Results for class Matrix

Found 62492 results in 3136 files, showing top 50 files (show more).

ExploreModelMatrix:R/ExploreModelMatrix.R: [ ]
177: class = "fa fa-question-circle",
658: class = "fa fa-question-circle",
55: ExploreModelMatrix <- function(sampleData = NULL, designFormula = NULL) {
199: class = "fa fa-question-circle",
224: class = "fa fa-question-circle",
252: class = "fa fa-question-circle",
283: class = "fa fa-question-circle",
306: class = "fa fa-question-circle",
333: class = "fa fa-question-circle",
355: class = "fa fa-question-circle",
23: #' app <- ExploreModelMatrix(
90: title = paste0("Design matrix visualization (ExploreModelMatrix v",
91: utils::packageVersion("ExploreModelMatrix"), ")"),
612: designMatrix = NULL)
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,
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")
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,

GenomicRanges:R/GPos-class.R: [ ]
189: Class <- sub("IPos$", "GPos", as.character(class(pos)))
395: .COL2CLASS <- c(
400: classinfo <- makeClassinfoRowForCompactPrinting(x, .COL2CLASS)
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",
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)

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
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$class
178: em.normal.partial.concordant <- function(data, class, components) {
184: g <- as.integer(nlevels(as.factor(class)))
190: zx <- .unmap(class[,1], components = components)
191: zy <- .unmap(class[,2], components = components)
219: class = apply(array(results[[3]], dim = c(n,g*g)),
239: classMatrix[diag] <- classVector
241: colnames(classMatrix) <- rownames(x)
246: classMatrix <- matrix(classVector, nrow = featureSize, byrow = FALSE)
248: colnames(classMatrix) <- rownames(y)
253: rownames(classMatrix) <- rownames(x)
261: classMatrix = classMatrix, classVector = classVector,
266: # Internal function to assign class to vector based on number of components and
36: #' \item{classVector}{Vector of classes that have the highest posterior
41: #' molecular feature pair and columns are nine different classes}
48: #' are five components, then there are two more classes for very negative
50: #' combinations for these components are made into classes. If there are three
51: #' components, there are 9 classes. If there are five components, there are 25
52: #' classes.
55: #' the value probMatrix. The value probMatrix is a matrix where each column is a
57: #' discordPPMatrix are the summed differential correlation posterior
115: .assignClass(v2, param2, components))
129: .assignClass(subSamples$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)
244: discordPPMatrix <- matrix(discordPPV, nrow = featureSize,
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))

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",
118: #' @param match Matrix: match between samples and subjects
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)
975: warn <- tags$div(class="alert alert-warning", role="alert",
1220: type <- sapply(cols, function(i) class(df[[i]]))
2064: #' @return A list with class \code{"htest"} containing the following components:
2103: class(rval) <- "htest"
2710: type <- sapply(cols, function(i) class(stats[[i]]))
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)
3: ...(348 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,
792: ...(54 bytes skipped)...atrix of %d rows/genes X %d columns/repeatsfolds, aggregated via '%s' (%s) ...", nrow(df_predictor_class), nfoldnrepeat, aggregateBy, as.character(now)), appendLF=TRUE)
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,
818: df_full <- as.matrix(xSparseMatrix(df_full, verbose=FALSE))

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)
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,
2364: Class.type <- ._Check_numeric
2365: Class.exp <- c("numeric","integer")
10: #' project. At the end, this function will return a S4 object of class
74: #' A value of length 1 of class character or numeric specifying the resolution
138: #' the function will return an object of class BrickContainer.
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")
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 ",
1314: #' Matrix_file <- system.file(file.path("extdata",
1319: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1326: BrickContainer_class_check(Brick)
1367: Matrix.file = matrix_file, delim = delim, Group.path = Group.path,
1411: #' Matrix_file <- system.file(file.path("extdata",
1416: #' chr = "chr2L", resolution = 100000, matrix_file = Matrix_file,
1462: Matrix.file = matrix_file, delim = delim, Group.path = Group.path,
1596: #' Matrix_file <- system.file(file.path("extdata",
1601: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1615: return(Matrix.list[Matrix.list$chr1 == chr1 &
1616: Matrix.list$chr2 == chr2, "done"])
1640: #' Matrix_file <- system.file(file.path("extdata",
1645: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1659: return(Matrix.list[Matrix.list$chr1 == chr1 &
1660: Matrix.list$chr2 == chr2, "sparsity"])
1692: #' Matrix_file <- system.file(file.path("extdata",
1697: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1715: return((Matrix.list[Matrix.list$chr1 == chr1 &
1716: Matrix.list$chr2 == chr2, "distance"]))
1747: #' Matrix_file <- system.file(file.path("extdata",
1752: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1786: #' Matrix_file <- system.file(file.path("extdata",
1791: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1805: Filter <- Matrix.list$chr1 == chr1 & Matrix.list$chr2 == chr2
1806: Extent <- c(Matrix.list[Filter, "min"],Matrix.list[Filter, "max"])
1832: #' Matrix_file <- system.file(file.path("extdata",
1837: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1881: #' Matrix_file <- system.file(file.path("extdata",
1886: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
1900: Filter <- Matrix.list$chr1 == chr1 & Matrix.list$chr2 == chr2
1901: Extent <- Matrix.list[Filter, "filename"]
1947: #' Matrix_file <- system.file(file.path("extdata",
1952: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2098: #' Matrix_file <- system.file(file.path("extdata",
2103: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2136: " found x_coords class ", class(x_coords), " and y_coords class ",
2137: class(y_coords))
2154: stop(chr1," ",chr2," matrix is yet to be loaded into the class.")
2171: return(Matrix)
2211: #' Matrix_file <- system.file(file.path("extdata",
2216: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2245: stop(chr1,chr2," matrix is yet to be loaded into the class.\n")
2258: return(Matrix)
2260: return(FUN(Matrix))
2320: #' Matrix_file <- system.file(file.path("extdata",
2325: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2357: stop("Provided Chromosomes does not appear to be of class character")
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)
2477: #' Matrix_file <- system.file(file.path("extdata",
2482: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2501: stop("Provided Chromosomes does not appear to be of class character")
2539: #' chromosome pair provided an object of class BrickContainer, and values for
2546: #' @return Returns an object of class matrix with dimensions corresponding to
2563: #' Matrix_file <- system.file(file.path("extdata",
2568: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2579: stop("Provided Chromosomes does not appear to be of class character")
2636: #' Matrix_file <- system.file(file.path("extdata",
2641: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2653: BrickContainer_class_check(Brick)
2659: stop("Matrix for this chromsome pair does not exist.\n")
2663: stop("Matrix for this chromsome pair is yet to be loaded.\n")
2716: #' `Brick_export_to_sparse` will accept as input an object of class
2746: #' Matrix_file <- system.file(file.path("extdata",
2751: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2833: #' Matrix_file <- system.file(file.path("extdata",
2838: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2847: BrickContainer_class_check(Brick)
2900: #' Matrix_file <- system.file(file.path("extdata",
2905: #' chr2 = "chr2L", matrix_file = Matrix_file, delim = " ",
2921: BrickContainer_class_check(Brick)
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()

