, find the x coordinates of the nodes of the classification tree
294: ## Add the (visible) classification meta-data
295: input <- Classify(input = input, thresh = thresh)
303: # of the current node of the classification tree,
312: ## (Re)construct the classification tree and cut at the highest node
653: #' (Re)classify after running \code{\link{CHETAHclassifier}} using a confidence threshold \cr
659: #' Selecting 0 will classify all cells, whereas 2 will result i
660: #' n (almost) no cells to be classified. \cr
662: #' @param return_clas Instead of returning the SingleCellExperiment, only return the classification vector
669: #' ## Classify all cells
670: #' input_mel <- Classify(input_mel, 0)
672: #' ## Classify only cells with a very high confidence
673: #' input_mel <- Classify(input_mel, 1)
676: #' input_mel <- Classify(input_mel)
678: #' ## Return only the classification vector
679: #' celltypes <- Classify(input_mel, 1, return_clas = TRUE)
697: names(classification) <- rownames(prof[[1]])
698: input$celltype_CHETAH <- classification
699: if (return_clas) return(classification) else return(input)
728: #' Plots the chetah classification tree with nodes numbered
739: #' A ggplot object of the classification tree
806: ggtitle("Classification Tree")
871: theme_classic() +
926: theme_classic() +
951: #' Plot the CHETAH classification on 2D visulization like t-SNE
952: #' + the corresponding classification tree,
958: #' @param tree plot the tree, along with the classification
959: #' @param pt.size the point-size of the classication plot
961: #' the classification plot should be returned
999: extra_nodes <- unique(classification)[!(unique(classification) %in% names(meta_data$nodetypes[[1]]))]
1025: toplot <- classification
1115: #' Use a reference dataset to classify itself.
1128: #' the columns the classifion labels.
1131: #' classified to the type of the column name.
1133: #' that is classified to an intermediate type
1135: #' A good reference would classify nearly 100% of cells of type A to type A.
1142: #' ClassifyReference(ref_cells = headneck_ref)
1146: ## Classify
1190: } ## ClassifyReference
1194: ## Do the classification of this node
1253: #' In the CHETAH classification, replace the name of a Node
1262: #' @param return_clas Instead of returning the SingleCellExperiment, only return the classification vector
1265: #' The SingleCellExperiment with the new classification or if `return_clas = TRUE` the classification vector.
1282: classification[classification == nodename] <- replacement
1295: classification[classification %in% replace] <- replacement
1296: input$celltype_CHETAH <- classification
1297: if (return_clas) return(classification) else return(input)
1304: #' Launch a web page to interactively go trough the classification
117: #' input_mel <- CHETAHclassifier(input = input_mel, ref_cells = headneck_ref)
299: } ### CHETAHclassifier
302: # Called by the CHETAHclassifier. Determines the branches
346: # Called by the CHETAHclassifier via SplitNode.
657: #' @param input a SingleCellExperiment on which \code{\link{CHETAHclassifier}} has been run
730: #' @param input a SingleCellExperiment on which \code{\link{CHETAHclassifier}} has been run
820: #' @param input a SingleCellExperiment on which \code{\link{CHETAHclassifier}} has been run
955: #' @param input a SingleCellExperiment on which \code{\link{CHETAHclassifier}} has been run
1045: #' \code{\link{CHETAHclassifier}}'s ref_cells
1047: #' \code{\link{CHETAHclassifier}}'s ref_profiles
1051: #' @param n_genes as in \code{\link{CHETAHclassifier}}
1052: #' @param fix_ngenes as in \code{\link{CHETAHclassifier}}
1053: #' @param print_steps as in \code{\link{CHETAHclassifier}}
1054: #' @param only_pos as in \code{\link{CHETAHclassifier}}
1120: #' \code{\link{CHETAHclassifier}}'s ref_cells
1125: #' \code{\link{CHETAHclassifier}}
1147: input <- CHETAHclassifier(input = ref_cells,
1256: #' @param input a SingleCellExperiment on which \code{\link{CHETAHclassifier}} has been run
1306: #' @param input a SingleCellExperiment on which \code{\link{CHETAHclassifier}} has been run
1342: if (is.null(input@int_metadata$CHETAH)) stop('Please run CHETAHclassifier on the SingleCellExperiment object before calling this funtion')

TRONCO:R/visualization.R: [ ]
2377: matrix = matrix(0, nrow = length(keys) + 3, ncol = 1)
407: pheat.matrix = data.lifting(x,data)
1223: draw_matrix <- function(matrix,
98: ## This function sorts a matrix to enhance mutual exclusivity
198: stop('"group.samples" should be matrix with sample names and group assignment.')
364: data.lifting <- function(obj, matrix) {
375: function(obj, matrix) {
377: ## Are you sure (obj %in% # matrix)
380: if (obj %in% matrix) {
385: rownames(matrix)))]
386: sub.data = matrix[keys.subset, , drop = FALSE]
394: matrix[keys.subset, ] = sub.data
404: return(list(data=matrix, colors=map.gradient))
408: map.gradient = pheat.matrix$colors
409: data = pheat.matrix$data
785: data = matrix(0, nrow = ngenes(x), ncol = ntypes(x))
882: tmp = as.matrix(subdata[which(refcol == i), ]);
1060: t = c(as.vector(as.matrix(annotation_col)), colnames(annotation_col))
1083: c(as.vector(as.matrix(annotation_row)),
1161: stop("Gaps do not match with matrix size")
1182: dist = matrix(0, nrow = 2 * n - 1, ncol = 2, dimnames = list(NULL, c("x", "y")))
1230: n = nrow(matrix)
1231: m = ncol(matrix)
1248: gp = gpar(fill = matrix, col = border_color))
1383: return(as.matrix(new))
1550: heatmap_motor <- function(matrix,
1585: nrow = nrow(matrix),
1586: ncol = ncol(matrix),
1634: ## gt = heatmap_motor(matrix, cellwidth = cellwidth,
1650: heatmap_motor(matrix,
1711: ## Draw matrix.
1713: elem = draw_matrix(matrix, border_color, gaps_row, gaps_col, fmat, fontsize_number, number_color)
1714: res = gtable_add_grob(res, elem, t = 4, l = 3, clip = "off", name = "matrix")
1810: mat = as.matrix(mat)
1811: return(matrix(scale_vec_colours(as.vector(mat),
1972: #' @param mat numeric matrix of the values to be plotted.
1994: #' of the above it is assumed that a distance matrix is provided.
2032: #' the cells. If this is a matrix (with same dimensions as original matrix), the contents
2033: #' of the matrix are shown instead of original values.
2067: #' # Create test matrix
2068: #' test = matrix(rnorm(200), 20, 10)
2144: ## Preprocess matrix.
2146: mat = as.matrix(mat)
2172: if (is.matrix(display_numbers) \| is.data.frame(display_numbers)) {
2174: stop("If display_numbers provided as matrix, its dimensions have to match with mat")
2177: display_numbers = as.matrix(display_numbers)
2178: fmat = matrix(as.character(display_numbers), nrow = nrow(display_numbers), ncol = ncol(display_numbers))
2182: fmat = matrix(sprintf(number_format, mat), nrow = nrow(mat), ncol = ncol(mat))
2185: fmat = matrix(NA, nrow = nrow(mat), ncol = ncol(mat))
2259: ## Select only the ones present in the matrix.
2378: rownames(matrix) = c(keys, 'soft', 'co-occurrence', 'other')
2379: ## colnames(matrix) = paste(to, collapse = ':')
2380: colnames(matrix) = to[1]
2420: matrix['co-occurrence', ] = length(co.occurrences)
2421: cat('Co-occurrence in #samples: ', matrix['co-occurrence', ], '\n')
2426: matrix[keys[i], ] = length(intersect(to.samples, hard.pattern.samples[[keys[i]]]))
2427: cat('Hard exclusivity in #samples:', matrix[keys, ], '\n')
2433: matrix['other', ] = length(intersect(to.samples, union))
2434: cat('Other observations in #samples:', matrix['other', ], '\n')
2438: matrix['soft', ] = length(to.samples) - colSums(matrix)
2439: cat('Soft exclusivity in #samples:', matrix['soft', ], '\n')
2443: sector.color = rep('gray', nrow(matrix) + 1)
2444: link.color = rep('gray', nrow(matrix))
2446: names(sector.color) = c(rownames(matrix), colnames(matrix))
2447: names(link.color) = rownames(matrix)
2465: idx.max = which(matrix == max(matrix))
2466: link.style = matrix(0, nrow=nrow(matrix), ncol=ncol(matrix))
2467: rownames(link.style) = rownames(matrix)
2468: colnames(link.style) = colnames(matrix)
2482: sector.color[colnames(matrix)] = as.colors(x)[as.events(x, genes = to[1], types=to[2])[, 'type' ]]
2489: ## rownames(matrix)[i] = paste(paste(rep(' ', i), collapse = ''), events.names[i, 'event' ])
2491: rownames(matrix)[i] = paste(paste(rep(' ', i), collapse = ''), events.names[i, 'event' ])
2492: else rownames(matrix)[i] = events.names[i, 'event' ]
2494: names(sector.color)[i] = rownames(matrix)[i]
2499: cat('Circlize matrix.\n')
2500: print(matrix)
2508: chordDiagram(matrix,
2536: layout(matrix(c(1,2,3,3), ncol = 2, byrow = TRUE), heights = c(4, 1))
2551: print(matrix)
2553: ## barplot(matrix[, 1], widths, space = 0)
2555: rownames(matrix)[length(keys) + 1] = '2 or more\n(soft-exclusivity)'
2556: rownames(matrix)[length(keys) + 2] = 'all together\n(co-occurrence)'
2558: rownames(matrix)[nrow(matrix)] = 'none of the\nevents'
2560: summary = matrix[ (length(keys) + 1):nrow(matrix), 1, drop = FALSE]
2561: summary = rbind( sum(matrix[1:length(keys),]), summary)
2586: exclus = matrix[1:length(keys), 1, drop = FALSE]
2617: c(paste(sum(matrix[1:length(keys) ,]), 'with 1 event (hard exclusivity)'),
2618: paste(matrix[length(keys) + 1, ], 'with 2 or more events'),
2619: paste(matrix[length(keys) + 2, ], 'with all events (co-occurrence)'),
2620: paste(matrix[length(keys) + 3, ], 'with no events')
20: #' @param ann.stage Boolean value to annotate stage classification, default depends on \code{x}

scBubbletree:R/get_r.R: [ ]
168: class = "boot_r",
112: gap_matrix <- base::matrix(
116: wcss_matrix <- base::matrix(
129: gap_matrix[,j] <- boot_obj$gap[[j]]$logWks-boot_obj$gap[[j]]$logW
130: wcss_matrix[,j] <- boot_obj$wcss[[j]]
141: X = gap_matrix,
145: X = gap_matrix, MARGIN = 2, FUN = stats::var))
156: X = wcss_matrix,
252: logWks <- base::matrix(0, B_gap, 1)
302: logWks <- base::matrix(0, B_gap, 1)
342: mu <- base::matrix(data = mu, nrow = 1)

nempi:other/TCGA.r: [ ]
132: class <- data@colData@listData$definition
10: samplenr <- matrix(NA, length(types), 2)
19: sizemat <- matrix(0, 1, 2)
53: meth2 <- as.matrix(data[, -(1:3)])
201: mut.mat <- matrix(0, length(allsub), length(unique(mutation$Tumor_Sample_Barcode)))
202: type.mat <- matrix("", length(allsub), length(unique(mutation$Tumor_Sample_Barcode)))
230: Mtype0 <- matrix(paste(Mtype0, Mtype[[i]][, which(colnames(Mtype[[i]]) %in% samples)]), nrow(Mtype0))
239: class <- data@colData@listData$definition
267: if (sum(class %in% "Solid Tissue Normal") < 10) {
326: DN <- D[, which(class %in% "Solid Tissue Normal")]
327: DT <- D[, which(class %in% "Primary solid Tumor")]
381: save(clinical, D, M, Mtype, DF, class, meth, cnv, file = paste0(path, type, "_final.rda"))
383: print(table(class))
384: sizemat <- rbind(sizemat, table(class))
387: samplenr[snrcount, 1] <- sum(class %in% "Primary solid Tumor")
388: samplenr[snrcount, 2] <- sum(class %in% "Solid Tissue Normal")
425: P <- matrix(0, length(unique(c(rownames(M), rownames(cnv), rownames(meth)))), length(unique(c(colnames(M), coln...(29 bytes skipped)...
479: Rho <- cbind(P, matrix(0, nrow(P), sum(!(colnames(D) %in% colnames(P)))))
659: library(class)
759: Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
789: Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
792: F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P...(11 bytes skipped)...
800: Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
803: F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P...(11 bytes skipped)...
811: Ptmp <- cbind(P, matrix(0, nrow(P), sum(!(colnames(Rho) %in% colnames(P)))))
814: F <- matrix(c(sum(pmeth >= 1 & P == 1), sum(pmeth >= 1 & P == 0), sum(pmeth == -2 & P == 1), sum(pmeth == 0 & P...(11 bytes skipped)...
817: Fmat <- matrix(c(sum(pmeth == 2), sum(pmeth == 1), sum(pmeth == -2), sum(pmeth == 0)), 2)
860: cormat <- matrix(0, nrow(pmeth), 2)
864: Fmat <- matrix(c(sum(pmeth[i, ] == 2), sum(pmeth[i, ] == 1), sum(pmeth[i, ] == -2), sum(pmeth[i, ] == 0)), 2)
871: Fmat <- matrix(c(sum(pmeth[3, ] %in% c(2,-2) & pmeth[7, ] %in% c(2,-2)),
979: M <- matrix(0, 5, 10)
988: phi <- matrix(0, 5, 5)
206: coln2 <- which(colnames(mutation) %in% "Variant_Classification")
560: svmres <- classpi(D4, full = TRUE, method = "svm")
578: nnres <- classpi(D4, full = TRUE, method = "nnet", MaxNWts = 50000, size = 5) # takes forever
619: rfres <- classpi(D4, full = TRUE, method = "randomForest")

CancerSubtypes:R/internal.R: [ ]
427: class <- unique(x)
674: s.matrix=t(fit$expZ)%*%fit$expZ
169: affinityMatrix <- function(Diff,K=20,sigma=0.5) {
406: classx <- unique(x)
407: classy <- unique(y)
5: #' @param affinity Similarity matrix
20: affinity=as.matrix(affinity)
57: R = matrix(0,k,k)
65: c = matrix(0,n,1)
67: c = c + abs(eigenVectors %% matrix*(R[,j-1],k,1))
95: Y = matrix(0,nrow(eigenVector),ncol(eigenVector))
105: .distanceWeighted2<-function(X,weight) ##X is the expression Matrix(Row is sample, column is feature)
115: XX=matrix(rep(sumsqX, times = X_row), X_row, X_row)
132: #' @param X A data matrix where each row is a different data point
133: #' @param C A data matrix where each row is a different data point. If this matrix is the same as X, pairwise distances for all data points are computed.
139: #' Returns an N x M matrix where N is the number of rows in X and M is the number of rows in M. element (n,m) is the squared E...(70 bytes skipped)...
150: res = matrix(rep(sumsqX,times=ncentres),ndata,ncentres) + t(matrix(rep(sumsqC,times=ndata),ncentres,ndata)) - XC
155: #' This is the affinity Matrix function extracted from SNFtool package.
156: #' Computes affinity matrix from a generic distance matrix
157: #' @param Diff Distance matrix
166: #' Returns an affinity matrix that represents the neighborhood graph of the data points.
175: sortedColumns = as.matrix(t(apply(Diff,2,sort)))
218: ### Calculate the local transition matrix.
226: sumWJ = matrix(0,dim(Wall[[j]])[1], dim(Wall[[j]])[2])
242: # construct the combined affinity matrix by summing diffused matrices
243: W = matrix(0,nrow(Wall[[1]]), ncol(Wall[[1]]))
249: # ensure affinity matrix is symmetrical
262: #affinity: the similarity matrix;
335: R = matrix(0,k,k)
343: c = matrix(0,n,1)
345: c = c + abs(eigenVectors %% matrix*(R[,j-1],k,1))
373: Y = matrix(0,nrow(eigenVector),ncol(eigenVector))
394: A = matrix(0,nrow(xx),ncol(xx));
412: probxy <- matrix(NA, ncx, ncy)
419: probx <- matrix(rowSums(probxy), ncx, ncy)
420: proby <- matrix(colSums(probxy), ncx, ncy, byrow=TRUE)
429: nc <- length(class)
433: prob[i] <- sum(x == class[i])/nx
449: matrix(t(matrix(X,mx,nxn)),mxm,nx*n,byrow=TRUE)
467: #scalar: a logical argument specifying whether scalar covariance matrix should be used
485: #create a big matrix combining multiple data types
510: expZ=matrix(rnorm((k-1)*n),nrow=k-1,ncol=n)
653: ## Generalized Inverse of a Matrix
675: #standardize s.matrix such that the diagonal is 1
676: diag.elements=diag(s.matrix)
678: denom=matrix(rep(diag.elements,n),nrow=n, byrow=T)
679: a=s.matrix/sqrt(denom)/sqrt(t(denom))
685: #flip matrix for plot orientation
686: f.a=t(as.matrix(rev(as.data.frame(t(a)))))
12: #' #W1 = affinityMatrix(Dist1, 20, 0.5)
163: #' W1 = affinityMatrix(Dist1, 20, 0.5)
196: ###You can do various applications on this graph, such as clustering(subtyping), classification, prediction.
409: ncx <- length(classx)
410: ncy <- length(classy)
415: probxy[i, j] <- sum((x == classx[i]) & (y == classy[j])) / nx