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$levels))
1269: nwClasses <- names(genesNetwork)
1473: numClasses<-length(genesNetwork)
43: ...(52 bytes skipped)...obAssignCoeff' should be a coefficient to modify the probability required to assign the sample to a class.")
44: ...(72 bytes skipped)...d be a coefficient to modify the required difference between probabilites to assign the sample to a class.")
67: ...(86 bytes skipped)...etSelection)) esetSelection <- t(cbind(NULL,esetSelection)) #To avoid error when there is only 1gen/class
72: ret <- list(call=match.call(), class= classes, probabilities=prob)
77: ...(6 bytes skipped)...ulates stats from the confussion matrix. i.e. sensitivity and specificity of the predictor for each class, global accuracy and call rate (rate of assigned samples)
79: # 100% Sensitivity = Recognizes all positives for the class
80: # 100% Specificity = Recognizes all negatives for the class
82: #Renombrado de class.accuracy
93: warning("The confussion matrix should have the real class in rows and the assigned class in cols. The matrix provided didn't seem to be in the right order so it was transposed:")
174: # Returns the matrix with the average probabilities of assigning a sample to each class (only of assigned samples)
186: if (length(realLabels) == length( names(queryResult$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))
466: if(class(sampleLabels) != "factor") {
480: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(27 bytes skipped)...
599: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(27 bytes skipped)...
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=""))
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"))
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?
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")]))
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).")
1345: if(any(! names(genesNetwork) %in% names(genesInfo))) { stop("The class names in genesInfo and genesNetwork do not match.")
1481: # For each class...
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 ))$probabilities)
69: if(is.null(names(prob))) colnames(prob)<-rownames(esetTdf) # if 2 classes... not labeled. Needed for mxcf...
83: externalValidation.stats <- function(confussionMatrix, numDecimals=2) #Confussion matrix
85: mxcf <- confussionMatrix
98: if(any(classes=="NotAssigned")) classes <- classes[-which(classes=="NotAssigned")]
103: if (any(!classes %in% colnames(mxcf)))
111: if (any(!classes %in% rownames(mxcf)))
113: missingClasses <- classes[which(!classes %in% rownames(mxcf))]
120: #Just in case they are not in order (Diagonal=hits). Will use only the real classes.
121: mxcf<- mxcf[,c(classes,"NotAssigned")] ...(22 bytes skipped)...
122: mxcf<- mxcf[c(classes),]
134: rownames(byClass)<- classes
135: colnames(byClass)<-c("Sensitivity","Specificity", "MCC", "CallRate")
159: byClass[i,1] <- round(100*(truePositives[i]/(truePositives[i]+falseNegatives[i])) ,numDecimals)
161: byClass[i,2] <- round(100*(trueNegatives[i]/(trueNegatives[i]+falsePositives[i])) ,numDecimals)
163: byClass[i,3] <- round(100( ((truePositives[i]trueNegatives[i])-(falsePositives[i]*falseNegatives[i])) / ...(175 bytes skipped)...
165: if(i <= dim(mxcf)[1]) byClass[i,4] <- round(100*( (sum(mxcf[i,])- mxcf[i,dim(mxcf)[2]])/sum(mxcf[i,])) ,numDecimals)
171: return( list(byClass=byClass, global=global, confMatrix=mxcf) )
210: rownames(probMatrix)<- levels(realLabels)
211: colnames(probMatrix)<- predClasses
216: assignedSamples <- names(classAssignments)[ which( classAssignments!= "NotAssigned")]
219: if(length(assignedSamples)>1) probMatrix...(8 bytes skipped)... <- apply(globalQueryResult$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)
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.")
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")
629: if(sum(!genes[which(genes!="NA")] %in% rownames(exprMatrix))!=0) stop ("The expression matrix doesn't contain all the genes.")
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)
711: matriz <- exprMatrix[genesVector,, drop=FALSE]
716: if(any(nchar(classes)>10))
796: } else { geneClass<-"" } #classes[which(genes == genesVector[i], arr.ind=TRUE)[2]]
810: ...(21 bytes skipped)... if(!is.na((sampleLabels[j] != sampleLabels[j+1]) ) ) abline(v=j+0.5, col="black") # Separate classes
816: graphics::lines(c(prevLim+1, j), c(classMean, classMean) , col="grey")
834: names(esetXclases) <- classLabels[names(esetXclases)] #paste("C", sapply(names(esetXclases),function(x) which(classes==x)), sep="")
844: ...(12 bytes skipped)... boxplot(Expression~sampleLabel, esetExprSamplesMelted, ylim=ylim, ylab="Expression values", col=classColors, las=2, outpch=16, outcex=0.5)
879: # discriminant.power.plot(classifier, classificationGenes, classNames=c("ALL","AML","CLL","CML","NoLeu"), fileName="test.pdf",correctedAlpha=TRUE)
880: # discriminant.power.plot(classifier, classificationGenes, fileName="test.pdf")
881: # Classification genes: Genes por columnas (nombrecolumna= clase)
882: # discriminant.power.plot(classifier, colnames(classif$SV), fileName="test.pdf")
888: # classifier: puede ser un svm o el objeto devuelto por la funcion principal
889: # classificationGenes: puede ser un c(), una matriz o un GenesRanking
891: plotDiscriminantPower <- function(classifier, classificationGenes=NULL , geneLabels=NULL, classNames=NULL, plotDP = TRUE, fileName= NULL, returnTable=FALSE, verbose=TRUE)
904: # Classifier
905: if(is(classifier, "GeNetClassifierReturn")){
906: if("classificationGenes" %in% names(classifier))
908: if(is.null(classificationGenes))
910: classificationGenes <- classifier@classificationGenes
912: if(is.null(geneLabels) && is.character(classificationGenes))
914: if(length(classifier@classificationGenes@geneLabels) > 0 && any(!is.na(classifier@classificationGenes@geneLabels[classificationGenes])))
915: geneLabels <- classifier@classificationGenes@geneLabels[classificationGenes]
919: if("classifier" %in% names(classifier)) {classifier <- classifier@classifier$SVMclassifier
920: }else stop("'classifier' doesn't contain a trained classifier.")
922: if(is.list(classifier) && ("SVMclassifier" %in% names(classifier))) classifier <- classifier$SVMclassifier
923: if(!is(classifier,"svm")) stop("The first argument should be a svm classifier or the object returned by geNetClassifier.")
925: # ClassificationGenes (GenesRanking)
928: if(sum(numGenes(classificationGenes)))
930: if(is.null(geneLabels) && (length(classificationGenes@geneLabels) > 0 && any(!is.na(classificationGenes@geneLabels)))) geneLabels <- classificationGenes@geneLabels
932: classificationGenes <- getRanking(classificationGenes, showGeneLabels=FALSE, showGeneID=TRUE)$geneID
934: classificationGenes <- NULL
935: classificationGenesRanking<-NULL
938: classificationGenesRanking<-NULL
941: # If classificationGenes is not provided/valid, use the classifier's SV
942: missingGenes <- !as.vector(classificationGenes[!is.na(classificationGenes)]) %in% colnames(classifier$SV)
945: missingGenes <- as.vector(classificationGenes[!is.na(classificationGenes)])[which(missingGenes)]
951: missingGenes <- missingGenes[which(!missingGenes %in% colnames(classifier$SV))]
953: classificationGenes[which(classificationGenes %in% missingGenes)] <- NA