TFBSTools:R/DB-methods.r: [ ]
265: matrixClass = tags[["class"]]
607: .store_matrix = function(con, pfm){
649: pfm_matrix = Matrix(pfm)
216: matrixVector = dbGetQuery(con, sqlCMD)[["val"]]
220: FMatrix = matrix(as.integer(matrixVector),
467: matrixSet = switch(opts[["matrixtype"]],
473: xmatrix = .get_Matrix_by_int_id(x, id, type="PFM")
210: .get_Matrix_by_int_id = function(con, int_id, type){
648: .store_matrix_data = function(con, pfm, int_id){
662: .store_matrix_annotation = function(con, pfm, int_id){
679: .store_matrix_species = function(con, pfm, int_id){
697: .store_matrix_acc = function(con, pfm, int_id){
39: if(!"class" %in% names(opts))
40: opts[["class"]] = NULL
62: sqlCMD = paste0("select count() from MATRIX* where
63: BASE_ID= (SELECT BASE_ID from MATRIX where ID='",
65: "AND VERSION>(SELECT VERSION from MATRIX where ID='",
103: sqlCMD = paste0("SELECT ID FROM MATRIX")
118: sqlCMD = paste0("SELECT ID FROM MATRIX WHERE BASE_ID='", baseID, "'")
137: sqlTables = "MATRIX M"
139: # in matrix table: collection
145: # in matrix table: names.
153: sqlTables = c(sqlTables, "MATRIX_SPECIES S")
166: # "class", "type", "comment", "family", "medline", "tax_group"
167: for(tag in c("class", "type", "comment", "family",
170: sqlCMD = paste0("SELECT distinct ID from MATRIX_ANNOTATION where ",
192: sqlCMD = paste0("SELECT VERSION FROM MATRIX WHERE BASE_ID='", baseID,
201: sqlCMD = paste0("SELECT ID FROM MATRIX WHERE BASE_ID='",
211: # Get the pfm matrix
214: sqlCMD = paste0("SELECT val FROM MATRIX_DATA WHERE ID='",
223: # get remaining data in the matrix table: name, collection
225: NAME FROM MATRIX WHERE ID='",
234: sqlCMD = paste0("SELECT TAX_ID FROM MATRIX_SPECIES WHERE ID='", int_id, "'")
245: sqlCMD = paste0("SELECT ACC FROM MATRIX_PROTEIN WHERE ID='", int_id, "'")
252: sqlCMD = paste0("SELECT TAG,VAL FROM MATRIX_ANNOTATION WHERE ID='",
263: if(is.null(tags[["class"]]))
264: tags[["class"]] <- ""
266: tags["class"] = NULL
270: matrixClass=matrixClass,
285: ### get_Matrix_by_ID fetches matrix data under
290: # returns NA if matrix with the given ID is not found.
310: # get matrix using internal ID
311: ans[[id]] <- .get_Matrix_by_int_id(x, int_id, type="PFM")
359: ### get_Matrix_by_name fetches matrix data under
364: # returns NA if matrix with the given name is not found.
373: # For specific versions, use get_Matrix_by_ID($ID.$version)
383: FROM MATRIX WHERE NAME='", eachName, "'")
439: ### get_MatrixSet fetches matrix...(9 bytes skipped)...er for all matrices in the database matching criteria defined by the named arguments and returns a XMatrixList object
441: ...(209 bytes skipped)...database storage, any tag can be used for information retrieval. Additionally, arguments as 'name','class...(1 bytes skipped)...,'collection' can be used (even though they are not tags). By default, only the last version of the matrix is given. The only way to get older matrices out of this to use an array of IDs with actual version...(110 bytes skipped)...
443: # -all: gives absolutely all matrix entry, regardless of versin and collection. Only useful for backup situations and sanity checks. Ta...(35 bytes skipped)...
447: # -all_versions: gives all matrix...(89 bytes skipped)...pical usage is in combiation with a stable IDs without versions to get all versinos of a particular matrix.
449: # -class: structural class names (strings)
453: # -min_ic: float, minimum total information content of the matrix.
454: # -matrixtype: string describing type of matrix to retrieve. If left out, the format will revert to the database format, which is PFM.
457: # my $matrixset = $db->(-class => ['TRP_CLUSTER', 'FORKHEAD'],
460: # gives a set of TFBS::Matrix::PFM objects (given that the matrix models are stored as such) whose (structural clas is 'TRP_CLUSTER' OR'FORKHEAD') AND (the species t...(57 bytes skipped)...
461: # As above, unless IDs with version numbers are used, only one matrix per stable ID wil be returned: the matrix with the highest version number
475: # we assume the matrix IS a PFM,
485: avg_sites = sum(Matrix(xmatrix)) / length(xmatrix)
542: # Deletes the matrix having the given ID from the database
544: # A string. Has to be a matrix ID with version suffix in JASPAR5.
551: stop("You have supplied a non-versioned matrix ID
555: for(dbTable in c("MATRIX_DATA", "MATRIX",
556: "MATRIX_SPECIES", "MATRIX_PROTEIN",
557: "MATRIX_ANNOTATION")){
605: ### utilities functions for store_Matrix
608: # creation of the matrix will also give an internal unique ID (incremental int)
611: # Get version from the matrix ID
627: sqlCMD = paste0("select count() from MATRIX* where VERSION='",
640: sqlCMD = paste0("INSERT INTO MATRIX VALUES (NULL,'", collection, "','",
650: i = rownames(pfm_matrix)[1]
652: for(i in rownames(pfm_matrix)){
653: for(j in seq_len(ncol(pfm_matrix))){
654: sqlCMD = paste0("INSERT INTO MATRIX_DATA VALUES(", int_id, ",'",
655: i, "',", j, ",", pfm_matrix[i,j], ")")
664: if(length(matrixClass(pfm)) != 0)
665: tags[["class"]] = matrixClass(pfm)
667: # we already have those in the MATRIX table
672: sqlCMD = paste0("INSERT INTO MATRIX_ANNOTATION (ID, tag, val) VALUES(",
690: sqlCMD = paste0("INSERT INTO MATRIX_SPECIES VALUES(",
706: sqlCMD = paste0("INSERT INTO MATRIX_PROTEIN VALUES(", int_id,
724: int_id = .store_matrix(x, pfm)
725: .store_matrix_data(x, pfm, int_id)
726: .store_matrix_annotation(x, pfm, int_id)
727: .store_matrix_species(x, pfm, int_id)
728: .store_matrix_acc(x, pfm, int_id)
820: sqlCMD = c("DROP TABLE IF EXISTS MATRIX",
821: "CREATE TABLE MATRIX(
829: "DROP TABLE IF EXISTS MATRIX_DATA",
830: "CREATE TABLE MATRIX_DATA(
837: "DROP TABLE IF EXISTS MATRIX_ANNOTATION",
838: "CREATE TABLE MATRIX_ANNOTATION(
844: "DROP TABLE IF EXISTS MATRIX_SPECIES",
845: "CREATE TABLE MATRIX_SPECIES(
849: "DROP TABLE IF EXISTS MATRIX_PROTEIN",
850: "CREATE TABLE MATRIX_PROTEIN(
2: ## Here are the parameters for get_MatrixSet (searching the jaspar DB)
32: if(!"matrixtype" %in% names(opts))
33: opts[["matrixtype"]] = "PFM"
35: opts[["matrixtype"]] = match.arg(opts[["matrixtype"]],
217: if(length(matrixVector) %% 4 != 0)
219: length(matrixVector), " is incomplete!")
268: ans_pfm = PFMatrix(ID=paste0(baseID, ".", version),
273: profileMatrix=FMatrix
286: ### the given ID from the database and returns a XMatrix object.
287: # Returns : a XMatrix object; the exact type of the object
360: ### the given name from the database and returns a XMatrix object.
361: # Returns : a XMatrix object;
477: if(sum(totalIC(toICM(xmatrix))) < opts[["min_ic"]])
481: if(length(xmatrix) < opts[["length"]])
489: if(opts[["matrixtype"]] == "PFM"){
490: matrixSet = c(matrixSet, list(xmatrix))
491: }else if(opts[["matrixtype"]] == "PWM"){
492: matrixSet = c(matrixSet, list(toPWM(xmatrix)))
493: }else if(opts[["matrixtype"]] == "ICM"){
494: matrixSet = c(matrixSet, list(toICM(xmatrix)))
497: names(matrixSet) = ID(matrixSet)
498: return(matrixSet)
719: setMethod("storeMatrix", signature(x="SQLiteConnection",
733: setMethod("storeMatrix", signature(x="character",
738: storeMatrix(con, pfmList)
741: setMethod("storeMatrix", signature(x="character", pfmList="PFMatrix"),
743: storeMatrix(x, PFMatrixList(pfmList))
746: setMethod("storeMatrix", signature(x="SQLiteConnection",
747: pfmList="PFMatrix"),
749: storeMatrix(x, PFMatrixList(pfmList))
752: setMethod("storeMatrix", signature(x="JASPAR2014", pfmList="PFMatrix"),
754: storeMatrix(x@db, pfmList)
757: setMethod("storeMatrix", signature(x="JASPAR2016", pfmList="PFMatrix"),
759: storeMatrix(x@db, pfmList)
762: setMethod("storeMatrix", signature(x="JASPAR2018", pfmList="PFMatrix"),
764: storeMatrix(x@db, pfmList)
768: setMethod("storeMatrix", signature(x="JASPAR2020", pfmList="PFMatrix"),
770: storeMatrix(x@db, pfmList)
774: setMethod("storeMatrix", signature(x="JASPAR2022", pfmList="PFMatrix"),
776: storeMatrix(x@db, pfmList)
780: setMethod("storeMatrix", signature(x="JASPAR2014",
783: storeMatrix(x@db, pfmList)
786: setMethod("storeMatrix", signature(x="JASPAR2016",
789: storeMatrix(x@db, pfmList)
792: setMethod("storeMatrix", signature(x="JASPAR2018",
795: storeMatrix(x@db, pfmList)
799: setMethod("storeMatrix", signature(x="JASPAR2020",
802: storeMatrix(x@db, pfmList)
806: setMethod("storeMatrix", signature(x="JASPAR2022",
809: storeMatrix(x@db, pfmList)
297: setMethod("getMatrixByID", "SQLiteConnection",
316: ans <- do.call(PFMatrixList, ans)
321: setMethod("getMatrixByID", "character",
328: getMatrixByID(con, ID)
331: setMethod("getMatrixByID", "JASPAR2014",
333: getMatrixByID(x@db, ID)
337: setMethod("getMatrixByID", "JASPAR2016",
339: getMatrixByID(x@db, ID)
342: setMethod("getMatrixByID", "JASPAR2018",
344: getMatrixByID(x@db, ID)
347: setMethod("getMatrixByID", "JASPAR2020",
349: getMatrixByID(x@db, ID)
353: setMethod("getMatrixByID", "JASPAR2022",
355: getMatrixByID(x@db, ID)
374: setMethod("getMatrixByName", "SQLiteConnection",
392: ans[[eachName]] <- getMatrixByID(x, baseID[1])
397: ans <- do.call(PFMatrixList, ans)
402: setMethod("getMatrixByName", "character",
406: getMatrixByName(con, name)
410: setMethod("getMatrixByName", "JASPAR2014",
412: getMatrixByName(x@db, name)
416: setMethod("getMatrixByName", "JASPAR2016",
418: getMatrixByName(x@db, name)
421: setMethod("getMatrixByName", "JASPAR2018",
423: getMatrixByName(x@db, name)
427: setMethod("getMatrixByName", "JASPAR2020",
429: getMatrixByName(x@db, name)
433: setMethod("getMatrixByName", "JASPAR2022",
435: getMatrixByName(x@db, name)
440: # Returns : a XMatrixList object
463: setMethod("getMatrixSet", "SQLiteConnection",
468: "PFM"=PFMatrixList(),
469: "PWM"=PWMatrixList(),
470: "ICM"=ICMatrixList()
502: setMethod("getMatrixSet", "character",
507: getMatrixSet(con, opts)
511: setMethod("getMatrixSet", "JASPAR2014",
513: getMatrixSet(x@db, opts)
517: setMethod("getMatrixSet", "JASPAR2016",
519: getMatrixSet(x@db, opts)
523: setMethod("getMatrixSet", "JASPAR2018",
525: getMatrixSet(x@db, opts)
529: setMethod("getMatrixSet", "JASPAR2020",
531: getMatrixSet(x@db, opts)
535: setMethod("getMatrixSet", "JASPAR2022",
537: getMatrixSet(x@db, opts)
541: setMethod("deleteMatrixHavingID", "SQLiteConnection",
566: setMethod("deleteMatrixHavingID", "character",
570: deleteMatrixHavingID(con, IDs)
574: setMethod("deleteMatrixHavingID", "JASPAR2014",
576: deleteMatrixHavingID(x@db, IDs)
580: setMethod("deleteMatrixHavingID", "JASPAR2016",
582: deleteMatrixHavingID(x@db, IDs)
586: setMethod("deleteMatrixHavingID", "JASPAR2018",
588: deleteMatrixHavingID(x@db, IDs)
592: setMethod("deleteMatrixHavingID", "JASPAR2020",
594: deleteMatrixHavingID(x@db, IDs)
598: setMethod("deleteMatrixHavingID", "JASPAR2022",
600: deleteMatrixHavingID(x@db, IDs)
714: ### Stores the contents of a PFMatrixList object in the database
717: # Args : (PFMatrixList)
720: pfmList="PFMatrixList"),
734: pfmList="PFMatrixList"),
781: pfmList="PFMatrixList"),
787: pfmList="PFMatrixList"),
793: pfmList="PFMatrixList"),
800: pfmList="PFMatrixList"),
807: pfmList="PFMatrixList"),

GSCA:inst/shiny/server.R: [ ]
1537: tags$div(class="row-fluid",
28: onesampleslidervalue <- matrix(0,nrow=5,ncol=3)
29: threesampleslidervalue <- matrix(0,nrow=5,ncol=3)
66: div(class = "busy",
77: div(class = "busy",
405: Maindata$uploadgeneexpr <- as.matrix(read.table(input$Summaryuploadgeneexprfile$datapath,stringsAsFactors=F,blank.lines.skip=TRUE,row.na...(7 bytes skipped)...
410: if (is.matrix(geneid)) {
477: if (is.matrix(geneid)) {
676: SCORE <- matrix(0, nrow=length(ttT), ncol=4)
683: tmpmat <- matrix(c(r1c1,r2c1,r1c2,r2c2),ncol=2)
709: scoremat <- matrix(0,nrow=Maindata$dim,ncol=nrow(Maindata$tab))
718: if (is.matrix(geneid)) {
812: cutoffval <- matrix(0,Maindata$dim,2)
1297: if (is.matrix(tmpcord) && nrow(tmpcord) > 1) {
1316: if (is.matrix(polycord) && nrow(polycord) != 0) {
1320: if (is.matrix(tmpcord) && nrow(tmpcord) > 1) {
1366: if (is.matrix(tmpcord) && nrow(tmpcord) > 2) {
1538: tags$div(class="span11",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot1")),
1539: tags$div(class="span1",plotOutput("GSCAinteractiveplotthreeheatmapzoominplotlab"))
1542: tags$div(class="row-fluid",
1543: tags$div(class="span5",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot1")),
1544: tags$div(class="span5",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot2")),
1545: tags$div(class="span1",plotOutput("GSCAinteractiveplotthreeheatmapzoominplotlab"))
1548: tags$div(class="row-fluid",
1549: tags$div(class="span3",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot1")),
1550: tags$div(class="span3",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot2")),
1551: tags$div(class="span3",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot3")),
1552: tags$div(class="span1",plotOutput("GSCAinteractiveplotthreeheatmapzoominplotlab"))
1555: tags$div(class="row-fluid",
1556: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot1")),
1557: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot2")),
1558: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot3")),
1559: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot4")),
1560: tags$div(class="span1",plotOutput("GSCAinteractiveplotthreeheatmapzoominplotlab"))
1563: tags$div(class="row-fluid",
1564: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot1")),
1565: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot2")),
1566: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot3")),
1567: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot4")),
1568: tags$div(class="span2",plotOutput("GSCAinteractiveplotthreeheatmapzoominplot5")),
1569: tags$div(class="span1",plotOutput("GSCAinteractiveplotthreeheatmapzoominplotlab"))
1936: polycord <<- as.matrix(tmp)
2236: if (is.matrix(tmpcord)) {
72: conditionalPanel(condition="$('html').hasClass('shiny-busy')",

scone:R/sconeReport.R: [ ]
837: Class = factor(strat_col())
8: #' @param qc matrix. QC metrics to be used for QC evaluation report. Required.
20: #' \code{\link{score_matrix}} for details). If NULL, PCA is used for
36: #' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
42: #' qc = as.matrix(cbind(colSums(mat),colSums(mat > 0)))
93: # Parameter matrix
96: # Merged score matrix
113: # Matrix nodes in scone_res
275: " matrix vs. PCs of the QC",
277: "matrix. It also shows the PCA ",
310: "Row Class",
319: label = "Column Class",
540: as.matrix(scone_res$normalized_data[[input$norm_code]])
691: prcomp(as.matrix(qc),center = TRUE, scale = TRUE)
840: ggplot(data.frame(Class,Val ),aes(x = Class,y = Val)) +
841: geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
888: Class = factor(strat_col())
891: ggplot(data.frame(Class,Val ),aes(x = Class,y = Val)) +
892: geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
944: colnames(datt) = c("Class-Bio","Class-Batch","Class-Pam")
101: ## ----- If NULL classifications, Replace with NA ------
745: text(0,labels = "Stratify plots by a multi-level classification.")
792: text(0,labels = "Stratify plots by a multi-level classification.")
820: text(0,labels = "Stratify plots by a multi-level classification.")
928: text(0,labels = "Stratify plots by a multi-level classification.")