955: if(all(is.na(classificationGenes))) stop("The given 'classificationGenes' are not used by the classifier. Their Discriminant Power cannot be calculated.")
956: if(length(missingGenes)>0) warning(paste("The following classificationGenes are not used by the classifier. Their Discriminant Power cannot be calculated: ", missingGenes, sep=""))
958: if(is.null(classificationGenes)) classificationGenes <- colnames(classifier$SV)
981: ...(35 bytes skipped)...ull(names(geneLabels))) stop("names(geneLabels) can't be empty. It should contain the names used in classification genes.")
982: #if(!is.null(geneLabels) && sum(!names(geneLabels) %in% classificationGenes[which(classificationGenes!="NA")] )>0) warning("Some geneLabels will not be used.")
983: if(!is.null(geneLabels) && sum(!classificationGenes[which(class...(9 bytes skipped)...Genes!="NA")] %in% names(geneLabels))>0) warning("geneLabels doesn't contain the symbol for all the classification genes.")
985: # classNames
986: if(is.null(classNames) \|\| (length(classifier$levels) != length(classNames))){
987: if (!is.null(classNames) && length(classifier$levels) != length(classNames)) {
989: classNames <-classifier$levels
994: for( i in 1:length(classNames)) #Add "C1:..."
996: if (nchar(classNames[i])>10 ) classNames[i] <- paste(substr(classNames[i] ,1,10), "...",sep="")
997: classNames[i] <- paste("C", i, ": ", classNames[i], sep="")
1005: if(length(classifier$levels) == 2) {
1006: if (dim(classificationGenes)[2] != 1) stop("The classes of the classifier and the classification genes provided don't match.")
1008: if(sum(!colnames(classificationGenes) %in% classifier$levels)>0) stop("The classes of the classifier provided and the classification genes don't match.")
1013: nGenes <- length(classificationGenes[which(!is.na(classificationGenes))]) #sum(numGenes(classifier$classificationGenes))
1014: if(nGenes>dim(classifier$SV)[2]){ warning(paste("The given number of genes is bigger than the classifier's.",sep=""), immediate. = TRUE)}
1021: if(is.matrix(classificationGenes)) # If it contains the genes by classes (columns)
1023: for(cl in 1:dim(classificationGenes)[2])
1025: discrPwList <- c(discrPwList, discrPwList=list(sapply(as.character(classificationGenes[which(classificationGenes[,cl]!="NA"),cl]), function(x) SV.dif(classifier, x, correctedAlpha=correctedAlpha))))
1026: names(discrPwList)[cl] <- colnames(classificationGenes)[cl]
1030: classificationGenes <- classificationGenes[which(!is.na(classificationGenes))]
1031: discrPwList <- list(sapply(as.character(classificationGenes), function(x) SV.dif(classifier, x, correctedAlpha=correctedAlpha)))
1032: classificationGenes <- as.matrix(classificationGenes)
1042: numRows <- numGenesPlot/dim(classificationGenes)[2]
1043: while ((numRows < dim(classificationGenes)[1]) && (length(classificationGenes[which(!is.na(classificationGenes[1:numRows,, drop=FALSE]))]) < numGenesPlot))
1048: if(length(classificationGenes[which(!is.na(classificationGenes[1:numRows,, drop=FALSE]))]) <= numGenesPlot)
1050: classificationGenes <- classificationGenes[1:numRows,, drop=FALSE]
1052: classificationGenes <- classificationGenes[1:(numRows-1),, drop=FALSE]
1068: mycols <- colorRampPalette(c("blue","white"))(max(classifier$nSV+2))
1070: for(c in 1:dim(classificationGenes)[2]) #numClasses
1072: for(g in 1:dim(classificationGenes)[1])
1075: gene <- classificationGenes[g,c]
1087: geneClass <- names(gene)
1088: if(nchar(geneClass)>70) geneClass<- substr(geneClass,1,70)
1101: tit<- paste(geneClass,"\n", geneName, "\n", sep="")
1104: ...(31 bytes skipped)...(tit, abs(round(discrPwList[[c]][,gene]$discriminantPower,2)), " (", discrPwList[[c]][,gene]$discrPwClass, ")", sep="")
1125: ...(0 bytes skipped)... barplot(pos,add=TRUE, col=mycols, width=0.9, space=0.1, names.arg=rep("",length(classNames)))
1126: ...(0 bytes skipped)... barplot(neg,add=TRUE, col=mycols, width=0.9, space=0.1, names.arg=rep("",length(classNames)))
1128: if(!correctedAlpha) graphics::text(seq(1, length(classNames), by=1)-0.5, par("usr")[3] - 0.2, labels = classNames, srt = 90, pos = 4, xpd = TRUE)
1129: if(correctedAlpha) graphics::text(seq(1, length(classNames), by=1)-0.5, par("usr")[3], labels = classNames, srt = 90, pos = 4, xpd = TRUE)
1148: discrPwDF<- rbind(discrPwDF, cbind(t(discrPwList[[cl]][c("discriminantPower","discrPwClass"),]), originalClass=rep(names(discrPwList)[cl],dim(discrPwList[[cl]])[2])))
1153: discrPwDF[,"discrPwClass"] <- as.character(discrPwDF[,"discrPwClass"])
1157: tempDpMatrix[,"discriminantPower"] <- abs (tempDpMatrix[,"discriminantPower"] )
1159: for(cl in classifier$levels)
1161: clGenes <- which(tempDpMatrix[,"discrPwClass"]==cl)
1162: discrPwDF <- rbind(discrPwDF, tempDpMatrix[clGenes[order(as.numeric(tempDpMatrix[clGenes,"discriminantPower"]),decreasing=TRUE)],])
1166: if(is.null(classificationGenesRanking))
1171: gDetails<-genesDetails(classificationGenesRanking)
1177: genesDetailsDF <- cbind(discrPwClass=rep(NA,dim(discrPwDF)[1]), discriminantPower=rep(NA,dim(discrPwDF)[1]), genesDetailsDF[rownames(dis...(39 bytes skipped)...
1178: genesDetailsDF[,"discrPwClass"] <- as.character(discrPwDF[,"discrPwClass"])
1187: # plotType="dynamic" (each can be modified), plotType="static" (1 image divided into classes), plotType="pdf"
1189: # genesInfo: Data.frame containing info about the genes. Can be replaced by classificationGenes or genesRanking (recommended).
1190: # If classificationGenes + genesRanking:
1191: # classificationGenes: Tiene q ser un genesRanking
1193: plotNetwork <- function(genesNetwork, class...(96 bytes skipped)...tType="dynamic", fileName=NULL, plotAllNodesNetwork=TRUE, plotOnlyConnectedNodesNetwork=FALSE, plotClassifcationGenesNetwork=FALSE, labelSize=0.5, vertexSize=NULL, width=NULL, height=NULL, verbose=TRUE)
1254: if(is.null(classificationGenes) && ("classificationGenes" %in% names(genesNetwork))) classificationGenes <- genesNetwork@classificationGenes
1257: nGenes <- max( 100, numGenes(genesNetwork@classificationGenes))
1277: }else nwClasses <- "geneClass"
1289: # Check classificationGenes and Genes ranking format and EXTRACT its genes INFO.
1290: if(is.matrix(classificationGenes) && nrow(classificationGenes)==0) classificationGenes <- NULL
1292: if(plotClassifcationGenesNetwork && is.null(classificationGenes)) warning("The classifcation genes network can only be plotted if the classification genes are provided.")
1293: if((!is.null(class...(30 bytes skipped)...nesRanking)) && !is.null(genesInfo)) stop("Please, provide either 'genesInfo' OR a genesRanking and classificationGenes.")
1294: if(!is.null(genesRanking) \|\| !is.null(classificationGenes))
1303: if(!is.null(classificationGenes))
1308: clGenes <- lapply(classificationGenesInfo, rownames)
1310: if(showWarning) warning("Not all the classificationGenes are available in the genesNetwork. They will be represented, but there may be missing...(29 bytes skipped)...
1314: genesInfo <- classificationGenesInfo
1317: missingColumnsInGlobal <- colnames(classificationGenesInfo[[1]])[which(!colnames(classificationGenesInfo[[1]]) %in% colnames(genesInfo[[1]]))]
1328: ...(30 bytes skipped)... if((is.factor(temp[,tempCol]))) levels(temp[,tempCol]) <- unique(c(levels(temp[,tempCol]), levels(classificationGenesInfo[[cl]][,tempCol])))