ShortRead:R/methods-QA.R: [ ]
243: class <- class(..1)
33: function (class, useFilter = TRUE, addFilter = TRUE, ..., html)
37: sprintf("%s.html", class))
42: new(class, useFilter = mkScalar(as.logical(useFilter)),
191: cat("source:", class(object@src),
193: elts <- paste(sapply(object, class), collapse = " ")
325: q0 <- as(do.call(class(quality(obj)), list(alphabet)), "matrix")
408: q0 <- as(do.call(class(quality(obj)), list(alphabet)), "matrix")
424: names(elts) <- sapply(object, class)
476: if (length(f)) DataFrame(Flag=f, Summary=class(x))

HiCBricks:R/Brick_functions.R: [ ]
2168: Matrix <- Brick_get_matrix(Brick = Brick, chr1 = chr1, chr2 = chr2,
2255: Matrix <- Brick_get_vector_values(Brick = Brick, chr1=chr1, chr2=chr2,
554: Matrix_info <- return_configuration_matrix_info(Brick)
776: Matrix.list.df <- do.call(rbind,chr1.list)
1322: Brick_load_matrix = function(Brick = NA, chr1 = NA, chr2 = NA, resolution = NA,
1613: Matrix.list <- Brick_list_matrices(Brick = Brick, chr1 = chr1,
1657: Matrix.list <- Brick_list_matrices(Brick = Brick, chr1 = chr1, chr2 = chr2,
1713: Matrix.list <- Brick_list_matrices(Brick = Brick, chr1 = chr1, chr2 = chr2,
1803: Matrix.list <- Brick_list_matrices(Brick = Brick, chr1 = chr1, chr2 = chr2,
1898: Matrix.list <- Brick_list_matrices(Brick = Brick, chr1 = chr1, chr2 = chr2,
2228: Brick_get_matrix = function(Brick, chr1, chr2, x_coords,
2364: Class.type <- ._Check_numeric
2365: Class.exp <- c("numeric","integer")
2574: Brick_get_entire_matrix = function(Brick, chr1, chr2, resolution){
2592: entire_matrix <- dataset_handle[]
2848: a_matrix <- .remove_nas(Brick_get_entire_matrix(Brick = Brick,
2850: normalised_matrix <- .normalize_by_distance_values(a_matrix)
2851: correlation_matrix <- cor(normalised_matrix)
206: Configuration_matrix_list <- list()
1419: Brick_load_cis_matrix_till_distance = function(Brick = NA, chr = NA,
1607: Brick_matrix_isdone = function(Brick, chr1, chr2, resolution = NA){
1651: Brick_matrix_issparse = function(Brick, chr1, chr2, resolution = NA){
1703: Brick_matrix_maxdist = function(Brick, chr1, chr2, resolution = NA){
1758: Brick_matrix_exists = function(Brick, chr1, chr2, resolution = NA){
1797: Brick_matrix_minmax = function(Brick, chr1, chr2, resolution = NA){
1843: Brick_matrix_dimensions = function(Brick, chr1, chr2, resolution = NA){
1892: Brick_matrix_filename = function(Brick, chr1, chr2, resolution = NA){
2124: Brick_get_matrix_within_coords = function(Brick, x_coords,
2647: Brick_get_matrix_mcols = function(Brick, chr1, chr2, resolution,
2706: Brick_list_matrix_mcols = function(){
8: #' table associated to the Hi-C experiment, creates a 2D matrix
10: #' project. At the end, this function will return a S4 object of class
21: #' contains 250 entries in the binning table, the _cis_ Hi-C data matrix for
24: #' matrices for chr1,chr2 will be a matrix with dimension 250 rows and
60: #' set to matrix dimensions/100.
74: #' A value of length 1 of class character or numeric specifying the resolution
95: #' \item Min - min value of Hi-C matrix
96: #' \item Max - max value of Hi-C matrix
97: #' \item sparsity - specifies if this is a sparse matrix
99: #' \item Done - specifies if a matrix has been loaded
101: #' \item matrix - \strong{dataset} - contains the matrix
138: #' the function will return an object of class BrickContainer.
235: Configuration_matrix_list <- return_configuration_matrix_info(
267: Configuration_matrix_list[[paste(chrom1, chrom2,
281: Configuration_matrix_list,
527: #' for that chromosome in a Hi-C matrix.
555: current_resolution <- vapply(Matrix_info, function(a_list){
561: chrom1_binned_length <- vapply(Matrix_info[current_resolution],
565: chrom1s <- vapply(Matrix_info[current_resolution],
569: chrom1_max_sizes <- vapply(Matrix_info[current_resolution],
646: #' An object of class ranges specifying the ranges to store in the Brick.
652: #' When an object of class BrickContainer is provided, resolution defines the
661: #' When an object of class BrickContainer is provided, num_cpus defines the
692: if(!(class(ranges) %in% "GRanges") \| ("list" %in% class(ranges))){
693: stop("Object of class Ranges expected")
720: #' List the matrix pairs present in the Brick store.
727: #' @inheritParams Brick_load_matrix
733: #' minimum and maximum values in the matrix, done is a logical value
734: #' specifying if a matrix has been loaded and sparsity specifies if a matrix
735: #' is defined as a sparse matrix.
777: rownames(Matrix.list.df) <- NULL
778: return(Matrix.list.df)
954: BrickContainer_class_check(Brick)
1127: #' Indexes is a column of class \code{IRanges::IntegerList}, which is
1169: stop("Provided chr, start, end do not match expected class ",
1263: #' Load a NxM dimensional matrix into the Brick store.
1269: #' the rows of the matrix
1273: #' the columns of the matrix
1275: #' @param matrix_file \strong{Required}.
1277: #' matrix into the Brick store.
1280: #' The delimiter of the matrix file.
1283: #' If a matrix was loaded before, it will not be replaced. Use remove_prior to
1284: #' override and replace the existing matrix.
1290: #' If true, designates the matrix as being a sparse matrix, and computes the
1306: #' out_dir <- file.path(tempdir(), "matrix_load_test")
1314: #' Matrix_file <- system.file(file.path("extdata",
1318: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1319: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1323: matrix_file = NA, delim = " ", remove_prior = FALSE, num_rows = 2000,
1326: BrickContainer_class_check(Brick)
1328: resolution = resolution, matrix_file = matrix_file,
1348: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
1352: if(Brick_matrix_isdone(Brick = Brick, chr1 = chr1,
1354: stop("A matrix was preloaded before. ",
1367: Matrix.file = matrix_file, delim = delim, Group.path = Group.path,
1375: #' Load a NxN dimensional sub-distance \emph{cis} matrix into
1380: #' @inheritParams Brick_load_matrix
1384: #' the rows and cols of the matrix
1388: #' it does not make sense to load the entire matrix into the data structure, as
1389: #' after a certain distance, the matrix will become extremely sparse. This
1403: #' out_dir <- file.path(tempdir(), "matrix_load_dist_test")
1411: #' Matrix_file <- system.file(file.path("extdata",
1415: #' Brick_load_cis_matrix_till_distance(Brick = My_BrickContainer,
1416: #' chr = "chr2L", resolution = 100000, matrix_file = Matrix_file,
1420: resolution = NA, matrix_file, delim = " ", distance, remove_prior = FALSE,
1425: matrix_file = matrix_file, delim = delim, distance = distance,
1443: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr,
1447: if(Brick_matrix_isdone(Brick = Brick, chr1 = chr,
1449: stop("A matrix was preloaded before. Use remove_prior = TRUE to ",
1461: RetVar <- ._Process_matrix_by_distance(Brick = Brick_filepath,
1462: Matrix.file = matrix_file, delim = delim, Group.path = Group.path,
1470: #' Load a NxN dimensional matrix into the Brick store from an mcool file.
1472: #' Read an mcool contact matrix coming out of 4D nucleome projects into a
1477: #' @inheritParams Brick_load_matrix
1489: #' @param matrix_chunk \strong{Optional}. Default 2000.
1490: #' The nxn matrix square to fill per iteration in a mcool file.
1493: #' cooler_read_limit sets the upper limit for the number of records per matrix
1495: #' matrix_chunk value will be re-evaluated dynamically.
1521: #' resolution = 50000, matrix_chunk = 2000, remove_prior = TRUE,
1527: #' @seealso \code{\link{Create_many_Bricks_from_mcool}} to create matrix from
1533: matrix_chunk = 2000, cooler_read_limit = 10000000, remove_prior = FALSE,
1569: resolution = resolution, matrix_chunk = matrix_chunk,
1575: #' Check if a matrix has been loaded for a chromosome pair.
1579: #' @inheritParams Brick_load_matrix
1581: #' @return Returns a logical vector of length 1, specifying if a matrix has
1588: #' out_dir <- file.path(tempdir(), "matrix_isdone_test")
1596: #' Matrix_file <- system.file(file.path("extdata",
1600: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1601: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1604: #' Brick_matrix_isdone(Brick = My_BrickContainer, chr1 = "chr2L",
1609: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
1615: return(Matrix.list[Matrix.list$chr1 == chr1 &
1616: Matrix.list$chr2 == chr2, "done"])
1619: #' Check if a matrix for a chromosome pair is sparse.
1623: #' @inheritParams Brick_load_matrix
1625: #' @return Returns a logical vector of length 1, specifying if a matrix was
1626: #' loaded as a sparse matrix.
1632: #' out_dir <- file.path(tempdir(), "matrix_issparse_test")
1640: #' Matrix_file <- system.file(file.path("extdata",
1644: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1645: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1648: #' Brick_matrix_issparse(Brick = My_BrickContainer, chr1 = "chr2L",
1653: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
1659: return(Matrix.list[Matrix.list$chr1 == chr1 &
1660: Matrix.list$chr2 == chr2, "sparsity"])
1664: #' Get the maximum loaded distance from the diagonal of any matrix.
1666: #' If values beyond a certain distance were not loaded in the matrix, this
1670: #' `Brick_matrix_maxdist` will return this parameter.
1674: #' @inheritParams Brick_load_matrix
1677: #' distance loaded for that matrix
1684: #' out_dir <- file.path(tempdir(), "matrix_maxdist_test")
1692: #' Matrix_file <- system.file(file.path("extdata",
1696: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1697: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1700: #' Brick_matrix_maxdist(Brick = My_BrickContainer, chr1 = "chr2L",
1705: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
1709: if(!Brick_matrix_isdone(Brick = Brick, chr1 = chr1, chr2 = chr2,
1715: return((Matrix.list[Matrix.list$chr1 == chr1 &
1716: Matrix.list$chr2 == chr2, "distance"]))
1722: #' are provided. If a user is in doubt regarding whether a matrix is present or
1729: #' @inheritParams Brick_load_matrix
1731: #' @return Returns a logical vector of length 1, specifying if the matrix
1739: #' out_dir <- file.path(tempdir(), "matrix_exists_test")
1747: #' Matrix_file <- system.file(file.path("extdata",
1751: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1752: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1755: #' Brick_matrix_exists(Brick = My_BrickContainer, chr1 = "chr2L",
1765: #' Return the value range of the matrix
1769: #' @inheritParams Brick_load_matrix
1772: #' maximum finite real values in the matrix.
1778: #' out_dir <- file.path(tempdir(), "matrix_minmax_test")
1786: #' Matrix_file <- system.file(file.path("extdata",
1790: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1791: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1794: #' Brick_matrix_minmax(Brick = My_BrickContainer, chr1 = "chr2L",
1799: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
1805: Filter <- Matrix.list$chr1 == chr1 & Matrix.list$chr2 == chr2
1806: Extent <- c(Matrix.list[Filter, "min"],Matrix.list[Filter, "max"])
1810: #' Return the dimensions of a matrix
1814: #' @inheritParams Brick_load_matrix
1816: #' @return Returns the dimensions of a Hi-C matrix for any given
1824: #' out_dir <- file.path(tempdir(), "matrix_dimension_test")
1832: #' Matrix_file <- system.file(file.path("extdata",
1836: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1837: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1840: #' Brick_matrix_dimensions(Brick = My_BrickContainer, chr1 = "chr2L",
1844: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
1853: dataset.path = Reference.object$hdf.matrix.name,
1859: #' Return the filename of the loaded matrix
1863: #' @inheritParams Brick_load_matrix
1866: #' the currently loaded matrix.
1873: #' out_dir <- file.path(tempdir(), "matrix_filename_test")
1881: #' Matrix_file <- system.file(file.path("extdata",
1885: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1886: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1889: #' Brick_matrix_filename(Brick = My_BrickContainer, chr1 = "chr2L",
1894: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
1900: Filter <- Matrix.list$chr1 == chr1 & Matrix.list$chr2 == chr2
1901: Extent <- Matrix.list[Filter, "filename"]
1914: #' A string specifying the chromosome for the cis Hi-C matrix from which values
1947: #' Matrix_file <- system.file(file.path("extdata",
1951: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
1952: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1966: #' @seealso \code{\link{Brick_get_matrix_within_coords}} to get matrix by
1967: #' using matrix coordinates, \code{\link{Brick_fetch_row_vector}} to get values
1969: #' to get values using matrix coordinates, \code{\link{Brick_get_matrix}} to
1970: #' get matrix by using matrix coordinates.
1979: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr,
1981: !Brick_matrix_isdone(Brick = Brick, chr1 = chr,
1993: Max.dist <- Brick_matrix_maxdist(Brick = Brick, chr1 = chr, chr2 = chr,
1997: "this matrix was at a distance of "
2041: Name = Reference.object$hdf.matrix.name,
2055: #' Return a matrix subset between two regions.
2057: #' `Brick_get_matrix_within_coords` will fetch a matrix subset after
2060: #' This function calls \code{\link{Brick_get_matrix}}.
2075: #' If true, will force the retrieval operation when matrix contains loaded
2080: #' the matrix is returned.
2082: #' @return Returns a matrix of dimension x_coords binned length by y_coords
2090: #' out_dir <- file.path(tempdir(), "get_matrix_coords_test")
2098: #' Matrix_file <- system.file(file.path("extdata",
2102: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2103: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2106: #' Brick_get_matrix_within_coords(Brick = My_BrickContainer,
2111: #' Brick_get_matrix_within_coords(Brick = My_BrickContainer,
2118: #' @seealso \code{\link{Brick_get_matrix}} to get matrix by using matrix
2122: #' \code{\link{Brick_get_vector_values}} to get values using matrix
2136: " found x_coords class ", class(x_coords), " and y_coords class ",
2137: class(y_coords))
2152: if(!Brick_matrix_isdone(Brick = Brick, chr1 = chr1, chr2 = chr2,
2154: stop(chr1," ",chr2," matrix is yet to be loaded into the class.")
2171: return(Matrix)
2174: #' Return a matrix subset.
2176: #' `Brick_get_matrix` will fetch a matrix subset between row values
2182: #' @inheritParams Brick_load_matrix
2191: #' If provided a data transformation with FUN will be applied before the matrix
2194: #' @inheritParams Brick_get_matrix_within_coords
2196: #' @return Returns a matrix of dimension x_coords length by y_coords length.
2203: #' out_dir <- file.path(tempdir(), "get_matrix_test")
2211: #' Matrix_file <- system.file(file.path("extdata",
2215: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2216: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2219: #' Brick_get_matrix(Brick = My_BrickContainer, chr1 = "chr2L", chr2 = "chr2L",
2222: #' @seealso \code{\link{Brick_get_matrix_within_coords}} to get matrix by using
2223: #' matrix genomic coordinates, \code{\link{Brick_get_values_by_distance}} to
2227: #' matrix coordinates.
2243: if(!Brick_matrix_isdone(Brick = Brick, chr1 = chr1, chr2 = chr2,
2245: stop(chr1,chr2," matrix is yet to be loaded into the class.\n")
2258: return(Matrix)
2260: return(FUN(Matrix))
2266: #' `Brick_fetch_row_vector` will fetch any given rows from a matrix. If
2273: #' @inheritParams Brick_load_matrix
2293: #' @inheritParams Brick_get_matrix_within_coords
2300: #' If provided a data transformation with FUN will be applied before the matrix
2320: #' Matrix_file <- system.file(file.path("extdata",
2324: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2325: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2334: #' @seealso \code{\link{Brick_get_matrix_within_coords}} to get matrix by
2335: #' using matrix genomic coordinates, \code{\link{Brick_get_values_by_distance}}
2338: #' subset them, \code{\link{Brick_get_matrix}} to get matrix by using
2339: #' matrix coordinates.
2354: max.dist <- Brick_matrix_maxdist(Brick = Brick, chr1 = chr1, chr2 = chr2,
2357: stop("Provided Chromosomes does not appear to be of class character")
2359: if(!Brick_matrix_isdone(Brick = Brick, chr1 = chr1, chr2 = chr2,
2361: stop(chr1,chr2," matrix is yet to be loaded.")
2368: Class.type <- is.character
2369: Class.exp <- "character"
2371: if(!Class.type(vector)){
2372: stop("vector must be of class ",
2373: ifelse(length(Class.exp)>1,paste(Class.exp,collapse=" or "),
2374: paste(Class.exp))," when by has value ",by)
2437: #' other matrix retrieval functions.
2441: #' @inheritParams Brick_load_matrix
2457: #' @inheritParams Brick_get_matrix_within_coords
2460: #' returns a matrix of dimension xaxis length by yaxis length.
2477: #' Matrix_file <- system.file(file.path("extdata",
2481: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2482: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2501: stop("Provided Chromosomes does not appear to be of class character")
2503: if(!Brick_matrix_isdone(Brick = Brick, chr1 = chr1, chr2 = chr2,
2505: stop(chr1,chr2," matrix is yet to be loaded.")
2513: Max.dist <- Brick_matrix_maxdist(Brick = Brick, chr1 = chr1, chr2 = chr2,
2518: "this matrix was at a distance of ",
2527: Brick = Brick_filepath, Name = Reference.object$hdf.matrix.name,
2536: #' Return an entire matrix for provided chromosome pair for a resolution.
2538: #' `Brick_get_entire_matrix` will return the entire matrix for the entire
2539: #' chromosome pair provided an object of class BrickContainer, and values for
2544: #' @inheritParams Brick_load_matrix
2546: #' @return Returns an object of class matrix with dimensions corresponding to
2563: #' Matrix_file <- system.file(file.path("extdata",
2567: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2568: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2571: #' Entire_matrix <- Brick_get_entire_matrix(Brick = My_BrickContainer,
2579: stop("Provided Chromosomes does not appear to be of class character")
2581: if(!Brick_matrix_isdone(Brick = Brick, chr1 = chr1, chr2 = chr2,
2583: stop(chr1,chr2," matrix is yet to be loaded.")
2590: Brick = Brick_filepath, Name = Reference_object$hdf.matrix.name,
2594: return(entire_matrix)
2597: #' Get the matrix metadata columns in the Brick store.
2599: #' `Brick_get_matrix_mcols` will get the specified matrix metadata column for
2600: #' a chr1 vs chr2 Hi-C data matrix. Here, chr1 represents the rows and chr2
2601: #' represents the columns of the matrix. For cis Hi-C matrices, where
2615: #' @inheritParams Brick_load_matrix
2618: #' A character vector of length 1 specifying the matrix metric to retrieve
2620: #' @return Returns a 1xN dimensional vector containing the specified matrix
2628: #' out_dir <- file.path(tempdir(), "get_matrix_mcols_test")
2636: #' Matrix_file <- system.file(file.path("extdata",
2640: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2641: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2644: #' Brick_get_matrix_mcols(Brick = My_BrickContainer, chr1 = "chr2L",
2652: Meta.cols <- Reference.object$hdf.matrix.meta.cols()
2653: BrickContainer_class_check(Brick)
2657: if(!Brick_matrix_exists(Brick = Brick, chr1 = chr1, chr2 = chr2,
2659: stop("Matrix for this chromsome pair does not exist.\n")
2661: if(!Brick_matrix_isdone(Brick = Brick, chr1 = chr1, chr2 = chr2,
2663: stop("Matrix for this chromsome pair is yet to be loaded.\n")
2668: if(!Brick_matrix_issparse(Brick = Brick, chr1 = chr1, chr2 = chr2,
2670: stop("This matrix is not a sparse matrix.",
2685: #' List the matrix metadata columns in the Brick store.
2687: #' `Brick_get_matrix_mcols` will list the names of all matrix metadata
2690: #' @return Returns a vector containing the names of all matrix metadata columns
2697: #' out_dir <- file.path(tempdir(), "list_matrix_mcols_test")
2705: #' Brick_list_matrix_mcols()
2708: Meta.cols <- Reference.object$hdf.matrix.meta.cols()
2714: #' upper triangle sparse matrix
2716: #' `Brick_export_to_sparse` will accept as input an object of class
2719: #' objects as a upper triangle sparse matrix (col > row) containing
2746: #' Matrix_file <- system.file(file.path("extdata",
2750: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2751: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2833: #' Matrix_file <- system.file(file.path("extdata",
2837: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2838: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2847: BrickContainer_class_check(Brick)
2852: correlation_matrix <- .remove_nas(correlation_matrix)
2856: pca_list <- prcomp(correlation_matrix)
2868: #' @inheritParams Brick_load_matrix
2873: #' sparse matrix
2875: #' @param matrix_chunk \strong{Optional}. Default 2000.
2876: #' The nxn matrix square to fill per iteration.
2900: #' Matrix_file <- system.file(file.path("extdata",
2904: #' Brick_load_matrix(Brick = My_BrickContainer, chr1 = "chr2L",
2905: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2918: resolution = NULL, batch_size = 1000000, matrix_chunk = 2000,
2921: BrickContainer_class_check(Brick)
2930: delim = delim, resolution = resolution, matrix_chunk = matrix_chunk,
173: Reference.object <- GenomicMatrix$new()
330: Reference.object <- GenomicMatrix$new()
426: Reference.object <- GenomicMatrix$new()
468: Reference.object <- GenomicMatrix$new()
549: Reference.object <- GenomicMatrix$new()
617: Reference.object <- GenomicMatrix$new()
691: Reference.object <- GenomicMatrix$new()
756: Reference.object <- GenomicMatrix$new()
810: Reference.object <- GenomicMatrix$new()
892: Reference.object <- GenomicMatrix$new()
953: Reference.object <- GenomicMatrix$new()
1086: Reference.object <- GenomicMatrix$new()
1325: Reference.object <- GenomicMatrix$new()
1366: RetVar <- ._ProcessMatrix_(Brick = Brick_filepath,
1423: Reference.object <- GenomicMatrix$new()
1535: Reference.object <- GenomicMatrix$new()
1608: Reference.object <- GenomicMatrix$new()
1652: Reference.object <- GenomicMatrix$new()
1704: Reference.object <- GenomicMatrix$new()
1798: Reference.object <- GenomicMatrix$new()
1848: Reference.object <- GenomicMatrix$new()
1893: Reference.object <- GenomicMatrix$new()
1977: Reference.object <- GenomicMatrix$new()
2494: Reference.object <- GenomicMatrix$new()
2575: Reference_object <- GenomicMatrix$new()
2651: Reference.object <- GenomicMatrix$new()
2707: Reference.object <- GenomicMatrix$new()
2760: Reference.object <- GenomicMatrix$new()
2920: Reference.object <- GenomicMatrix$new()