1331: temp[rownames(classificationGenesInfo[[cl]]), ] <- classificationGenesInfo[[cl]][,colnames(temp)]
1364: ...(143 bytes skipped)...lot, but there may be missing relationships.") # showWarning: The warning was already shown for the classification genes.
1383: names(classGeneLabels) <- rownames(genesInfoList[[cl]])[availableNames]
1384: geneLabels <- c(geneLabels, classGeneLabels)
1396: missingGenes <- classGenes[which(!classGenes %in% rownames(genesInfoList[[cl]]))]
1408: # - Add classification nodes Network
1411: # Extract CLASSIFICATIONgenesNetwork if available/needed & add to list
1414: if(!is.null(classificationGenes) && plotClassifcationGenesNetwork)
1416: classificationGenesID <- getRanking(classificationGenes, showGeneLabels=FALSE, showGeneID=TRUE)$geneID[, nwClasses, drop=FALSE]
1417: classificationGenesNetwork <- getSubNetwork(genesNetwork, classificationGenesID)
1418: names(classificationGenesNetwork) <- paste(names(classificationGenesNetwork), " - Classification Genes",sep="")
1420: clToAdd <- which(sapply(genesNetwork, function(x){length(getNodes(x))}) - sapply(classificationGenesNetwork, function(x){length(getNodes(x))}) != 0)
1423: warning("Only the classification genes network was provided. Only 'AllNodesNetwork' will be plotted.")
1431: genesNetwork <- c(genesNetwork[1:pos], classificationGenesNetwork[clToAdd[i]], genesNetwork[-(1:pos)])
1433: genesInfoList <- c(genesInfoList[1:pos], list(genesInfoList[[i]][classificationGenesID[,clToAdd[i]][!is.na(classificationGenesID[,i])],] ), genesInfoList[-(1:pos)])
1434: names(genesInfoList)[pos+1] <- names(classificationGenesNetwork[clToAdd[i]])
1440: # (Needs to be added after classific. in order to add it right after the "full" network)
1474: if( numClasses>25 ) stop("Too many classes to draw in a single plot. Use 'pdf' instead.")
1485: classGenes <- unique(c(genesNetwork[[nw]]@edges[,"gene1"],genesNetwork[[nw]]@edges[,"gene2"]))
1487: if((is.null(genesInfoList) \|\| nrow(genesInfoList[[nw]])==0 ) \|\| any(!classGenes %in% rownames(genesInfoList[[nw]])))
1490: } else classGraph <- igraph::graph.data.frame(as.data.frame(genesNetwork[[nw]]@edges[,ntwColnames,drop=FALSE]), ...(15 bytes skipped)...
1493: classGraph <- igraph::graph.data.frame(as.data.frame(genesNetwork[[nw]]@edges[,ntwColnames,drop=FALSE]), ...(101 bytes skipped)...
1495: if (igraph::vcount(classGraph) != 0)
1501: graphLayout <- igraph::layout.fruchterman.reingold(classGraph) # .grid is faster, but the result looks far worse.
1509: vertexLabels <- igraph::get.vertex.attribute(classGraph,"name")
1515: if(!is.null(igraph::get.vertex.attribute(classGraph,"exprsMeanDiff")))
1517: exprsDiff <- as.numeric(igraph::get.vertex.attribute(classGraph,"exprsMeanDiff"))
1539: if(!is.null(igraph::get.vertex.attribute(classGraph,"discriminantPower")))
1541: discPower<-round(as.numeric(igraph::get.vertex.attribute(classGraph,"discriminantPower")))
1552: # Shape: Classification gene
1555: if(!is.null(igraph::get.vertex.attribute(classGraph,"discriminantPower")))
1560: if(!is.null(classificationGenes)) # alguna comprobacion mas?
1562: vertexShape[which(igraph::get.vertex.attribute(classGraph,"name")%in% as.vector(getRanking(classificationGenes, showGeneID=TRUE)$geneID))] <- "square"
1567: relColors <- ifelse( igraph::get.edge.attribute(classGraph,"relation")==levels(factor(igraph::get.edge.attribute(classGraph,"relation")))[1], relColors[1],relColors[2])
1571: if(igraph::ecount(classGraph) > 0)
1573: igraph::tkplot(classGraph, layout=graphLayout, vertex.label=vertexLabels, vertex.label.family="sans", vertex.color=vert...(178 bytes skipped)...
1583: plot(classGraph, layout=graphLayout, vertex.label=vertexLabels, vertex.label.family="sans", vertex.label.cex=...(219 bytes skipped)...
1586: graphList <- c(graphList, graph=list(classGraph))
1607: text(0,0.6,"Node shape: Chosen/Not chosen for classification", pos=4, font=2)
5: # queryGeNetClassifier
21: # queryGeNetClassifier:
106: mxcf <- cbind(mxcf, matrix(ncol=length(missingClasses), nrow=dim(mxcf)[1], data=0))
107: colnames(mxcf)[which(colnames(mxcf)=="")]<-missingClasses
114: mxcf <- rbind(mxcf, matrix(nrow=length(missingClasses), ncol=dim(mxcf)[2], data=0))
115: rownames(mxcf)[which(rownames(mxcf)=="")]<-missingClasses
129: #nclasses <- dim(mxcf)[1]
131: numNA <- sum(mxcf[,nclasses+1])
137: falseNegatives <- array(0,dim=nclasses)
138: falsePositives <- array(0,dim=nclasses)
139: trueNegatives <- array(0,dim=nclasses)
140: truePositives <- array(0,dim=nclasses)
144: for(j in 1:nclasses) #dim(mxcf)[2]) Para incluir NA
156: for (i in 1:nclasses) #We need another loop in order to have the whole trueNegatives ready
175: # Can receive the result from executing queryGeNetClassifier, or a list of several: queryResult<-c(assignment1, assignment2)
229: # Can receive the result from executing queryGeNetClassifier, or a list of several: queryResult<-c(prediction1, prediction2)
252: stats <- cbind(c(rep(0,numClasses)), c(rep(1,numClasses)), c(rep(0,numClasses)),c(rep(NA,numClasses)),c(rep(NA,numClasses)))
257: for (c in 1:numClasses)
321: ...(12 bytes skipped)...nts <- function(queryResult, realLabels, minProbAssignCoeff=1, minDiffAssignCoeff=0.8, totalNumberOfClasses=NULL, pointSize=0.8, identify=FALSE)
340: numClasses <- totalNumberOfClasses
357: rand <- 1/numClasses
358: if(numClasses>2) { minProb <- 2rand minProbAssignCoeff
364: if(numClasses>2)
377: if(numClasses>2) graphics::text(0.3, 0.95, labels="Not Assigned", col="#606362", cex=0.8)
513: colnames(meanExprDiff) <- gClasses(genesRanking)
526: if (length(gClasses(genesRanking)) > 2){ ord <- genesRanking@ord[1:numGenesPlot,]
535: if((numClasses>3 && numClasses<10) && ("RColorBrewer" %in% rownames(utils::installed.packages())))
537: cols <- RColorBrewer::brewer.pal(numClasses,"Set1")
538: }else cols <- grDevices::rainbow(numClasses)
547: for(i in 1:numClasses)
555: legend("bottomleft", paste( gClasses(genesRanking)," (",lp," genes)",sep=""), lty=1, col=cols, pch=pchs)
700: for(i in 1:numClasses) #Por si no estan agrupados
731: if( numClasses == 0 \|\| !is.matrix(genes) ) {
992: if(longClassNames)
1102: if(longClassNames){ tit<- paste(tit,"DP: ", sep="")
1105: barplot(rep(0,numClasses),add=FALSE, ylim=lims, main=tit, col=mycols, width=0.9, space=0.1, cex.main=1)
1217: if(!is.logical(plotClassifcationGenesNetwork)) stop("plotClassifcationGenesNetwork should be either TRUE or FALSE.")
1248: if(!(returniGraphs \|\| plotAllNodesNetwork \|\| plotOnlyConnectedNodesNetwork \|\| plotClassifcationGenesNetwork)) stop("No network plots have been requested.")
1249: if(!(plotAllNodesNetwork \|\| plotOnlyConnectedNodesNetwork \|\| plotClassifcationGenesNetwork)) warning("No network plots have been requested, only the iGraph will be return...(13 bytes skipped)...
1252: if(is(genesNetwork, "GeNetClassifierReturn"))
1264: }else stop("'genesNetwork' is the return of geNetClassifier, but doesn't contain a genesNetwork.")
1276: # if (nwClasses[1] == nwClasses[2]) nwClasses <- nwClasses[1]
1280: names(genesNetwork) <- nwClasses[1]
1320: for( cl in nwClasses)
1460: genesNetwork <- genesNetwork[-which(names(genesNetwork) %in% nwClasses)]
1475: cols <- ceiling(sqrt(numClasses))
1476: rows <- ifelse(sqrt(numClasses)<round(sqrt(numClasses)), ceiling(sqrt(numClasses)),round(sqrt(numClasses)))