GRaNIE:R/core.R: [ ]
5509: d = tibble::add_row(d, r_positive = r_pos, class = classCur, peak_gene.p.raw.class = pclassCur, n = 0)
686: peak.GC.class = cut(.data$`G\|C`, breaks = seq(0,1,1/nBins), include.lowest = TRUE, ordered_result = TRUE)) %>%
895: peaks_GC_class = cut(GC_data.df$peak.GC.perc, breaks = seq(0,1,1/nBins), include.lowest = TRUE, ordered_result = T...(4 bytes skipped)...
1689: matrix1.norm.TFs.df = dplyr::filter(matrix1, .data$ENSEMBL %in% HOCOMOCO_mapping$TF.ENSEMBL, rowSums != 0)
443: .storeAsMatrixOrSparseMatrix <- function (GRN, df, ID_column, slotName, threshold = 0.1) {
720: .normalizeCountMatrix <- function(GRN, data, normalization, additionalParams =list()) {
4657: classPeaks = class(GRN@data$peaks$counts)
4670: classRNA = class(GRN@data$RNA$counts)
4911: classPeaks = class(GRN@data$peaks$counts)
4924: classRNA = class(GRN@data$RNA$counts)
5504: for (classCur in networkType_details){
5557: classAll = c(as.character(d2$class), d3$r_positive),
2739: GC_class.cur = GC_classes_foreground.df$GC_class[i]
5586: peak_gene.p.raw.class.bin = as.numeric(.data$peak_gene.p.raw.class)) %>%
698: GC_classes.df = GC.data %>%
1558: TFBS_bindingMatrix.df = tibble::as_tibble(res.l)
2002: AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05,
2435: GC_classes_foreground.df = peaksForeground %>%
2443: GC_classes_background.df = peaksBackground %>%
2490: GC_classes_all.df = dplyr::full_join(GC_classes_foreground.df, GC_classes_background.df, suffix = c(".fg",".bg"), by = "GC_class") %>%
5597: .classFreq_label <- function(tbl_freq) {
5505: for (pclassCur in levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class)) {
3: #' Create and initialize a \code{\linkS4class{GRN}} object.
24: #' @return Empty \code{\linkS4class{GRN}} object
127: #' Add data to a \code{\linkS4class{GRN}} object.
129: #' This function adds both RNA and peak data to a \code{\linkS4class{GRN}} object, along with data normalization.
167: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function (e.g., slots \code{GRN@data$peaks} and \code{GRN@d...(9 bytes skipped)...
293: countsPeaks.norm.df = .normalizeCountMatrix(GRN, counts_peaks %>% tibble::column_to_rownames("peakID") %>% as.matrix(),
300: countsRNA.norm.df = .normalizeCountMatrix(GRN, counts_rna %>% tibble::column_to_rownames("ENSEMBL") %>% as.matrix(),
361: # Store the matrices either as normal or sparse matrix
447: # Store as sparse matrix if enough 0s
453: as.matrix()
456: fractionZero = (length(df.m) - Matrix::nnzero(df.m)) / length(df.m)
460: futile.logger::flog.info(paste0("Storing ", slotName, " matrix as sparse matrix because fraction of 0s is > ", threshold, " (", fractionZero, ")"))
699: dplyr::group_by(.data$peak.GC.class) %>%
702: tidyr::complete(.data$peak.GC.class, fill = list(n = 0)) %>%
706: #ggplot2::ggplot(GC.data, ggplot2::aes(GC.class)) + geom_histogram(stat = "count") + ggplot2::theme_bw()
708: #ggplot2::ggplot(GC_classes.df , ggplot2::aes(GC.class, n_rel)) + geom_bar(stat = "identity") + ggplot2::theme_bw()
798: # the normalization factors matrix should not have 0's in it
898: GC_class = peaks_GC_class,
942: .gcqn <- function(data, GC_class, summary='mean', round=FALSE){
944: gcBinNormCounts <- matrix(NA, nrow=nrow(data), ncol=ncol(data), dimnames=list(rownames(data),colnames(data)))
946: for(ii in 1:nlevels(GC_class)){
948: id <- which(GC_class==levels(GC_class)[ii])
981: # gcBinNormCounts <- matrix(NA, nrow=nrow(counts), ncol=ncol(counts), dimnames=list(rownames(counts),colnames(counts)))
982: # for(ii in 1:nlevels(peaksAnnotation$peak.GC.class)){
983: # id <- which(peaksAnnotation$peak.GC.class==levels(peaksAnnotation$peak.GC.class)[ii])
1013: # input = as.matrix(rawCounts[,-rmCols])
1015: # input = as.matrix(rawCounts)
1075: #' Filter RNA-seq and/or peak data from a \code{\linkS4class{GRN}} object
1081: ...(166 bytes skipped)...s_peak_gene}}. \strong{This function does NOT (re)filter existing connections when the \code{\linkS4class{GRN}} object already contains connections. Thus, upon re-execution of this function with different ...(60 bytes skipped)...
1106: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function.
1365: #' Add TFBS to a \code{\linkS4class{GRN}} object
1374: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function (\code{GRN@annotation$TFs} in pa...(9 bytes skipped)...
1493: #' Overlap peaks and TFBS for a \code{\linkS4class{GRN}} object
1498: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function (\code{GRN@data$TFs$TF_peak_overlap} in particular...(1 bytes skipped)...
1571: # Collect binary 0/1 binding matrix from all TF and concatenate
1578: GRN@data$TFs$TF_peak_overlap = .asSparseMatrix(as.matrix(GRN@data$TFs$TF_peak_overlap),
1676: .correlateMatrices <- function(matrix1, matrix_peaks, HOCOMOCO_mapping, corMethod = "pearson", whitespacePrefix = " ") {
1686: rowSums = rowSums(dplyr::select(matrix1, -.data$ENSEMBL))
1691: nFiltered1 = dplyr::filter(matrix1, ! .data$ENSEMBL %in% HOCOMOCO_mapping$TF.ENSEMBL) %>% nrow()
1692: nFiltered2 = dplyr::filter(matrix1, rowSums == 0) %>% nrow()
1694: diff = nrow(matrix1) - nrow(matrix1.norm.TFs.df)
1696: message = paste0(whitespacePrefix, "Retain ", nrow(matrix1.norm.TFs.df), " unique genes from TF/gene data out of ", nrow(matrix1),
1702: if (nrow(matrix1.norm.TFs.df) == 0) {
1707: HOCOMOCO_mapping.exp = dplyr::filter(HOCOMOCO_mapping, .data$TF.ENSEMBL %in% matrix1.norm.TFs.df$ENSEMBL)
1708: futile.logger::flog.info(paste0(whitespacePrefix, "Correlate TF/gene data for ", nrow(matrix1.norm.TFs.df), " unique Ensembl IDs (TFs) and peak counts for ", nrow(matrix_peaks), " peaks."))
1711: # matrix1: rows: all TF genes, columns: all samples
1712: # matrix_peaks: rows: peak IDs, columns: samples
1718: cor.m = suppressWarnings(t(cor(t(dplyr::select(matrix1.norm.TFs.df, -.data$ENSEMBL)), t(dplyr::select(matrix_peaks, -.data$peakID)), method = corMethod)))
1720: colnames(cor.m) = matrix1.norm.TFs.df$ENSEMBL
1721: rownames(cor.m) = matrix_peaks$peakID
1772: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function
1797: # Input: Raw peak counts per TF and TF-binding matrix
1813: TF.activity.m = matrix(NA, nrow = length(allTF), ncol = length(GRN@config$sharedSamples),
1823: # Filter count matrix to those peaks with TFBS
1872: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function.
1963: # Therefore, use TF names as row names, same as with the TF Activity matrix
1986: #' Run the activator-repressor classification for the TFs for a \code{\linkS4class{GRN}} object
1996: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
2028: message = paste0("Could not find peak - TF matrix. Run the function overlapPeaksAndTFBS first / again")
2080: TF_peak_cor = .correlateMatrices(matrix1 = counts1,
2081: matrix_peaks = counts_peaks,
2110: t.cor.sel.matrix = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground),
2111: t.cor.sel.matrix.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
2125: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_density"), "_", connectionTypeCur, suffixFile, ".pdf")
2135: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_medianClass"), "_", connectionTypeCur, suffixFile, ".pdf")
2148: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_densityClass"), "_", connectionTypeCur, suffixFile, ".pdf")
2180: .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground), convertNA_to_zero = TRUE)
2182: .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background), convertNA_to_zero = TRUE)
2209: #' Add TF-peak connections to a \code{\linkS4class{GRN}} object
2227: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
2277: message = paste0("Could not find peak - TF matrix. Run the function overlapPeaksAndTFBS first / again")
2371: peaksCor.m = .correlateMatrices( matrix1= counts_connectionTypeCur,
2372: matrix_peaks = counts_peaks,
2436: dplyr::group_by(.data$GC_class) %>%
2439: tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
2444: dplyr::group_by(.data$GC_class) %>%
2447: tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
2505: peaksBackgroundGCCur = peaksBackground %>% dplyr::filter(.data$GC_class == GC_classes_foreground.df$GC_class[i])
2744: dplyr::filter(.data$GC_class == GC_class.cur) %>%
2746: #futile.logger::flog.info(paste0(" GC.class ", GC.class.cur, ": Required: ", requiredNoPeaks, ", available: ", availableNoPeaks))
2751: #futile.logger::flog.info(paste0(" Mimicking distribution FAILED (GC class ", GC.class.cur, " could not be mimicked"))
2774: #' Add peak-gene connections to a \code{\linkS4class{GRN}} object
2791: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
3173: #res.m = matrix(NA, ncol = 2, nrow = nrow(overlaps.sub.filt.df), dimnames = list(NULL, c("p.raw", "peak_gene.r")))
3250: res.m = matrix(NA, ncol = 6, nrow = end - start + 1,
3253: res.m = matrix(NA, ncol = 2, nrow = end - start + 1, dimnames = list(NULL, c("p.raw", "r")))
3370: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
3985: #' Add TF-gene correlations to a \code{\linkS4class{GRN}} object.
3995: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
4246: ...(4 bytes skipped)...enerate a summary for the number of connections for different filtering criteria for a \code{\linkS4class{GRN}} object.
4265: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
4563: #' @return An small example \code{\linkS4class{GRN}} object
4620: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object
4622: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object.
4623: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4627: #' @param asMatrix...(128 bytes skipped)... without an ID column (see \code{includeIDColumn}). If set to \code{TRUE}, counts are returned as a matrix with the ID column as row names.
4637: ...(37 bytes skipped)... the type as indicated by the function parameters. This function does NOT return a \code{\linkS4class{GRN}} object.
4658: if ("matrix" %in% classPeaks) {
4663: message = paste0("Unsupported class for GRN@data$peaks$counts. Contact the authors.")
4671: if ("matrix" %in% classRNA) {
4676: message = paste0("Unsupported class for GRN@data$RNA$counts. Contact the authors.")
4726: #' Extract connections or links from a \code{\linkS4class{GRN}} object as da data frame.
4729: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4743: ...(5 bytes skipped)...eturn A data frame with the requested connections. This function does NOT return a \code{\linkS4class{GRN}} object.
4874: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object
4876: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object.
4877: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4881: #' @param asMatrix...(128 bytes skipped)... without an ID column (see \code{includeIDColumn}). If set to \code{TRUE}, counts are returned as a matrix with the ID column as row names.
4891: ...(37 bytes skipped)... the type as indicated by the function parameters. This function does NOT return a \code{\linkS4class{GRN}} object.
4912: if ("matrix" %in% classPeaks) {
4917: message = paste0("Unsupported class for GRN@data$peaks$counts. Contact the authors.")
4925: if ("matrix" %in% classRNA) {
4930: message = paste0("Unsupported class for GRN@data$RNA$counts. Contact the authors.")
4980: #' Extract connections or links from a \code{\linkS4class{GRN}} object as da data frame.
4983: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4997: ...(5 bytes skipped)...eturn A data frame with the requested connections. This function does NOT return a \code{\linkS4class{GRN}} object.
5129: ...(0 bytes skipped)...#' Retrieve parameters for previously used function calls and general parameters for a \code{\linkS4class{GRN}} object.
5131: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
5137: #' @return The requested parameters. This function does NOT return a \code{\linkS4class{GRN}} object.
5183: #' @return An updated \code{\linkS4class{GRN}} object, with some slots being deleted (\code{GRN@data$TFs$classification} as well as \code{GRN@stats$connectionDetails.l})
5217: #' @return An updated \code{\linkS4class{GRN}} object, with the output directory being adjusted accordingly
5411: "plot_class_density" = "TF_classification_densityPlots",
5412: "plot_class_medianClass" = "TF_classification_stringencyThresholds",
5413: "plot_class_densityClass" = "TF_classification_summaryHeatmap",
5498: dplyr::group_by(.data$r_positive, class, .data$peak_gene.p.raw.class) %>%
5507: row = which(d$r_positive == r_pos & d$class == classCur & as.character(d$peak_gene.p.raw.class) == as.character(pclassCur))
5515: # Restore the "ordered" factor for class
5516: d$peak_gene.p.raw.class = factor(d$peak_gene.p.raw.class, ordered = TRUE, levels = levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class))
5521: dplyr::group_by(.data$r_positive, .data$class) %>%
5527: dplyr::group_by(class, .data$peak_gene.p.raw.class)%>%
5536: normFactor_real = dplyr::filter(dsum, class == !! (networkType_details[1])) %>% dplyr::pull(.data$sum_n) %>% sum() /
5537: dplyr::filter(dsum, class == !! (networkType_details[2])) %>% dplyr::pull(.data$sum_n) %>% sum()
5541: dplyr::group_by(.data$peak_gene.p.raw.class, .data$r_positive) %>%
5542: dplyr::summarise(n_real = .data$n[class == !! (names(colors_vec)[1]) ],
5543: n_permuted = .data$n[class == !! (names(colors_vec)[2]) ]) %>%
5551: stopifnot(identical(levels(d2$peak_gene.p.raw.class), levels(d3$peak_gene.p.raw.class)))
5554: d_merged <- tibble::tibble(peak_gene.p.raw.class = c(as.character(d2$peak_gene.p.raw.class),
5555: as.character(d3$peak_gene.p.raw.class)),
5560: peak_gene.p.raw.class = factor(.data$peak_gene.p.raw.class, levels = levels(d2$peak_gene.p.raw.class)))
5562: d4 = tibble::tibble(peak_gene.p.raw.class = unique(d$peak_gene.p.raw.class),
5569: row_d2 = which(d2$class == networkType_details[1] & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5573: row_d2 = which(d2$class == paste0("random_",range) & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5577: row_d3 = which(d3$r_positive == TRUE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5579: row_d3 = which(d3$r_positive == FALSE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5587: dplyr::arrange(.data$peak_gene.p.raw.class.bin)
5589: d4_melt = reshape2::melt(d4, id = c("peak_gene.p.raw.class.bin", "peak_gene.p.raw.class")) %>%
5642: ...(36 bytes skipped)...ources of biological and technical variation for features (TFs, peaks, and genes) in a \code{\linkS4class{GRN}} object
5662: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function to \code{GRN@stats$varianceParti...(111 bytes skipped)...
5720: coltypes = meta %>% dplyr::summarise_all(class)
5797: # row_order <- matrixStats::rowVars(as.matrix(data.l[[dataType]]) ) %>% order(decreasing = T)
88: # Stringencies for AR classification
94: # Colors for the different classifications
188: checkmate::assertClass(GRN, "GRN")
362: GRN@data$peaks$counts = .storeAsMatrixOrSparseMatrix(GRN, df = data.l[["peaks"]], ID_column = "peakID", slotName = "GRN@data$peaks$counts")
365: GRN@data$peaks$counts_raw = .storeAsMatrixOrSparseMatrix(GRN, df = counts_peaks %>% dplyr::select("peakID", tidyselect::one_of(GRN@config$sharedSamples)),
369: GRN@data$RNA$counts = .storeAsMatrixOrSparseMatrix(GRN, df = data.l[["RNA"]], ID_column = "ENSEMBL", slotName = "GRN@data$RNA$counts")
372: GRN@data$RNA$counts_raw = .storeAsMatrixOrSparseMatrix(GRN, df = counts_rna %>% dplyr::select("ENSEMBL", tidyselect::one_of(GRN@config$sharedSamples)),
461: df.m = .asSparseMatrix(df.m, convertNA_to_zero = FALSE,
547: countsPeaks.clean = getCounts(GRN, type = "peaks", permuted = FALSE, asMatrix = TRUE, includeFiltered = TRUE)
552: rowMedians_peaks = matrixStats::rowMedians(countsPeaks.clean)
553: CV_peaks = matrixStats::rowSds(countsPeaks.clean) / rowMeans_peaks
634: countsRNA.m = getCounts(GRN, type = "rna", permuted = FALSE, asMatrix = TRUE, includeFiltered = TRUE)
640: rowMedians_rna = matrixStats::rowMedians(countsRNA.m)
641: CV_rna = matrixStats::rowSds(countsRNA.m) / rowMeans_rna
712: GRN@stats$peaks$GC = GC_classes.df
722: checkmate::assertMatrix(data)
733: dd <- suppressMessages(DESeq2::DESeqDataSetFromMatrix(countData = data,
930: refdist <- matrixStats::rowMedians(counts.sort)
971: # counts = getCounts(GRN, type = "peaks", asMatrix = TRUE, includeFiltered = TRUE)
1035: # dd <- DESeq2::DESeqDataSetFromMatrix(countData = countDataNew,
1123: checkmate::assertClass(GRN, "GRN")
1381: checkmate::assertClass(GRN, "GRN")
1508: checkmate::assertClass(GRN, "GRN")
1560: if (!all(colnames(TFBS_bindingMatrix.df) %in% GRN@config$allTF)) {
1572: GRN@data$TFs$TF_peak_overlap = TFBS_bindingMatrix.df %>%
1777: checkmate::assertClass(GRN, "GRN")
1803: countsPeaks = .normalizeCountMatrix(GRN@data$peaks$counts_orig, normalization = normalization)
1875: checkmate::assertClass(GRN, "GRN")
1930: countsNorm = .normalizeCountMatrix(data %>% dplyr::select(-.data$ENSEMBL), normalization = normalization)
1984: ######## AR classification ########
1989: ...(47 bytes skipped)...c[0,1]. Default 0.05. Significance threshold for Wilcoxon test that is run in the end for the final classification. See the Vignette and diffTF paper for details.
2000: #' # GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)
2010: checkmate::assertClass(GRN, "GRN")
2024: GRN@data$TFs$classification$TF.translation.orig = GRN@annotation$TFs %>%
2048: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]])) {
2049: if (is.null(GRN@data$TFs$classification[[permIndex]])) {
2050: GRN@data$TFs$classification[[permIndex]] = list()
2052: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
2055: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground) \|
2056: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background) \|
2057: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground) \|
2058: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background) \|
2059: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor) \|
2063: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
2086: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = res.l[["median_foreground"]]...(0 bytes skipped)...
2087: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = res.l[["median_background"]]...(0 bytes skipped)...
2088: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = res.l[["foreground"]]
2089: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = res.l[["background"]]
2091: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor = TF_peak_cor
2094: # Final classification: Calculate thresholds by calculating the quantiles of the background and compare the real ...(24 bytes skipped)...
2096: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l) \| forceRerun) {
2097: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l =
2098: .calculate_classificationThresholds(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
2102: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification) \| forceRerun) {
2104: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification =
2106: output.global.TFs = GRN@data$TFs$classification$TF.translation.orig %>% dplyr::mutate(TF = .data$TF.name),
2107: median.cor.tfs = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground,
2108: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
2116: # PLOTS FOR THE RNA-SEQ CLASSIFICATION
2127: .plot_density(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground),
2128: .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
2138: median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground),
2139: median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background),
2141: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
2151: TF_peak_cor = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor
2153: .plot_heatmapAR(TF.peakMatrix.df = peak_TF_overlapCur.df,
2158: median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground),
2159: median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background),
2160: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
2161: finalClassification = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification,
2171: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = NULL
2172: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = NULL
2173: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = NULL
2174: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = NULL
2175: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l = NULL
2179: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground =
2181: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background =
2244: checkmate::assertClass(GRN, "GRN")
2459: minPerc = .findMaxBackgroundSize(GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage = threshold_percentage)
2503: for (i in seq_len(nrow(GC_classes_foreground.df))) {
2511: #Select the minimum, which for low % classes is smaller than the required number to mimic the foreground 100%
2513: nPeaksCur = GC_classes_all.df$n.bg.needed[i]
2515: nPeaksCur = min(GC_classes_all.df$n.bg.needed[i], nrow(peaksBackgroundGCCur))
2518: if (GC_classes_all.df$n.bg.needed[i] > nrow(peaksBackgroundGCCur)) {
2533: plots_GC.l[[TFCur]] = .generateTF_GC_diagnosticPlots(TFCur, GC_classes_foreground.df, GC_classes_background.df, GC_classes_all.df, peaksForeground, peaksBackground, peaksBackgroundGC)
2719: .findMaxBackgroundSize <- function (GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage = 0.05) {
2736: for (i in seq_len(nrow(GC_classes_foreground.df))) {
2738: n_rel = GC_classes_foreground.df$n_rel[i]
2743: availableNoPeaks = GC_classes_background.df %>%
2755: if (i == nrow(GC_classes_foreground.df)) {
2761: } # end of for (i in 1:nrow(GC_classes_foreground.df)) {
2807: checkmate::assertClass(GRN, "GRN")
3406: checkmate::assertClass(GRN, "GRN")
4004: checkmate::assertClass(GRN, "GRN")
4284: checkmate::assertClass(GRN, "GRN")
4628: ...(10 bytes skipped)...includeIDColumn Logical. \code{TRUE} or \code{FALSE}. Default \code{TRUE}. Only relevant if \code{asMatrix = FALSE}. If set to \code{TRUE}, an explicit ID column is returned (no row names). If set to \code{...(45 bytes skipped)...
4638: getCounts <- function(GRN, type, permuted = FALSE, asMatrix = FALSE, includeIDColumn = TRUE, includeFiltered = FALSE) {
4640: checkmate::assertClass(GRN, "GRN")
4660: } else if ("dgCMatrix" %in% classPeaks) {
4673: } else if ("dgCMatrix" %in% classRNA) {
4705: if (!asMatrix) {
4758: checkmate::assertClass(GRN, "GRN")
4882: ...(10 bytes skipped)...includeIDColumn Logical. \code{TRUE} or \code{FALSE}. Default \code{TRUE}. Only relevant if \code{asMatrix = FALSE}. If set to \code{TRUE}, an explicit ID column is returned (no row names). If set to \code{...(45 bytes skipped)...
4892: getCounts <- function(GRN, type, permuted = FALSE, asMatrix = FALSE, includeIDColumn = TRUE, includeFiltered = FALSE) {
4894: checkmate::assertClass(GRN, "GRN")
4914: } else if ("dgCMatrix" %in% classPeaks) {
4927: } else if ("dgCMatrix" %in% classRNA) {
4959: if (!asMatrix) {
5012: checkmate::assertClass(GRN, "GRN")
5144: checkmate::assertClass(GRN, "GRN")
5180: #' Optional convenience function to delete intermediate data from the function \code{\link{AR_classification_wrapper}} and summary statistics that may occupy a lot of space
5190: checkmate::assertClass(GRN, "GRN")
5198: GRN@data$TFs$classification[[permIndex]]$TF_cor_median_foreground = NULL
5199: GRN@data$TFs$classification[[permIndex]]$TF_cor_median_background = NULL
5200: GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_foreground = NULL
5201: GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_background = NULL
5202: GRN@data$TFs$classification[[permIndex]]$act.rep.thres.l = NULL
5225: checkmate::assertClass(GRN, "GRN")
5289: GRN@data$peaks[["counts"]] = .storeAsMatrixOrSparseMatrix(GRN, df = GRN@data$peaks$counts_norm %>% dplyr::select(-.data$isFiltered),
5309: GRN@data$RNA[["counts"]] = .storeAsMatrixOrSparseMatrix(GRN, df = GRN@data$RNA$counts_norm.l[["0"]] %>% dplyr::select(-.data$isFiltered),
5502: # Some classes might be missing, add them here with explicit zeros
5559: dplyr::mutate(classAll = factor(.data$classAll, levels=c(paste0("real_",range), paste0("random_",range), "TRUE", "FALSE")),
5604: checkmate::assertClass(GRN, "GRN")
5615: expMeans.m = getCounts(GRN, type = "rna", permuted = FALSE, asMatrix = TRUE)
5626: ...(5 bytes skipped)... mean = rowMeans(getCounts(GRN, type = "peaks", permuted = FALSE, asMatrix = TRUE))) %>%
5679: checkmate::assertClass(GRN, "GRN")
89: par.l$internal$allClassificationThresholds = c(0.1, 0.05, 0.01, 0.001)
1746: peak_TF_overlapCur.df = .asMatrixFromSparse(GRN@data$TFs$TF_peak_overlap, convertZero_to_NA = FALSE) %>%
2105: .finalizeClassificationAndAppend(
4661: result = .asMatrixFromSparse(GRN@data$peaks$counts, convertZero_to_NA = FALSE)
4674: result = .asMatrixFromSparse(GRN@data$RNA$counts, convertZero_to_NA = FALSE)
4915: result = .asMatrixFromSparse(GRN@data$peaks$counts, convertZero_to_NA = FALSE)
4928: result = .asMatrixFromSparse(GRN@data$RNA$counts, convertZero_to_NA = FALSE)