CancerSubtypes:R/internal.R: [ ]
427: class <- unique(x)
169: affinityMatrix <- function(Diff,K=20,sigma=0.5) {
406: classx <- unique(x)
407: classy <- unique(y)
105: .distanceWeighted2<-function(X,weight) ##X is the expression Matrix(Row is sample, column is feature)
155: #' This is the affinity Matrix function extracted from SNFtool package.
429: nc <- length(class)
433: prob[i] <- sum(x == class[i])/nx
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

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

ClassifyR:R/utilities.R: [ ]
762: class <- factor(object$groups[min_scores], levels = object$groups)
66: isDesiredClass <- sapply(measurements, function(column) is(column, restrict))
138: isDesiredClass <- sapply(dataTable, function(column) is(column, restrict))
525: classTable <- subset(PRtable, class == aClass)
562: classRow <- which(autoCharacteristics[, "characteristic"] == "Classifier Name")
659: .rebalanceTrainingClasses <- function(measurementsTrain, classesTrain, balancing)
661: samplesPerClassTrain <- table(classesTrain)
10: ## String specifies the name of a single outcome column, typically a class.
20: if(class(outcomes) != "factor") # Assume there will be no ordinary regression prediction tasks ... for now.
179: # Create maximally-balanced folds, so class balance is about the same in all.
184: # Dummy encoding for when outcome is not a class.
188: # Permute the indexes of samples in the class.
209: # Take the same percentage of samples from each class to be in training set.
348: # Classifiers will use a column "class" and survival models will use a column "risk".
349: if(class(predictions) == "data.frame")
350: predictedOutcomes <- predictions[, na.omit(match(c("class", "risk"), colnames(predictions)))]
403: if(class(predictions) == "data.frame")
404: predictedOutcomes <- predictions[, "class"]
455: if(class(predictions) == "data.frame")
519: # PRtable is a data frame with columns FPR, TPR and class.
520: # distinctClasses is a vector of all of the class names.
549: else if("Pairs" %in% class(importantFeatures[[1]]))
637: lapply(1:length(densities), function(densityIndex) # All crossing points with other class densities.
657: # Samples in the training set are upsampled or downsampled so that the class imbalance is
702: stop("There must be at least 2 observations in each class.")
705: # By default, we estimate the 'a priori' probabilities of class membership with
711: # For each class, we calculate the MLEs (or specified alternative estimators)
713: # estimators for each class.
729: class(obj) <- "dlda"
735: stop("object not of class 'dlda'")
742: sapply(object$est, function(class_est) {
743: with(class_est, sum((obs - xbar)^2 / object$var_pool) + log(prior))
764: list(class = class, scores = scores, posterior = posterior)
16: outcomes <- measurements[, classColumn]
17: measurements <- measurements[, -classColumn]
67: measurements <- measurements[, isDesiredClass, drop = FALSE]
139: dataTable <- dataTable[, isDesiredClass, drop = FALSE]
144: # Only return independent variables in dataTable for making classifications with.
156: # For classifiers which use one single function for inputting a training and a testing table,
160: .checkVariablesAndSame <- function(trainingMatrix, testingMatrix)
162: if(ncol(trainingMatrix) == 0) # Filtering of table removed all columns, leaving nothing to classify with.
164: else if(ncol(trainingMatrix) != ncol(testingMatrix))
219: testSet <- setdiff(1:length(classes), trainSet)
286: betterValues <- .ClassifyRenvir[["performanceInfoTable"]][.ClassifyRenvir[["performanceInfoTable"]][, "type"] == performanceType, "better"]
339: # Do either resubstitution classification or nested-CV classification and calculate the resulting performance metric.
398: result <- runTest(measurementsSelected, classesTrain,
468: betterValues <- .ClassifyRenvir[["performanceInfoTable"]][.ClassifyRenvir[["performanceInfoTable"]][, "type"] == performanceType, "better"]
474: if(modellingParams@trainParams@classifier@generic != "previousTrained")
475: # Don't name these first two variables. Some classifier functions might use classesTrain and others use outcomesTrain.
477: else # Don't pass the measurements and classes, because a pre-existing classifier is used.
485: trained <- do.call(modellingParams@trainParams@classifier, paramList)
523: do.call(rbind, lapply(distinctClasses, function(aClass)
527: for(index in 2:nrow(classTable))
529: # Some samples had identical predictions but belong to different classes.
530: if(classTable[index, "FPR"] != classTable[index - 1, "FPR"] && classTable[index, "TPR"] != classTable[index - 1, "TPR"])
532: newArea <- (classTable[index, "FPR"] - classTable[index - 1, "FPR"]) * classTable[index - 1, "TPR"] + # Rectangle part
533: 0.5 * (classTable[index, "FPR"] - classTable[index - 1, "FPR"]) * (classTable[index, "TPR"] - classTable[index - 1, "TPR"]) # Triangle part on top.
535: newArea <- (classTable[index, "FPR"] - classTable[index - 1, "FPR"]) * classTable[index, "TPR"]
539: data.frame(classTable, AUC = round(areaSum, 2), check.names = FALSE)
558: # Remove duplication of values for classifiers that have one function for training and
560: if("Classifier Name" %in% autoCharacteristics[, "characteristic"] && "Predictor Name" %in% autoCharacteristic...(22 bytes skipped)...
564: if(autoCharacteristics[classRow, "value"] == autoCharacteristics[predRow, "value"])
631: # Used by the mixtures of normals and naive Bayes classifiers.
664: trainBalanced <- unlist(mapply(function(classSize, className)
666: if(balancing == "downsample" && classSize > downsampleTo)
667: sample(which(classesTrain == className), downsampleTo)
668: else if(balancing == "upsample" && classSize < upsampleTo)
669: sample(which(classesTrain == className), upsampleTo, replace = TRUE)
671: which(classesTrain == className)
674: classesTrain <- classesTrain[trainBalanced]
676: list(measurementsTrain = measurementsTrain, classesTrain = classesTrain)
692: stop("The number of 'prior' probabilities must match the number of classes in 'y'.")
712: # for each parameter used in the DLDA classifier. The 'est' list contains the
722: # Calculates the pooled variance across all classes.
521: .calcArea <- function(PRtable, distinctClasses)
662: downsampleTo <- min(samplesPerClassTrain)
663: upsampleTo <- max(samplesPerClassTrain)
672: }, samplesPerClassTrain, names(samplesPerClassTrain), SIMPLIFY = FALSE))

scone:R/sconeReport.R: [ ]
837: Class = factor(strat_col())
113: # Matrix nodes in scone_res
310: "Row Class",
319: label = "Column Class",
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.")