BANDITS:R/create_data.R: [ ]
395: Class = rep(classes_tmp, Ngenes)
405: class = do.call(c, Class)
231: cond_1_gene_per_class = ( n == 1 )
316: n_genes_per_class = vapply(genes_in_classes_Together, length, FUN.VALUE = integer(1))
411: class_byGene = split(class, gene)
273: classes_split_per_gene_Unique = split(all_classes_vector[cond_1_gene_per_class_FINAL], genes_names_Unique)
289: classes_Unique = lapply(seq_len(N_genes_Unique), function(x){
328: classes_split_per_gene_Together = split( all_classes_vector_Together,
343: classes_associated_to_GROUPs = list()
382: classes_bigGroup = classes_associated_to_GROUPs[[p]]
392: classes_tmp = classes_split_per_gene_Together[classes_bigGroup]
424: classes_split_bigGroup = lapply(seq_along(class_byGene), function(x){
435: classes_split_bigGroup_num = lapply(classes_split_bigGroup, function(y){
441: classes_split_bigGroup_Unique = lapply(seq_along(classes_split_bigGroup), function(id){
445: classes_split_bigGroup_Unique_num = lapply(seq_along(classes_split_bigGroup_num), function(id){
479: classes_Unique_bigGroup = lapply(seq_len(N_genes_Unique_bigGroup), function(x){
530: classes_ALL_together_per_GROUP = lapply(classes_associated_to_GROUPs, function(x){
246: cond_1_gene_per_class_FINAL = split(df_tmp$More_genes_in_classes, df_tmp$Class_id)
187: all_classes = unique( unlist( lapply(x, function(y){y$class_ids}) ) )
191: all_classes_vector = vapply( all_classes, strsplit, split = sep, fixed = TRUE, FUN.VALUE = list(1) )
195: match_classes = lapply(x, function(u) match(u$class_ids, all_classes) )
212: df_all_classes = data.frame( Class_id=rep(seq_along(n), n), Tr_id = unlist(all_classes_vector) )
218: genes_in_classes = split(df_all_classes$Gene_id, df_all_classes$Class_id)
233: More_genes_in_classes = unique(unlist(genes_in_classes[cond_1_gene_per_class ==FALSE])) # list the genes happearing together in at least 1 class
314: all_classes_vector_Together = all_classes_vector[cond_1_gene_per_class_FINAL==FALSE]
315: genes_in_classes_Together = genes_in_classes[cond_1_gene_per_class_FINAL==FALSE]
324: genes_in_classes_vector_Together = vapply( genes_in_classes_Together, paste, collapse = sep, FUN.VALUE = character(1) )
335: genes_in_classes_split_per_gene_Together = strsplit(names(classes_split_per_gene_Together), split = sep, fixed = TRUE )
7: #' @param gene_to_transcript a matrix or data.frame with a list of gene-to-transcript correspondances.
15: #' @param kallisto_counts (for kallisto input only) a matrix or data.frame,
18: #' The matrix must be unfiltered and the order or rows must be unchanged.
29: #' @return A \code{\linkS4class{BANDITS_data}} object.
83: ...(18 bytes skipped)...\link{eff_len_compute}}, \code{\link{filter_transcripts}}, \code{\link{filter_genes}}, \code{\linkS4class{BANDITS_data}}
104: # check that gene_to_transcript is a matrix or data.frame object
105: if( !is.data.frame(gene_to_transcript) & !is.matrix(gene_to_transcript) ){
106: message("'gene_to_transcript' must be a matrix or data.frame")
111: message("'gene_to_transcript' must be a 2 column matrix or data.frame")
188: # unique( unlist( sapply(x, function(y){y$class_ids}) ) )
194: # match the class id of each sample to the long vector containing all classes ids.
199: # I find the counts of each equiv class in all the samples.
200: all_counts = matrix(0, nrow = length(all_classes), ncol = N)
201: # I set the matrix to 0: in case of no matching (class not present in a sample)
208: # nr of transcripts per class:
215: # all_classes_vector is a list: every element of the list corresponds to a class.
235: # 1 - mean(cond_1_gene_per_class) # mean of equiv classes with tr from > 1 gene.
237: # sum(all_counts[!cond_1_gene_per_class,])/sum(all_counts)
241: # check what classes have at least 1 gene happearing with other genes in at least 1 class:
244: df_tmp = data.frame( Class_id=rep(seq_along(n), n), Gene_id = unlist(genes_in_classes))
247: cond_1_gene_per_class_FINAL = !vapply(cond_1_gene_per_class_FINAL, any, FUN.VALUE = logical(1))
248: # !sapply(cond_1_gene_per_class_FINAL, any)
251: genes_names_Unique = as.character( genes_in_classes[cond_1_gene_per_class_FINAL] )
275: # Collect the counts associated to each class in every gene.
276: counts_split_per_gene_Unique = split(data.frame(all_counts[cond_1_gene_per_class_FINAL,]), genes_names_Unique)
279: # and make a matrix of 0, 1 indicating, for each class, what transcripts they have.
313: all_counts_Together = all_counts[cond_1_gene_per_class_FINAL==FALSE,]
321: # maybe I can use a similar approach to the one used to build the classes from the transcripts, although a 2,000 * 17,000 matrix would be quite big...
396: Class = unname(Class, force = TRUE) # remove names of top lists of Class
397: Class = lapply(Class, unname, force = TRUE) # remove names of each list in Class
406: gene = rep(Gene, vapply(Class, length, FUN.VALUE = integer(1)) )
407: # sapply(Class, length) )
413: genes_bigGroup = names(class_byGene)
425: X = class_byGene[[x]]
471: # and make a matrix of 0, 1 indicating, for each class, what transcripts they have.
510: # Collect the counts associated to each class
513: # Then "unlist" the classes and counts of each group: turn them into a single class and cont matrix.
540: # and make a matrix of 0, 1 indicating, for each class, what transcripts they have.
546: # and then each classes_ALL_together_per_GROUP[[i]][[j]][[1]] has a list per equiv class
577: df_eff_len_Unique = data.frame( Class_id=rep(seq_along(n), n), Tr_id = unlist(Transcripts_per_GROUP))
580: eff_len_tr_Together = split(df_eff_len_Unique$eff_len, df_eff_len_Unique$Class_id)
585: # For each Group, I match (on the truth matrix), the tr_id with the gene_to_transcript
590: # use the gene_to_transcript matrix
594: df_tmp = data.frame( Class_id=rep(seq_along(n), n), Tr_id = unlist(Transcripts_per_GROUP))
597: genes_per_GROUP = split(df_tmp$Gene_id, df_tmp$Class_id)
672: # Make a class out of the results:
3: #' \code{create_data} imports the equivalence classes and create a 'BANDITS_data' object.
9: #' @param salmon_path_to_eq_classes (for salmon input only) a vector of length equals to the number of samples:
10: #' each element indicates the path to the equivalence classes of the respective sample (computed by salmon).
11: #' @param kallisto_equiv_classes (for kallisto input only) a vector of length equals to the number of samples:
12: #' each element indicates the path to the equivalence classes ('.ec' files) of the respective sample (computed by kallisto).
14: #' each element indicates the path to the counts of the equivalence classes ('.tsv' files) of the respective sample (computed by kallisto).
26: #' When equivalence classes contain transcripts from distinct genes, these genes are analyzed together.
56: #' # specify the path to the equivalence classes:
57: #' equiv_classes_files = file.path(data_dir, "STAR-salmon", sample_names, "aux_info", "eq_classes.txt")
62: #' salmon_path_to_eq_classes = equiv_classes_files,
69: #' kallisto_equiv_classes = file.path(data_dir, "kallisto", sample_names, "pseudoalignments.ec")
74: #' kallisto_equiv_classes = kallisto_equiv_classes,
88: salmon_path_to_eq_classes = NULL,
89: kallisto_equiv_classes = NULL,
127: n_cores = ifelse(salmon_or_kallisto == "salmon", length(salmon_path_to_eq_classes), length(kallisto_equiv_classes))
162: if(salmon_or_kallisto == "salmon"){ # load salmon equivalence classes:
164: path_to_eq_classes = salmon_path_to_eq_classes,
167: }else{ # load kallisto equivalence classes:
169: kallisto_equiv_classes = kallisto_equiv_classes,
186: # merge the ids of all classes here:
192: # sapply( all_classes, strsplit, split = sep, fixed = TRUE )
196: # match_classes is a list, every element of the list refers to a sample.
197: # match_classes may now have some NAs.
203: match_NA = is.na(match_classes[[i]])
204: all_counts[match_classes[[i]][match_NA==FALSE],i] = x[[i]]$counts[match_NA==FALSE]
205: # here I filter the classes which were filtered out when filtering the transcripts.
209: n = vapply(all_classes_vector, length, FUN.VALUE = integer(1))
210: # sapply(all_classes_vector, length)
211: # create a data.frame structure keeping the info of what transcritps belong to what classes:
213: rownames(df_all_classes) = c()
214: # then match equivalence classes to their genes (see readDGE)
216: df_all_classes$Gene_id = Gene_id[match(df_all_classes$Tr_id, Tr_id)]
219: genes_in_classes = lapply(genes_in_classes, unique)
224: genes_SELECTED = unique( df_all_classes$Gene_id ) # All genes
227: n = vapply(genes_in_classes, length, FUN.VALUE = integer(1))
228: # sapply(genes_in_classes, length)
230: ## I need to consider classes with 1 gene only...easy: "cond = sapply(genes_in_classes, length) == 1"
232: # sapply(genes_in_class...(7 bytes skipped)...gth) == 1 # But I also need to make sure that the genes respecting "cond" does not happear in other classes!
238: # mean counts from these classes
242: # in other words, check whether it's on the "More_genes_in_classes" list or not.
245: df_tmp$More_genes_in_classes = df_tmp$Gene_id %in% More_genes_in_classes
259: genes_SELECTED_Together = unique( df_all_classes$Gene_id[df_all_classes$Gene_id %in% genes_SELECTED_Unique ==FALSE] );
272: # Collect, for each gene, the classes associated to it.
280: Transcripts_per_gene_Unique = lapply(classes_split_per_gene_Unique, function(x){ unique(unlist(x)) })
286: #Error in classes_split_per_gene_Unique[[x]] : subscript out of bounds
287: # length(classes_split_per_gene_Unique) == length(counts_split_per_gene_Unique) # TRUE
290: m = vapply(classes_split_per_gene_Unique[[x]], function(y){ Transcripts_per_gene_Unique[[x]] %in% y }, FUN.VALUE = l...(53 bytes skipped)...
291: # sapply(classes_split_per_gene_Unique[[x]], function(y){ Transcripts_per_gene_Unique[[x]] %in% y })
317: # sapply(genes_in_classes_Together, length)
319: # First, I need to separate classes corresponding to 1 gene only to classes corresponding to >1 gene...DONE
325: # sapply( genes_in_classes_Together, paste, collapse = sep )
326: names( genes_in_classes_vector_Together ) = genes_in_classes_vector_Together
329: genes_in_classes_vector_Together )
332: genes_in_classes_vector_Together )
334: # I look for what classes each gene appers in and record whether it happears uniquely or not.
336: n_genes = vapply(genes_in_classes_split_per_gene_Together, length, FUN.VALUE = integer(1))
337: # sapply(genes_in_classes_split_per_gene_Together, length)
340: # I make group of genes to be modelled together and make a correspondance with the classes in classes_split_per_gene_Together
350: classes_associated_to_GROUPs[[g_id]] = which( vapply(genes_in_classes_split_per_gene_Together, function(x){ genes_SELECTED_Together[i] %in% x}, FUN.VALUE = logical(1))...(2 bytes skipped)...
351: # which( sapply(genes_in_classes_split_per_gene_Together, function(x){ genes_SELECTED_Together[i] %in% x}) )
352: GROUPs_of_genes[[g_id]] = unique( unlist(genes_in_classes_split_per_gene_Together[classes_associated_to_GROUPs[[g_id]]]) ) # Genes associated to i-th gene
358: classes_associated_to_GROUPs[[g_id]] = unique( c(classes_associated_to_GROUPs[[g_id]],
359: which( vapply(genes_in_classes_split_per_gene_Together,
362: unlist(genes_in_classes_split_per_gene_Together[classes_associated_to_GROUPs[[g_id]]]) ) )
384: # Split genes and define their classes:
386: classes_split_per_gene_Together = split( all_classes_vector_Together,
387: genes_in_classes_vector_Together )
389: genes_in_classes_vector_Together )
390: Gene = unlist(genes_in_classes_split_per_gene_Together[classes_bigGroup])
393: Ngenes = vapply(genes_in_classes_split_per_gene_Together[classes_bigGroup], length, FUN.VALUE = integer(1))
394: # sapply(genes_in_classes_split_per_gene_Together[classes_bigGroup], length)
400: Counts = rep(counts_split_per_gene_Together[classes_bigGroup], vapply(genes_in_classes_split_per_gene_Together[classes_bigGroup], length, FUN.VALUE = integer(1)))
401: # sapply(genes_in_classes_split_per_gene_Together[classes_bigGroup], length))
410: # Split counts and classes by their gene name.
432: # Merge Identical classes and add up corresponding counts:
434: # put transcript names of classes together as tr1_tr2
440: DUPS = lapply(classes_split_bigGroup_num, duplicated)
442: classes_split_bigGroup[[id]][ DUPS[[id]] == FALSE ]
446: classes_split_bigGroup_num[[id]][ DUPS[[id]] == FALSE ]
449: # Select the counts for the unique classes:
454: # Select the counts for the duplicated classes:
462: match_dups = classes_split_bigGroup_num[[id]][DUPS[[id]]] == classes_split_bigGroup_Unique_num[[id]][i] # I look for the matching between the unique classes and duplicatd ones (eliminated)
464: ...(26 bytes skipped)...+ colSums(counts_byGene_DUPS[[id]][match_dups,]) # I add the counts of the corresponding duplicated classes
473: Transcripts_per_gene_bigGroup = lapply(classes_split_bigGroup_Unique, function(x){ unique(unlist(x)) })
477: N_genes_Unique_bigGroup = length(classes_split_bigGroup_Unique);
480: m = vapply(classes_split_bigGroup_Unique[[x]], function(y){ Transcripts_per_gene_bigGroup[[x]] %in% y }, FUN.VALUE =...(57 bytes skipped)...
481: # sapply(classes_split_bigGroup_Unique[[x]], function(y){ Transcripts_per_gene_bigGroup[[x]] %in% y })
484: # TO DO: REMOVE CLASSES from transcripts which are not present!
485: # AND merge classes which are identical.
502: classes_Unique = c( classes_Unique, classes_Unique_bigGroup)
506: classes_associated_to_GROUPs = classes_associated_to_GROUPs[-bigGroup]
512: # I now need to select the classes and counts associated to each "group" of genes
514: counts_ALL_together_per_GROUP = lapply(classes_associated_to_GROUPs, function(x){
517: #counts_ALL_together_per_GROUP = lapply(classes_associated_to_GROUPs, function(x){
533: res[[i]] = classes_split_per_gene_Together[x[i]]
541: Transcripts_per_GROUP = lapply(classes_ALL_together_per_GROUP, function(x){ unique(unlist(x)) })
545: # since classes_ALL_together_per_GROUP[[i]] has a list per gene,
547: # I need to unlist manually classes_ALL_together_per_GROUP
549: N_GROUPS = length(classes_ALL_together_per_GROUP)
550: classes_ALL_together_per_GROUP_unlisted = list()
552: classes_ALL_together_per_GROUP_unlisted[[i]] = list(classes_ALL_together_per_GROUP[[i]][[1]][[1]][[1]])
553: J = length(classes_ALL_together_per_GROUP[[i]])
555: K = length(classes_ALL_together_per_GROUP[[i]][[j]][[1]])
558: classes_ALL_together_per_GROUP_unlisted[[i]] = c(classes_ALL_together_per_GROUP_unlisted[[i]],
559: list(classes_ALL_together_per_GROUP[[i]][[j]][[1]][[k]]))
565: # From the transcripts, I create the classes
566: classes_Together = lapply(seq_along(classes_ALL_together_per_GROUP_unlisted), function(x){
567: # m = sapply(classes_ALL_together_per_GROUP_unlisted[[x]], function(y){ Transcripts_per_GROUP[[x]] %in% unlist(y) })
568: m = vapply(classes_ALL_together_per_GROUP_unlisted[[x]], function(y){ Transcripts_per_GROUP[[x]] %in% unlist(y) }, F...(59 bytes skipped)...
615: classes_Unique = classes_Unique[SEL_tr]
633: classes_Together = classes_Together[SEL_tr]
637: # automatically check the coherence of the matrixed in UNIQUE:
641: K = length(Transcripts_per_gene_Unique[[i]]); J = ncol(data.frame(classes_Unique[[i]]))
643: {length(eff_len_tr_Unique[[i]]) == K} & {nrow(data.frame(classes_Unique[[i]])) == K} & {nrow(counts_split_per_gene_Unique[[i]]) == J} & {ncol(counts_split_per_ge...(22 bytes skipped)...
647: message("something wrong in Unique classes")
651: # automatically check the coherence of the matrixed in TOGETHER:
656: K = nrow(classes_Together[[i]]); J = ncol(classes_Together[[i]])
662: message("something wrong in Together classes")
682: classes = c(classes_Unique, classes_Together),

Modstrings:R/Modstrings-separate.R: [ ]
844: class <- paste0(class(x),"Set")
602: .pos_to_logical_matrix <- function(x, at)
53: qualities <- matrix(unlist(qualities),nrow = nlength,byrow = TRUE)
54: f_qualities <- matrix(unlist(lapply(coverage(split(ranges,f_names)),
578: # convert the position information into a logical list or matrix
605: m <- matrix(rep(FALSE,sum(width)),length(x))
622: m <- matrix(unlist(m),length(m),byrow = TRUE)
694: at <- .pos_to_logical_matrix(as(x, paste0(seqtype(x), "StringSet")),
730: at <- .pos_to_logical_matrix(as(x, paste0(seqtype(x), "StringSet"))[f],
775: at <- .pos_to_logical_matrix(as(x, paste0(seqtype(x), "StringSet"))[f],
835: modValues <- .norm_seqtype_modtype(unlist(mod), seqtype, "short", class(x))
845: current_letter <- as(do.call(class, list(current_letter)),
846: gsub("Mod","",class))
864: at <- .pos_to_logical_matrix(as(x, paste0(seqtype(x), "StringSet")),

SeqVarTools:R/duplicateDiscordance.R: [ ]
563: class <- .getGenotypeClass(geno)
258: class.map <- c("alt", "het", "ref")
366: class1 <- .getGenotypeClass(dos1)
367: class2 <- .getGenotypeClass(dos2)
480: class1 <- .getGenotypeClass(dos1)
481: class2 <- .getGenotypeClass(dos2)
567: class1 <- class[samp.pairs$sample.id.1,,drop=FALSE]
568: class2 <- class[samp.pairs$sample.id.2,,drop=FALSE]
255: .getGenotypeClass <- function(x){
260: # 0 = alt/alt, 1 = het, 2 = ref/ref, so we can just subset class map by the dosage plus 1
261: tmp <- class.map[x + 1]
264: if (is.matrix(x)) tmp <- matrix(tmp, nrow=nrow(x))
375: n.concordant[i] <- sum(.getMatchesHetHom(class1, class2)[sel])
377: n.concordant[i] <- sum(.getMatchesConc(class1, class2)[sel])
378: n.alt[i] <- sum(.getAlt(class1, class2)[sel])
379: n.alt.conc[i] <- sum(.getMatchesAltConc(class1, class2)[sel])
380: n.het.ref[i] <- sum(.getMatchesHetRef(class1, class2)[sel])
381: n.het.alt[i] <- sum(.getMatchesHetAlt(class1, class2)[sel])
382: n.ref.alt[i] <- sum(.getMatchesRefAlt(class1, class2)[sel])
389: n.concordant <- n.concordant + .getMatchesHetHom(class1, class2)
391: n.concordant <- n.concordant + .getMatchesConc(class1, class2)
392: n.alt <- n.alt + .getAlt(class1, class2)
393: n.alt.conc <- n.alt.conc + .getMatchesAltConc(class1, class2)
394: n.het.ref <- n.het.ref + .getMatchesHetRef(class1, class2)
395: n.het.alt <- n.het.alt + .getMatchesHetAlt(class1, class2)
396: n.ref.alt <- n.ref.alt + .getMatchesRefAlt(class1, class2)
486: n.concordant[i] <- sum(.getMatchesConc(class1, class2)[sel])
487: n.alt[i] <- sum(.getAlt(class1, class2)[sel])
488: n.alt.conc[i] <- sum(.getMatchesAltConc(class1, class2)[sel])
489: n.het.ref[i] <- sum(.getMatchesHetRef(class1, class2)[sel])
490: n.het.alt[i] <- sum(.getMatchesHetAlt(class1, class2)[sel])
491: n.ref.alt[i] <- sum(.getMatchesRefAlt(class1, class2)[sel])
494: n.concordant <- n.concordant + .getMatchesConc(class1, class2)
495: n.alt <- n.alt + .getAlt(class1, class2)
496: n.alt.conc <- n.alt.conc + .getMatchesAltConc(class1, class2)
497: n.het.ref <- n.het.ref + .getMatchesHetRef(class1, class2)
498: n.het.alt <- n.het.alt + .getMatchesHetAlt(class1, class2)
499: n.ref.alt <- n.ref.alt + .getMatchesRefAlt(class1, class2)
564: rownames(class) <- sample.id
572: n.variants = rowSums(.getNonMissing(class1, class2)),
573: n.concordant = rowSums(.getMatchesConc(class1, class2)),
574: n.alt = rowSums(.getAlt(class1, class2)),
575: n.alt.conc = rowSums(.getMatchesAltConc(class1, class2)),
576: n.het.ref = rowSums(.getMatchesHetRef(class1, class2)),
577: n.het.alt = rowSums(.getMatchesHetAlt(class1, class2)),
578: n.ref.alt = rowSums(.getMatchesRefAlt(class1, class2))
583: n.samples = colSums(.getNonMissing(class1, class2)),
584: n.concordant = colSums(.getMatchesConc(class1, class2)),
585: n.alt = colSums(.getAlt(class1, class2)),
586: n.alt.conc = colSums(.getMatchesAltConc(class1, class2)),
587: n.het.ref = colSums(.getMatchesHetRef(class1, class2)),
588: n.het.alt = colSums(.getMatchesHetAlt(class1, class2)),
589: n.ref.alt = colSums(.getMatchesRefAlt(class1, class2))
123: # definition for the signature with two SeqVarGDSClass objects
257: # map for genotype classes

ISAnalytics:R/internal-functions.R: [ ]
98: rlang::warn(warn, class = "missing_crit_tags")
218: ), class = "missing_req_col_err")
249: ), class = "missing_req_col_err")
434: rlang::inform(warn_empty_iss, class = "warn_empty_iss")
542: class = "warn_empty_iss"
604: rlang::abort(error_msg(tags_names), class = "missing_tags_err")
712: rlang::abort(single_err, class = "tag_dupl_err")
777: rlang::abort(compact_msg, class = "tag_type_err")
1128: rlang::abort(add_types_err, class = "add_types_err")
1299: class = "xls_file"
1475: class = "na_concat"
1575: class = "filter_warn"
1948: class = "missing_path_col"
2904: rlang::inform(missing_msg, class = "auto_mode_miss")
2971: matrix <- do.call(.import_single_matrix, args = append(
3164: class = "coll_matrix_issues"
3229: rlang::abort(not_date_err, class = "not_date_coll_err")
4222: rlang::inform(warn, class = "rec_unsupp_ext")
4728: rlang::abort(err_msg, class = "genomic_file_char")
4817: rlang::warn(warn_miss, class = "warn_miss_genes")
5177: class = "missing_cols_key"
5225: rlang::inform(flag_msg, class = "flag_logic_long")
5542: rlang::abort(format_err, class = "outlier_format_err")
173: matrix_type <- names(annotation_suffix[
240: matrix_required_cols <- c(
1100: .import_single_matrix <- function(path,
5584: matrix_desc <- df %>%
5842: sub_matrix <- matrix_desc[, seq(from = t1, to = t2, by = 1)]
5888: sub_matrix <- matrix_desc[, seq(from = t1, to = t2, by = 1)]
5146: KnownGeneClass = ifelse(
1008: colClasses = col_types,
182: matrix_type = matrix_type,
244: if (!all(matrix_required_cols %in% colnames(integration_matrices))) {
246: matrix_required_cols,
247: matrix_required_cols[!matrix_required_cols %in%
375: sparse <- as_sparse_matrix(.x)
379: "fragmentEstimate_matrix.no0.annotated.tsv.gz",
380: "fragmentEstimate_matrix.tsv.gz"
383: "seqCount_matrix.no0.annotated.tsv.gz",
384: "seqCount_matrix.tsv.gz"
463: ### -- Choose a pool for matrix error
473: sparse <- as_sparse_matrix(.x)
477: "fragmentEstimate_matrix.no0.annotated.tsv.gz",
478: "fragmentEstimate_matrix.tsv.gz"
481: "seqCount_matrix.no0.annotated.tsv.gz",
482: "seqCount_matrix.tsv.gz"
608: rlang::abort(error_msg(missing_tags), class = "missing_tags_err")
730: rlang::inform(single_warn, class = "tag_dupl_warn")
753: err <- c(paste("Wrong col class for tag '", sub_df$tag[1], "'"),
899: # @param x A data.frame object (or any extending class)
918: # @param x A data.frame object (or any extending class)
931: # Finds experimental columns in an integration matrix.
934: # standard integration matrix columns, if there are returns their names.
953: # Checks if the integration matrix is annotated or not.
968: #### ---- Internals for matrix import ----####
970: #---- USED IN : import_single_Vispa2Matrix ----
972: # Reads an integration matrix using data.table::fread
1037: # Reads an integration matrix using readr::read_delim
1099: # import_single_Vispa2_matrix
1125: "?import_single_Vispa2Matrix"
1142: class = "unsup_comp_format"
1157: class = "im_single_miss_mand_vars"
1258: class = "ism_import_summary"
1916: matrix_type,
1918: multi_quant_matrix) {
1926: stopifnot(is.logical(multi_quant_matrix) & length(multi_quant_matrix) == 1)
2333: # @param matrix_type The matrix_type to lookup (one between "annotated" or
2359: suffixes <- matrix_file_suffixes() %>%
2361: .data$matrix_type == m_type,
2749: matrix_type,
2757: matrix_type,
2928: rlang::inform(dupl_msg, class = "auto_mode_dupl")
2962: progress, import_matrix_args) {
2965: sample_col_name <- if ("id_col_name" %in% names(import_matrix_args)) {
2966: import_matrix_args[["id_col_name"]]
2978: return(matrix)
2986: arg_list = import_matrix_args,
2994: import_matrix_args,
3038: return(list(matrix = NULL, imported_files = imported_files))
3045: list(matrix = matrices, imported_files = imported_files)
3055: import_matrix_args) {
3086: import_matrix_args
3099: import_matrix_args
3108: imported_matrices <- purrr::map(imported_matrices, ~ .x$matrix)
3110: list(matrix = imported_matrices, summary = summary_files)
3137: ## remove_collisions requires seqCount matrix, check if the list
3167: ## Transform the list in a multi-quant matrix
3171: x <- rlang::exec(comparison_matrix, !!!args)
3238: # Checks if association file contains more information than the matrix.
3241: # the examined matrix there are additional CompleteAmplificationIDs contained
3242: # in the association file that weren't included in the integration matrix (for
3393: # for multi quantification matrix)
3431: # for multi quantification matrix)
3539: # (obtained via `.join_matrix_af`)
3540: # @param after The final matrix obtained after collision processing
3590: # Internal for obtaining summary info about the input sequence count matrix
3617: ## Joined is the matrix already joined with metadata
3674: MATRIX = unique(x[[pcr_col]]),
3896: # - x is a single matrix
3960: # meaning a subset of an integration matrix in which all rows
3968: # @return A named list with recalibrated matrix and recalibration map.
4028: return(list(recalibrated_matrix = x, map = map_recalibr))
4173: return(list(recalibrated_matrix = x, map = map_recalibr))
4799: "the matrix. Here is a summary: "
5195: class = "missing_cols_pool"
5247: class = "flag_logic_short"
5265: rlang::abort(unknown_logi_op_err, class = "unsupp_logi_op")
5545: rlang::abort(format_err, class = "outlier_format_err")
5583: # --- OBTAIN MATRIX (ALL TPs)
5601: as.matrix()
5602: # --- OBTAIN MATRIX (STABLE TPs)
5624: as.matrix()
5629: timecaptures <- length(colnames(matrix_desc))
5636: matrix_desc = matrix_desc,
5650: matrix_desc = patient_slice_stable,
5666: matrix_desc = matrix_desc,
5679: matrix_desc = patient_slice_stable,
5694: estimate_consecutive_m0 <- if (ncol(matrix_desc) > 1) {
5696: matrix_desc = matrix_desc,
5713: estimate_consecutive_mth <- if (stable_tps & ncol(matrix_desc) > 2) {
5714: # - Note: pass the whole matrix, not only stable slice
5716: matrix_desc = matrix_desc,
5746: .closed_m0_est <- function(matrix_desc, timecaptures, cols_estimate_mcm,
5749: models0 <- Rcapture::closedp.0(matrix_desc,
5757: colnames(matrix_desc)[1],
5761: colnames(matrix_desc)[ncol(matrix_desc)],
5791: .closed_mthchaobc_est <- function(matrix_desc, timecaptures, cols_estimate_mcm,
5793: mthchaobc <- Rcapture::closedp.bc(matrix_desc,
5801: colnames(matrix_desc)[1],
5805: colnames(matrix_desc)[ncol(matrix_desc)],
5835: .consecutive_m0bc_est <- function(matrix_desc, cols_estimate_mcm, subject) {
5837: indexes <- seq(from = 1, to = ncol(matrix_desc) - 1, by = 1)
5844: colnames(sub_matrix)[1],
5848: colnames(sub_matrix)[ncol(sub_matrix)],
5851: patient_trend_M0 <- Rcapture::closedp.bc(sub_matrix,
5882: .consecutive_mth_est <- function(matrix_desc, cols_estimate_mcm, subject) {
5883: indexes_s <- seq(from = 1, to = ncol(matrix_desc) - 2, by = 1)
5890: colnames(sub_matrix)[1],
5894: colnames(sub_matrix)[ncol(sub_matrix)],
5897: patient_trend_Mth <- Rcapture::closedp.bc(sub_matrix,
774: "Wrong column classes for some tags",
1041: col_types <- .mandatory_IS_types("classic")
1045: .annotation_IS_types("classic")
1137: ### If not, switch to classic for reading
1138: mode <- "classic"

interactiveDisplay:inst/www/js/d3.v2.js: [ ]
5000: var chord = {}, chords, groups, matrix, n, padding = 0, sortGroups, sortSubgroups, sortChords;
5001: chord.matrix = function(x) {
408: function classedConstant() {
412: function classedFunction() {
2585: var d3_arraySubclass = [].__proto__ ? function(array, prototype) {
3640: d3_selectionPrototype.classed = function(name, value) {
404: function d3_selection_classedRe(name) {
420: function d3_selection_classedName(name) {
2: function d3_class(ctor, properties) {
2595: d3_class(d3_Map, {
2751: d3.transpose = function(matrix) {
2752: return d3.zip.apply(d3, matrix);
3138: return new d3_transform(t ? t.matrix : d3_transformIdentity);
4382: ...(87 bytes skipped)...electAll(".minor").data(subticks, String), subtickEnter = subtick.enter().insert("line", "g").attr("class", "tick minor").style("opacity", 1e-6), subtickExit = d3.transition(subtick.exit()).style("opacity"...(78 bytes skipped)...
4384: ...(31 bytes skipped)...ge(scale), path = g.selectAll(".domain").data([ 0 ]), pathEnter = path.enter().append("path").attr("class", "domain"), pathUpdate = d3.transition(path);
4387: tickEnter.append("line").attr("class", "tick");
4511: bg.enter().append("rect").attr("class", "background").style("visibility", "hidden").style("cursor", "crosshair");
4512: fg.enter().append("rect").attr("class", "extent").style("cursor", "move");
4513: tz.enter().append("g").attr("class", function(d) {
4937: x += matrix[i][j];
4951: return sortSubgroups(matrix[i][a], matrix[i][b]);
4960: var di = groupIndex[i], dj = subgroupIndex[di][j], v = matrix[di][dj], a0 = x, a1 = x += v * k;
5002: if (!arguments.length) return matrix;
5003: n = (matrix = x) && matrix.length;
367: d3_arraySubclass(groups, d3_selectionPrototype);
407: function d3_selection_classed(name, value) {
416: name = name.trim().split(/\s+/).map(d3_selection_classedName);
418: return typeof value === "function" ? classedFunction : classedConstant;
421: var re = d3_selection_classedRe(name);
423: if (c = node.classList) return value ? c.add(name) : c.remove(name);
424: var c = node.className, cb = c.baseVal != null, cv = cb ? c.baseVal : c;
429: if (cb) c.baseVal = cv; else node.className = cv;
433: if (cb) c.baseVal = cv; else node.className = cv;
516: d3_arraySubclass(selection, d3_selection_enterPrototype);
520: d3_arraySubclass(groups, d3_transitionPrototype);
651: point = point.matrixTransform(container.getScreenCTM().inverse());
3644: if (value = node.classList) {
3647: value = node.className;
3649: while (++i < n) if (!d3_selection_classedRe(name[i]).test(value)) return false;
3653: for (value in name) this.each(d3_selection_classed(value, name[value]));
3656: return this.each(d3_selection_classed(name, value));
4638: ...(171 bytes skipped)... !/^(n\|s)$/.test(resizing) && x, resizingY = !/^(e\|w)$/.test(resizing) && y, dragging = eventTarget.classed("extent"), center, origin = mouse(), offset;