ISAnalytics:R/internal-functions.R: [ ]
98: rlang::inform(warn, class = "missing_crit_tags")
215: ), class = "missing_req_col_err")
243: ), class = "missing_req_col_err")
427: rlang::inform(warn_empty_iss, class = "warn_empty_iss")
535: class = "warn_empty_iss"
597: rlang::abort(error_msg(tags_names), class = "missing_tags_err")
705: rlang::abort(single_err, class = "tag_dupl_err")
770: rlang::abort(compact_msg, class = "tag_type_err")
1011: rlang::abort(add_types_err, class = "add_types_err")
1189: class = "xls_file"
1365: class = "na_concat"
1465: class = "filter_warn"
1846: class = "missing_path_col"
2802: rlang::inform(missing_msg, class = "auto_mode_miss")
3025: class = "coll_matrix_issues"
3090: rlang::abort(not_date_err, class = "not_date_coll_err")
4085: rlang::inform(warn, class = "rec_unsupp_ext")
4852: class = "missing_cols_key"
4900: rlang::inform(flag_msg, class = "flag_logic_long")
5217: rlang::abort(format_err, class = "outlier_format_err")
4821: KnownGeneClass = ifelse(
892: colClasses = col_types,
601: rlang::abort(error_msg(missing_tags), class = "missing_tags_err")
723: rlang::inform(single_warn, class = "tag_dupl_warn")
746: err <- c(paste("Wrong col class for tag '", sub_df$tag[1], "'"),
783: # @param x A data.frame object (or any extending class)
802: # @param x A data.frame object (or any extending class)
854: #---- USED IN : import_single_Vispa2Matrix ----
1008: "?import_single_Vispa2Matrix"
1024: class = "unsup_comp_format"
1039: class = "im_single_miss_mand_vars"
1148: class = "ism_import_summary"
2826: rlang::inform(dupl_msg, class = "auto_mode_dupl")
4870: class = "missing_cols_pool"
4922: class = "flag_logic_short"
4940: rlang::abort(unknown_logi_op_err, class = "unsupp_logi_op")
5220: rlang::abort(format_err, class = "outlier_format_err")
767: "Wrong column classes for some tags",
925: col_types <- .mandatory_IS_types("classic")
929: .annotation_IS_types("classic")
1020: ### If not, switch to classic for reading
1021: mode <- "classic"

BioNERO:R/gcn_inference.R: [ ]
428: hm <- WGCNA::labeledHeatmap(Matrix = modtraitcor,
407: textMatrix <- paste(signif(modtraitcor, 2), modtraitsymbol, sep = "")
732: # Create a data frame containing annotations and the annotation class
741: # Add column containing the annotation class
408: dim(textMatrix) <- dim(modtraitcor)
412: textMatrix <- t(textMatrix)
433: textMatrix = textMatrix, setStdMargins = FALSE,

trio:R/qingInternal.R: [ ]
7782: class = unlist(lapply(elm, FUN=class))
7773: class.last = NULL
9892: anyMatrix = inMatrix[,colVec]
3071: if(class(data[,m[i]])=="factor") data[,i]=as.character(data[,m[i]])
3074: if(class(data[,m[i]])=="factor") data[,i]=as.numeric(as.character(data[,m[i]]))
3077: if(class(data[,m[i]])=="factor") data[,i]=as.numeric(as.character(data[,m[i]]))
7793: if(length(class.last)==length(class)){
7794: all.m = class.last==class
7795: if(sum(all.m)!=length(class)) search=FALSE
7809: class.last=class
7810: class=NULL
7818: reStr = paste(c("name", "class", "length"),
7819: c("", class.last[1], len.last[1]),
7822: reStr = paste(c("name", "class", "length"),
7823: c(names.last[1], class.last[1], len.last[1]),
9415: if(class(data[,i])=="factor") data[,i]=as.numeric(as.character(data[,i]))
9836: util.listMatrix.2matrix <-
9890: function(inMatrix, colVec, discIn=NULL, discVec=NULL, delimVec, digitVec, missingVec=NULL){
9893: mRow = dim(anyMatrix)[1]
9894: mCol = dim(anyMatrix)[2]
9899: re[,1]= inMatrix[,discIn]
9906: num = anyMatrix[row, col]

GRaNIE:R/core.R: [ ]
4441: d = tibble::add_row(d, r_positive = r_pos, class = classCur, peak_gene.p.raw.class = pclassCur, n = 0)
587: GC_class = cut(`G\|C`, breaks = seq(0,1,0.1), include.lowest = TRUE, ordered_result = TRUE))
604: peak.GC.class = GC_class)
4436: for (classCur in networkType_details){
4489: classAll = c(as.character(d2$class), d3$r_positive),
2253: GC_class.cur = GC_classes_foreground.df$GC_class[i]
4518: peak_gene.p.raw.class.bin = as.numeric(peak_gene.p.raw.class)) %>%
589: GC_classes.df = GC_content.df %>%
1052: TFBS_bindingMatrix.df = tibble::as_tibble(res.l)
1539: AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05,
1946: GC_classes_foreground.df = peaksForeground %>%
1954: GC_classes_background.df = peaksBackground %>%
2005: GC_classes_all.df = dplyr::full_join(GC_classes_foreground.df, GC_classes_background.df, suffix = c(".fg",".bg"), by = "GC_class") %>%
4529: .classFreq_label <- function(tbl_freq) {
4437: for (pclassCur in levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class)) {
3: #' Initialize a \code{\linkS4class{GRN}} object
9: #' @return Empty \code{\linkS4class{GRN}} object
101: #' Add data to a \code{\linkS4class{GRN}} object
114: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
590: dplyr::group_by(GC_class) %>%
593: tidyr::complete(GC_class, fill = list(n = 0)) %>%
597: #ggplot(GC_content.df, aes(GC.class)) + geom_histogram(stat = "count") + theme_bw()
599: #ggplot(GC_classes.df , aes(GC.class, n_rel)) + geom_bar(stat = "identity") + theme_bw()
614: #' Filter data from a \code{\linkS4class{GRN}} object
629: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
871: #' Add TFBS to a \code{\linkS4class{GRN}} object
880: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
992: #' Overlap peaks and TFBS for a \code{\linkS4class{GRN}} object
997: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
1252: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
1411: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
1523: #' Run the activator-repressor classification for the TFs for a \code{\linkS4class{GRN}} object
1533: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
1657: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_density"), "_", connectionTypeCur, suffixFile, ".pdf")
1667: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_medianClass"), "_", connectionTypeCur, suffixFile, ".pdf")
1680: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_densityClass"), "_", connectionTypeCur, suffixFile, ".pdf")
1742: #' Add TF-peak connections to a \code{\linkS4class{GRN}} object
1756: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
1947: dplyr::group_by(GC_class) %>%
1950: tidyr::complete(GC_class, fill = list(n = 0)) %>%
1955: dplyr::group_by(GC_class) %>%
1958: tidyr::complete(GC_class, fill = list(n = 0)) %>%
1970: minPerc= min(GC_class.all$maxSizeBackground) / nPeaksBackground
2020: peaksBackgroundGCCur = peaksBackground %>% dplyr::filter(GC_class == GC_classes_foreground.df$GC_class[i])
2258: dplyr::filter(GC_class == GC_class.cur) %>%
2260: #futile.logger::flog.info(paste0(" GC.class ", GC.class.cur, ": Required: ", requiredNoPeaks, ", available: ", availableNoPeaks))
2265: #futile.logger::flog.info(paste0(" Mimicking distribution FAILED (GC class ", GC.class.cur, " could not be mimicked"))
2288: #' Add peak-gene connections to a \code{\linkS4class{GRN}} object
2302: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function in different flavors.
2844: #' @return The same \code{\linkS4class{GRN}} object, with the filtered and merged TF-peak and peak-gene connections in the slot connection...(15 bytes skipped)...
3411: #' Add TF-gene correlations to a \code{\linkS4class{GRN}} object. The information is currently stored in \code{GRN@connections$TF_genes.filtered}. Note...(36 bytes skipped)...
3419: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
3654: #' Generate a summary PDF for the number of connections for a \code{\linkS4class{GRN}} object.
3669: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
3952: #' An example \code{\linkS4class{GRN}} object
3988: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object
3990: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object.
4081: #' Extract connections from a \code{\linkS4class{GRN}} object
4088: ...(8 bytes skipped)...rn A data frame with the connections. Importantly, this function does NOT return a \code{\linkS4class{GRN}} object.
4211: "plot_class_density" = "TF_classification_densityPlots",
4212: "plot_class_medianClass" = "TF_classification_stringencyThresholds",
4213: "plot_class_densityClass" = "TF_classification_summaryHeatmap",
4273: ...(0 bytes skipped)...#' Retrieve parameters for previously used function calls and general parameters for a \code{\linkS4class{GRN}} object.
4279: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
4331: #' @return The same \code{\linkS4class{GRN}} object, with some slots being deleted (\code{GRN@data$TFs$classification} as well as \code{GRN@stats$connectionDetails.l})
4409: #' @return The same \code{\linkS4class{GRN}} object, with the output directory being adjusted accordingly
4430: dplyr::group_by(r_positive, class, peak_gene.p.raw.class) %>%
4439: row = which(d$r_positive == r_pos & d$class == classCur & as.character(d$peak_gene.p.raw.class) == as.character(pclassCur))
4447: # Restore the "ordered" factor for class
4448: d$peak_gene.p.raw.class = factor(d$peak_gene.p.raw.class, ordered = TRUE, levels = levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class))
4453: dplyr::group_by(r_positive, class) %>%
4459: dplyr::group_by(class, peak_gene.p.raw.class)%>%
4468: normFactor_real = dplyr::filter(dsum, class == !! (networkType_details[1])) %>% dplyr::pull(sum_n) %>% sum() /
4469: dplyr::filter(dsum, class == !! (networkType_details[2])) %>% dplyr::pull(sum_n) %>% sum()
4473: dplyr::group_by(peak_gene.p.raw.class, r_positive) %>%
4474: dplyr::summarise(n_real = .data$n[class == !! (names(colors_vec)[1]) ],
4475: n_permuted = .data$n[class == !! (names(colors_vec)[2]) ]) %>%
4483: stopifnot(identical(levels(d2$peak_gene.p.raw.class), levels(d3$peak_gene.p.raw.class)))
4486: d_merged <- tibble::tibble(peak_gene.p.raw.class = c(as.character(d2$peak_gene.p.raw.class),
4487: as.character(d3$peak_gene.p.raw.class)),
4492: peak_gene.p.raw.class = factor(peak_gene.p.raw.class, levels = levels(d2$peak_gene.p.raw.class)))
4494: d4 = tibble::tibble(peak_gene.p.raw.class = unique(d$peak_gene.p.raw.class),
4501: row_d2 = which(d2$class == networkType_details[1] & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4505: row_d2 = which(d2$class == paste0("random_",range) & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4509: row_d3 = which(d3$r_positive == TRUE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4511: row_d3 = which(d3$r_positive == FALSE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4519: dplyr::arrange(peak_gene.p.raw.class.bin)
4521: d4_melt = reshape2::melt(d4, id = c("peak_gene.p.raw.class.bin", "peak_gene.p.raw.class")) %>%
66: # Stringencies for AR classification
72: # Colors for the different classifications
132: checkmate::assertClass(GRN, "GRN")
269: if (isIntegerMatrix(counts_peaks[, GRN@config$sharedSamples])) {
270: GRN@data$peaks$counts_orig = DESeq2::DESeqDataSetFromMatrix(counts_peaks[, GRN@config$sharedSamples],
278: if (isIntegerMatrix(counts_rna[, GRN@config$sharedSamples])) {
279: GRN@data$RNA$counts_orig = DESeq2::DESeqDataSetFromMatrix(counts_rna[, GRN@config$sharedSamples],
607: GRN@stats$peaks$GC = GC_classes.df
645: checkmate::assertClass(GRN, "GRN")
888: checkmate::assertClass(GRN, "GRN")
1007: checkmate::assertClass(GRN, "GRN")
1054: if (!all(colnames(TFBS_bindingMatrix.df) %in% GRN@config$allTF)) {
1066: GRN@data$TFs$TF_peak_overlap = TFBS_bindingMatrix.df %>%
1072: GRN@data$TFs$TF_peak_overlap = .asSparseMatrix(as.matrix(GRN@data$TFs$TF_peak_overlap),
1259: checkmate::assertClass(GRN, "GRN")
1337: if (checkmate::testClass(data, "DESeqDataSet")) {
1418: checkmate::assertClass(GRN, "GRN")
1521: ######## AR classification ########
1526: ...(58 bytes skipped)... and 1. Default 0.05. Significance threshold for Wilcoxon test that is run in the end for the final classification. See the Vignette and diffTF paper for details.
1537: #' # GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)
1547: checkmate::assertClass(GRN, "GRN")
1557: GRN@data$TFs$classification$TF.translation.orig = GRN@data$TFs$translationTable %>%
1581: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]])) {
1582: if (is.null(GRN@data$TFs$classification[[permIndex]])) {
1583: GRN@data$TFs$classification[[permIndex]] = list()
1585: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
1588: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground) \|
1589: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background) \|
1590: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground) \|
1591: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background) \|
1592: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor) \|
1596: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
1619: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = res.l[["median_foreground"]]...(0 bytes skipped)...
1620: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = res.l[["median_background"]]...(0 bytes skipped)...
1621: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = res.l[["foreground"]]
1622: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = res.l[["background"]]
1624: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor = TF_peak_cor
1627: # Final classification: Calculate thresholds by calculating the quantiles of the background and compare the real ...(24 bytes skipped)...
1628: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l) \| forceRerun) {
1629: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l =
1630: .calculate_classificationThresholds(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
1634: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification) \| forceRerun) {
1636: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification =
1638: output.global.TFs = GRN@data$TFs$classification$TF.translation.orig %>% dplyr::mutate(TF = TF.name),
1639: median.cor.tfs = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground,
1640: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
1642: t.cor.sel.matrix = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground),
1643: t.cor.sel.matrix.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
1648: # PLOTS FOR THE RNA-SEQ CLASSIFICATION
1659: .plot_density(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground),
1660: .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
1670: median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground),
1671: median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background),
1673: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
1683: TF_peak_cor = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor
1685: .plot_heatmapAR(TF.peakMatrix.df = peak_TF_overlapCur.df,
1690: median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground),
1691: median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background),
1692: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
1693: finalClassification = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification,
1703: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = NULL
1704: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = NULL
1705: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = NULL
1706: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = NULL
1707: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l = NULL
1711: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground =
1712: .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground), convertNA_to_zero = TRUE)
1713: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background =
1714: .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background), convertNA_to_zero = TRUE)
1772: checkmate::assertClass(GRN, "GRN")
1973: minPerc = .findMaxBackgroundSize(GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage = threshold_percentage)
2018: for (i in seq_len(nrow(GC_classes_foreground.df))) {
2026: #Select the minimum, which for low % classes is smaller than the required number to mimic the foreground 100%
2028: nPeaksCur = GC_classes_all.df$n.bg.needed[i]
2030: nPeaksCur = min(GC_classes_all.df$n.bg.needed[i], nrow(peaksBackgroundGCCur))
2033: if (GC_classes_all.df$n.bg.needed[i] > nrow(peaksBackgroundGCCur)) {
2048: plots_GC.l[[TFCur]] = .generateTF_GC_diagnosticPlots(TFCur, GC_classes_foreground.df, GC_classes_background.df, GC_classes_all.df, peaksForeground, peaksBackground, peaksBackgroundGC)
2233: .findMaxBackgroundSize <- function (GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage = 0.05) {
2250: for (i in seq_len(nrow(GC_classes_foreground.df))) {
2252: n_rel = GC_classes_foreground.df$n_rel[i]
2257: availableNoPeaks = GC_classes_background.df %>%
2269: if (i == nrow(GC_classes_foreground.df)) {
2275: } # end of for (i in 1:nrow(GC_classes_foreground.df)) {
2318: checkmate::assertClass(GRN, "GRN")
2877: checkmate::assertClass(GRN, "GRN")
3689: checkmate::assertClass(GRN, "GRN")
4023: if (checkmate::testClass(countsOrig, "DESeqDataSet")) {
4041: if (checkmate::testClass(countsOrig, "DESeqDataSet")) {
4097: checkmate::assertClass(GRN, "GRN")
4286: checkmate::assertClass(GRN, "GRN")
4328: #' Optional convenience function to delete intermediate data from the function \code{\link{AR_classification_wrapper}} and summary statistics that may occupy a lot of space
4341: checkmate::assertClass(GRN, "GRN")
4346: GRN@data$TFs$classification[[permIndex]]$TF_cor_median_foreground = NULL
4347: GRN@data$TFs$classification[[permIndex]]$TF_cor_median_background = NULL
4348: GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_foreground = NULL
4349: GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_background = NULL
4350: GRN@data$TFs$classification[[permIndex]]$act.rep.thres.l = NULL
4434: # Some classes might be missing, add them here with explicit zeros
4491: dplyr::mutate(classAll = factor(classAll, levels=c(paste0("real_",range), paste0("random_",range), "TRUE", "FALSE")),
67: par.l$internal$allClassificationThresholds = c(0.1, 0.05, 0.01, 0.001)
1228: peak_TF_overlapCur.df = .asMatrixFromSparse(GRN@data$TFs$TF_peak_overlap, convertZero_to_NA = FALSE) %>%
1637: .finalizeClassificationAndAppend(