geNetClassifier:R/functions.public.R: [ ]
389: class <- names(x)[largest]
71: classes <- factor(apply(prob, 2, function(x) {assignment.conditions(x, minProb, minDiff)}))
97: classes<-unique(c(rownames(mxcf),colnames(mxcf)))
133: byClass <- matrix(nrow=nclasses, ncol=4)
176: externalValidation.probMatrix<- function(queryResult, realLabels, numDecimals=2)
209: probMatrix <- matrix(0,nrow=length(levels(realLabels)), ncol=length(predClasses))
215: classAssignments <- globalQueryResult$class[names(realLabels)[which(realLabels==label)]] #Prob for the class samples, even if the prediction was wrong
249: classes <- rownames(globalQueryResult$probabilities)
289: highestProbClass...(9 bytes skipped)...mes(globalQueryResult$probabilities)[apply(globalQueryResult$probabilities[,which(globalQueryResult$class == "NotAssigned"), drop=FALSE], 2, function(x) which(order(x, decreasing=TRUE)==1))] #Clase con la ...(18 bytes skipped)...
293: nextClass...(17 bytes skipped)...mes(globalQueryResult$probabilities)[apply(globalQueryResult$probabilities[,which(globalQueryResult$class == "NotAssigned"), drop=FALSE], 2, function(x) which(order(x, decreasing=TRUE)==2))]
608: exprMatrix <- eset
695: classes <- levels(sampleLabels)
718: classLabels <- stats::setNames(paste("C", sapply(classes,function(x) which(classes==x)), sep=""), classes)
795: if(!is.null(colnames(genes))) { geneClass <- unique(colnames(genes)[which(genes == genesVector[i],arr.ind=TRUE)[,2]])
815: classMean <- mean(matriz[genesVector[i], (prevLim+1):j])
931: classificationGenesRanking <- classificationGenes
1084: geneClass<-NULL
1156: tempDpMatrix <- discrPwDF
1307: classificationGenesInfo <- genesDetails(classificationGenes)[nwClasses]
1382: classGeneLabels <- as.vector(genesInfoList[[cl]][,"GeneName"])[availableNames]
1395: classGenes <- getNodes(genesNetwork[[cl]])
1412: classificationGenesNetwork <- NULL
1413: classificationGenesID <- NULL
1489: if(length(genesNetwork[[nw]]@nodes)>0) { classGraph <- igraph::graph.data.frame(as.data.frame(genesNetwork[[nw]]@edges[,ntwColnames,drop=FALSE]), ...(68 bytes skipped)...
31: queryGeNetClassifier <- function(classifier, eset, minProbAssignCoeff=1, minDiffAssignCoeff=0.8, verbose=TRUE)
42: numClasses <- length(classifier$levels)
99: nclasses <- length(classes)
105: missingClasses <- classes[which(!classes %in% colnames(mxcf))]
207: predClasses <- c(levels(realLabels), rownames(globalQueryResult$probabilities)[which(!rownames(globalQueryRes...(46 bytes skipped)...
250: numClasses <- length(classes)
337: if(is.null(totalNumberOfClasses)) {numClasses <- length(levels(realLabels))
509: numClasses <- length(gClasses(genesRanking))
697: numClasses <- length(classes)
991: longClassNames <- any(nchar(classNames)>6)
1000: numClasses <- ifelse(is.matrix(classificationGenes), length(classNames), length(classifier$levels))
1269: nwClasses <- names(genesNetwork)
1473: numClasses<-length(genesNetwork)
34: if(is(eset, "ExpressionSet")) eset <- exprs(eset) else if (!is.matrix(eset)) stop("The last argument should be either an expression matrix or an ExpressionSet.")
43: ...(52 bytes skipped)...obAssignCoeff' should be a coefficient to modify the probability required to assign the sample to a class.")
44: ...(72 bytes skipped)...d be a coefficient to modify the required difference between probabilites to assign the sample to a class.")
55: if(sum(!genes %in% colnames(esetTdf))>0) stop("The expression matrix provided does not have the required genes.")
67: ...(86 bytes skipped)...etSelection)) esetSelection <- t(cbind(NULL,esetSelection)) #To avoid error when there is only 1gen/class
72: ret <- list(call=match.call(), class= classes, probabilities=prob)
77: # Calculates stats from the confussion matrix. i.e. sensitivity and specificity of the predictor for each class, global accuracy and call rate (rate of assigned samples)
78: # Receives a confussion matrix (actual(rows) x prediction(cols)) --> Columns and rows in same order. "NotAssigned" last column
79: # 100% Sensitivity = Recognizes all positives for the class
80: # 100% Specificity = Recognizes all negatives for the class
82: #Renombrado de class.accuracy
83: externalValidation.stats <- function(confussionMatrix, numDecimals=2) #Confussion matrix
86: if(!is.matrix(mxcf))stop("The argument should be a confussion matrix.")
93: warning("The confussion matrix should have the real class in rows and the assigned class in cols. The matrix provided didn't seem to be in the right order so it was transposed:")
106: mxcf <- cbind(mxcf, matrix(ncol=length(missingClasses), nrow=dim(mxcf)[1], data=0))
114: mxcf <- rbind(mxcf, matrix(nrow=length(missingClasses), ncol=dim(mxcf)[2], data=0))
126: warning("There were missing columns or rows in the confussion matrix, empty ones were added.", immediate.=TRUE)
174: # Returns the matrix with the average probabilities of assigning a sample to each class (only of assigned samples)
186: if (length(realLabels) == length( names(queryResult$class)))
188: names(realLabels) <- names(queryResult$class)
201: if(sum(!names(globalQueryResult$class) %in% names(realLabels)) >0) stop("There are samples for which the real label was not provided.")
203: if((length(globalQueryResult$class)!=dim(globalQueryResult$probabilities)[2]) \|\| (sum(!names(globalQueryResult$class) %in% colnames(globalQueryResult$probabilities))>0 )) {stop("The samples in $class and in $probabilities do not match.")}
228: # Gives basic stats of the probabilities with wich the samples were assigned to the class
246: if(length(globalQueryResult$class)!=dim(globalQueryResult$probabilities)[2]) {}#El numero de samples no encaja
248: numSamples <- length(globalQueryResult$class)
262: if (globalQueryResult$class[i] == classes[c])
270: else if (c==1 && (globalQueryResult$class[i] == "NotAssigned")) notAssigned <- notAssigned+1
284: # Info about NotAssigned samples (most likely class & probs)
288: highestProb <- apply(globalQueryResult$probabilities[,which(globalQueryResult$class == "NotAssigned"), drop=FALSE], 2, function(x) round(max(x), numDecimals)) #--> mayor probabilid...(13 bytes skipped)...
291: nextProbIndex <- cbind(apply(globalQueryResult$probabilities[,which(globalQueryResult$class...(10 bytes skipped)...signed"), drop=FALSE], 2, function(x) which(order(x, decreasing=TRUE)==2)), which(globalQueryResult$class == "NotAssigned"))
302: ...(19 bytes skipped)...("The query contains ", samplesQueried=numSamples, " samples. ",sum(stats[,1])," were assigned to a class resulting on a call rate of ", callRate,"%. \n", sep=""))
331: if (length(realLabels) == length(names(queryResult$class)))
333: names(realLabels) <- names(queryResult$class)
342: ...(72 bytes skipped)...d be a coefficient to modify the required difference between probabilites to assign the sample to a class.")
363: plot(c(minX,1), c(0,1), type="n", xlab="Probability of the most likely class", ylab="Difference with next class", frame=FALSE, main="Thresholds to assign query samples")
380: ...(0 bytes skipped)... graphics::legend("bottomright", "(x,y)", legend=c("Correct", "Incorrect"), title = "Most likely class", text.width = strwidth("1,000,000"), xjust = 1, yjust = 1, lty = 0, pch=16, col=c(correctColor, i...(24 bytes skipped)...
393: class <- "NA"
395: return(c(biggestProb=x[largest], nextProb=nextProb, assignedClass=class))
457: if(is.data.frame(sampleLabels) \|\| is.matrix(sampleLabels))
466: if(class(sampleLabels) != "factor") {
475: if(is(eset, "ExpressionSet")) eset <- exprs(eset) else if (!is.matrix(eset)) stop("The argument 'eset' should be an expression matrix or an ExpressionSet.")
480: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(27 bytes skipped)...
528: postProb <- matrix(nrow=numGenesPlot, ncol=ncol(ord))
589: if(is.data.frame(sampleLabels) \|\| is.matrix(sampleLabels))
599: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(27 bytes skipped)...
610: if(is(exprMatrix, "ExpressionSet")) exprMatrix <- exprs(exprMatrix) else if (!is.matrix(exprMatrix)) stop("The first argument should be an expression matrix or an ExpressionSet.")
628: if(!is.matrix(genes) && !is.vector(genes)) stop ("The genes list should be either a vector or a matrix.")
629: if(sum(!genes[which(genes!="NA")] %in% rownames(exprMatrix))!=0) stop ("The expression matrix doesn't contain all the genes.")
638: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(4 bytes skipped)...
671: # Default class colors for boxplot
719: warning(paste("Some class names are longer than 10 characters. The following labels will be used in plots:\n",paste(classLabels, names(classLabels), sep=": ", collapse="\n"), sep=""))
731: if( numClasses == 0 \|\| !is.matrix(genes) ) {
746: geneTitles <- matrix(ncol=3, dimnames=list(genesVector,c("class","label","labelShort")),nrow=length(genesVector))
749: # Class
752: # Gene class, from genes table
753: geneTitles[g,"class"] <- unique(colnames(genes)[which(genes == g,arr.ind=TRUE)[,2]])
754: if(nchar(geneTitles[g,"class"])>10) geneTitles[g,"class"] <- paste(geneTitles[g,"class"], " (C",which(classes==geneTitles[g,"class"]), ")", sep="")
757: geneTitles[g,"class"]<-""
799: title(paste(geneTitles[genesVector[i],"class"], geneTitles[genesVector[i],"label"], sep="\n" ))
811: if (any(nchar(classes)>10) ) {graphics::text(prevLim+((j-prevLim)/2), ylim[2]-(ylim[2]0.04), labels=classLabels[sampleLabels[j]]) # Class* title
812: ...(52 bytes skipped)...evLim+((j-prevLim)/2)+0.5, y=ylim[2]-(ylim[2]0.04), labels=paste(sampleLabels[j],sep=""), pos=3) # Class* title
845: title(paste(geneTitles[gen,"class"], geneTitles[gen,"label"], sep="\n" ))
926: if(any(class(classificationGenes) == "GenesRanking"))
947: m <- matrix(ncol=length(missingGenes))
961: if(is.matrix(geneLabels) \|\| is.data.frame(geneLabels))
965: stop("geneLabels should be a named vector or a one dimensional matrix.")
988: warning(paste("The number of classes provided don't match the classifier's. The default class names will be used instead.",sep=""), immediate. = TRUE) }
1002: if(!is.matrix(classificationGenes)) { #if(verbose) warning("The 'classification genes' are not sorted by colums and classes, the gene class will not be shown .")
1010: classificationGenes <- classificationGenes[,apply(classificationGenes, 2, function(x) !all(is.na(x)))] # Is there any class without genes?
1021: if(is.matrix(classificationGenes)) # If it contains the genes by classes (columns)
1032: classificationGenes <- as.matrix(classificationGenes)
1170: # Merge the geneDetails(genesRanking) with the discriminant power into one matrix
1259: warning(paste("Plotting up to ", max(numGenes(genesRanking)), " genes of each class.", sep=""))
1272: if(!class(genesNetwork) == "GenesNetwork") stop("genesNetwork should be either a list or a GenesNetwork.")
1273: if((sum(c("class1", "class2") %in% colnames(genesNetwork@edges)) == 2 ) && nrow(genesNetwork@edges)>0)
1275: nwClasses <- unique(as.vector(genesNetwork@edges[,c("class1", "class2")]))
1285: if(!is.matrix(genesNetwork[[cl]]@edges)) stop("genesNetwork should be either a GenesNetwork or a list of GenesNet...(7 bytes skipped)...
1290: if(is.matrix(classificationGenes) && nrow(classificationGenes)==0) classificationGenes <- NULL
1291: if(any(class(classificationGenes) == "GenesRanking") && all(numGenes(classificationGenes) == 0)) classificationGenes <- NULL
1298: if(class(genesRanking) != "GenesRanking") stop("genesRanking should be an object of type GenesRanking.")
1305: if(class(classificationGenes)[1] != "GenesRanking") stop("classificationGenes should be an object of type GenesRanking (the classificationGenes object returned by the classifier).")
1323: temp <- cbind(genesInfo[[cl]], matrix(nrow=nrow(genesInfo[[cl]]),ncol=length(missingColumnsInGlobal)))
1345: if(any(! names(genesNetwork) %in% names(genesInfo))) { stop("The class names in genesInfo and genesNetwork do not match.")
1398: temp <- matrix(NA, nrow=length(missingGenes), ncol=length(colnames(genesInfoList[[cl]])))
1481: # For each class...
1522: vertexColors[which(exprsDiff>0)] <- reds[apply( matrix(data=sapply((max(exprsDiff+0.001,na.rm=TRUE)/length(reds)) * 1:length(reds), function(x){ exprsDiff...(120 bytes skipped)...
1523: vertexColors[which(exprsDiff<0)] <- greens[apply(matrix(data=sapply((min(exprsDiff-0.001,na.rm=TRUE)/length(greens))*1:length(greens), function(x){exprsDif...(101 bytes skipped)...
4: # clasificador (in file: classifier.main.r)
7: # externalValidation.probMatrix
24: # classifier:
30: # WARNING!: The arrays should have been normalized with the samples used for the classifier training.
36: if(is(classifier, "GeNetClassifierReturn")){
37: if("classifier" %in% names(classifier)) { classifier <- classifier@classifier$SVMclassifier
38: }else stop("'classifier' doesn't contain a trained classifier.")
40: if(!is(classifier, "svm")) classifier <- classifier$SVMclassifier
41: if(!is(classifier, "svm")) stop("The first argument should be the classifier returned by geNetClassifier.")
45: if(minProbAssignCoeff<0 \|\| ((numClasses != 2) &&(minProbAssignCoeff>(numClasses/2)))) stop("'minProbAssignCoeff' should be between 0 and half of the number of classes.")
46: if(minDiffAssignCoeff<0 \|\| minDiffAssignCoeff>numClasses) stop("'minDiffAssignCoeff' should be between 0 and the number of classes.")
47: genes <- colnames(classifier$SV)
59: rand <- 1/length(classifier$levels)
60: if(length(classifier$levels)>2) { minProb <- 2rand minProbAssignCoeff
63: ...(147 bytes skipped)..., "(default)",""),".\n Minimum difference between the probabilities of first and second most likely classes = ", round(minDiff,2),ifelse(minDiffAssignCoeff==1, "(default)",""), sep="")) ; utils::flush.co...(8 bytes skipped)...
68: prob <- t(attributes(stats::predict(classifier, esetSelection, probability=TRUE ))$probabilities)
69: if(is.null(names(prob))) colnames(prob)<-rownames(esetTdf) # if 2 classes... not labeled. Needed for mxcf...
85: mxcf <- confussionMatrix
98: if(any(classes=="NotAssigned")) classes <- classes[-which(classes=="NotAssigned")]
103: if (any(!classes %in% colnames(mxcf)))
111: if (any(!classes %in% rownames(mxcf)))
113: missingClasses <- classes[which(!classes %in% rownames(mxcf))]
120: #Just in case they are not in order (Diagonal=hits). Will use only the real classes.
121: mxcf<- mxcf[,c(classes,"NotAssigned")] ...(22 bytes skipped)...
122: mxcf<- mxcf[c(classes),]
134: rownames(byClass)<- classes
135: colnames(byClass)<-c("Sensitivity","Specificity", "MCC", "CallRate")
159: byClass[i,1] <- round(100*(truePositives[i]/(truePositives[i]+falseNegatives[i])) ,numDecimals)
161: byClass[i,2] <- round(100*(trueNegatives[i]/(trueNegatives[i]+falsePositives[i])) ,numDecimals)
163: byClass[i,3] <- round(100( ((truePositives[i]trueNegatives[i])-(falsePositives[i]*falseNegatives[i])) / ...(175 bytes skipped)...
165: if(i <= dim(mxcf)[1]) byClass[i,4] <- round(100*( (sum(mxcf[i,])- mxcf[i,dim(mxcf)[2]])/sum(mxcf[i,])) ,numDecimals)
171: return( list(byClass=byClass, global=global, confMatrix=mxcf) )
210: rownames(probMatrix)<- levels(realLabels)
211: colnames(probMatrix)<- predClasses
216: assignedSamples <- names(classAssignments)[ which( classAssignments!= "NotAssigned")]
219: if(length(assignedSamples)>1) probMatrix...(8 bytes skipped)... <- apply(globalQueryResult$probabilities[,assignedSamples], 1, function(x) {mean(x)})[colnames(probMatrix)]
220: else probMatrix[label,] <- globalQueryResult$probabilities[,assignedSamples][colnames(probMatrix)]
224: ret<- round(probMatrix,numDecimals)
253: rownames(stats)<-c(classes)
295: notAssignedSamples <- cbind(highestProbClass=highestProbClass, as.data.frame(highestProb), nextProb=nextProb, nextClass=nextClass)
339: if(!is.numeric(totalNumberOfClasses) \|\| (totalNumberOfClasses<length(levels(realLabels)))) stop ("totalNumberOfClasses should be the number of classes for which the classifier was originaly trained.")
343: if(minProbAssignCoeff<0 \|\| ((numClasses != 2) &&(minProbAssignCoeff>(numClasses/2)))) stop("'minProbAssignCoeff' should be between 0 and half of the number of classes.")
344: if(minDiffAssignCoeff<0 \|\| minDiffAssignCoeff>numClasses) stop("'minDiffAssignCoeff' should be between 0 and the number of classes.")
397: rownames(prob) <- c("biggestProb", "nextProb", "assignedClass")
400: correct <- which(prob["assignedClass",] == prob["realLabels",])
401: incorrect <- which(prob["assignedClass",] != prob["realLabels",])
467: #warning("The argument 'classification sampleLabels' had to be converted into a factor.", immediate. = TRUE)
533: numClasses <- ncol(postProb) # If there are only 2 classes, postProb only has 1 column
575: ...(55 bytes skipped)...leName=NULL, geneLabels=NULL, type="lines", sampleLabels=NULL, sampleColors=NULL, labelsOrder=NULL, classColors=NULL, sameScale=TRUE, showSampleNames=FALSE, showMean= FALSE, identify=TRUE, verbose=TRUE)
615: genes <- rownames(exprMatrix)
618: if(is(genes, "GeNetClassifierReturn") && "classificationGenes" %in% names(genes)) {
619: genes <- genes@classificationGenes
620: warning("Plotting expression profiles of the classification genes. To plot other genes, set i.e. genes=...@genesRanking")
632: if(!is.null(geneLabels)) geneLabels<-extractGeneLabels(geneLabels, rownames(exprMatrix[genesVector,]))
635: numSamples <- dim(exprMatrix)[2]
643: if(sum(!names(sampleLabels) %in% colnames(exprMatrix))>0 ) stop("The names of the labels do not match the samples.")
646: names(sampleLabels)<-colnames(exprMatrix)
659: if(!is.null(sampleColors) && !is.null(classColors)) stop("Provide either 'sampleColors' or 'classColors'")
662: if(is.null(classColors))
672: if(is.null(classColors))
674: if(!is.null(sampleLabels)) classColors <- rev(hcl(h=seq(0,360, length.out=length(levels(sampleLabels))+1))[1:length(levels(sampleLab...(7 bytes skipped)...
675: if(is.null(sampleLabels)) classColors <- "white"
680: if(is.null(sampleLabels)) stop("Cannot use 'classColors' if 'sampleLabels' is not provided.")
681: if(length(levels(sampleLabels)) != length(classColors))stop("Length of 'classColors' should match the number of classes in the samples.")
683: if(any(type%in%"lines")) sampleColors <- classColors[sampleLabels]
696: if(!is.null(labelsOrder)) classes <- labelsOrder
702: indexes <- c(indexes, which(sampleLabels==classes[i]))
704: matriz <- exprMatrix[genesVector, indexes, drop=FALSE]
709: classes <- colnames(genes)
710: numClasses <- length(classes)

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