spatialHeatmap:inst/extdata/shinyApp/R/server.R: [ ]
205: tags$div(class='tp', span(class='tpt', 'Ensure "columns in the data matrix corresponds with "rows" in the targets file respectively...(4 bytes skipped)...
207: tags$div(class='tp', span(class='tpt', 'Ensure "rows" in the data matrix corresponds with "rows" in the row metadata file respectiv...(7 bytes skipped)...
224: tags$div(class='tp', span(class='tpt', 'The data is matched with a single aSVG file.'),
226: tags$div(class='tp', span(class='tpt', 'The data is matched with multiple aSVG files (e.g. developmental stages).'),
764: class='cell-border strip hover') %>% formatStyle(0, backgroundColor="orange", cursor='pointer') %>%
783: class='cell-border strip hover') %>% formatStyle(0, backgroundColor="orange", cursor='pointer') %>%
796: class='cell-border strip hover') %>% formatStyle(0, backgroundColor="orange", cursor='pointer') %>%
971: validate(need(try(ncol(gene)>4), 'The "sample__condition" variables in the Data Matrix are less than 5, so no coexpression analysis is applied!'))
990: #if (is.null(input$mhm.but)) return() # Matrix heatmap sections is removed.
1000: withProgress(message="Matrix heatmap:", value=0, {
1009: cat('Matrix heatmap ... \n')
1014: withProgress(message="Matrix heatmap:", value=0, {
1027: withProgress(message="Matrix heatmap:", value=0, {
1145: if (is.null(input$gen.sel)) return() # Matrix heatmap section is removed.
1398: span(class = "panel panel-default", style = 'margin-left:0px',
1399: div(class = "panel-heading", strong("Features in aSVG")),
1400: div(class = "panel-body", id = ns("ftSVG"), ft2tag(sf.all))
1402: div(class = "panel panel-default",
1403: div(class = "panel-heading", strong("Features in data")),
1946: # print(list(ID, is.null(svg.df()), is.null(geneIn()), ids$sel, color$col[1], class(color$col[1])))
2866: d.tab <- datatable(vs, selection='none', extensions='Scroller', plugins = "ellipsis", class='cell-border strip hover', options = list(dom = 't', scrollX = TRUE)); cat('Done! \n')
3053: class='cell-border strip hover') %>% formatStyle(0, backgroundColor="orange", cursor='pointer'); cat('Don...(7 bytes skipped)...
3164: class='cell-border strip hover') %>% formatStyle(0, backgroundColor="white", cursor='pointer')
3334: class='cell-border strip hover') %>% formatStyle(0, backgroundColor="white", cursor='pointer')
3363: ...(176 bytes skipped)...TRUE, search=list(regex=TRUE, smart=FALSE, caseInsensitive=TRUE), searching=TRUE, columnDefs=NULL), class='cell-border strip hover') %>% formatStyle(0, backgroundColor="white", cursor='pointer')
3423: library(Matrix)
3430: if ('logcounts' %in% assay.na) logcounts(sce.upl) <- as(logcounts(sce.upl), 'dgCMatrix')
3431: if ('count' %in% assay.na) assays(sce.upl)$count <- as(assays(sce.upl)$count, 'dgCMatrix')
3432: if ('counts' %in% assay.na) assays(sce.upl)$count <- as(assays(sce.upl)$counts, 'dgCMatrix')

singleCellTK:inst/shiny/server.R: [ ]
1486: shinyjs::addClass(id = "f_button1", class = "btn-block")
5005: classes <- names(colData(hmTemp$sce))
6144: classCol <- colData(vals$counts)[[input$deC1Class]]
6145: classChoices <- sort(as.vector(unique(classCol)))
98: updateSelectInput(session, "deC1Class",
200: label = "Select Input Matrix:",
204: label = "Select Input Matrix:",
209: updateSelectInputTag(session, "clustScranSNNMat", label = "Select Input Matrix:",
1487: shinyjs::addClass(id = "f_button2", class = "btn-block")
1488: shinyjs::addClass(id = "f_button3", class = "btn-block")
1489: shinyjs::addClass(id = "f_button4", class = "btn-block")
1490: shinyjs::addClass(id = "f_button5", class = "btn-block")
1491: shinyjs::addClass(id = "f_button6", class = "btn-block")
1492: shinyjs::addClass(id = "f_button7", class = "btn-block")
1493: shinyjs::addClass(id = "filterData", class = "btn-block")
1494: shinyjs::addClass(id = "resetData", class = "btn-block")
1495: shinyjs::addClass(id = "convertGenes", class = "btn-block")
1496: shinyjs::addClass(id = "deleterowDatabutton", class = "btn-block")
1497: shinyjs::addClass(id = "downsampleGo", class = "btn-block")
2780: shinyjs::show(selector = ".dimRedPCAICA_plotTabset_class")
4097: shinyjs::addClass(id = "cv_button1", class = "btn-block")
4098: shinyjs::addClass(id = "cv_button2", class = "btn-block")
4099: shinyjs::addClass(id = "cv_button3", class = "btn-block")
4519: pltVars$class <- "factor"
4521: pltVars$class <- "numeric"
4574: conditionClass = pltVars$class, defaultTheme = as.logical(pltVars$defTheme))
4590: ...(28 bytes skipped)... legendSize = input$adjustlegendsize,legendTitleSize = input$adjustlegendtitlesize,conditionClass = pltVars$class)
5072: ...(7 bytes skipped)... p("Since more than 12 unique values detected, discrete colors will be assigned for this class")
5095: p(paste0("Totally ", nUniq, " unique values in this class of annotation, which is too many to provide manual selection. Coloring will be provided by default....(3 bytes skipped)...
5128: p("No effective category found for the class.")
5454: selectInput("batchCheckCorrName", "Corrected Matrix",
6143: !input$deC1Class == "None"){
6156: !input$deC1Class == "None"){
6157: classCol <- colData(vals$counts)[[input$deC1Class]]
6170: g1Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G1
6184: g2Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G2
6378: class = input$deC1Class,
3490: conditionClass = "factor",
3541: conditionClass = "factor",
5006: selectInput('hmCellAnn', 'Add cell annotation', classes,
5013: classes <- names(rowData(hmTemp$sce))
5014: selectInput('hmGeneAnn', 'Add feature annotation', classes,
5537: conditionClass = "character",
5545: conditionClass = "character",
6147: choices = classChoices, multiple = TRUE)
6158: classChoices <- sort(as.vector(unique(classCol)))
6160: choices = classChoices, multiple = TRUE)
6379: classGroup1 = input$deC1G1,
6380: classGroup2 = input$deC1G2,
7301: vals$effectSizes <- calcEffectSizes(countMatrix = expData(vals$counts, input$snapshotAssay), condition = colData(vals$counts)[, input$selectSnapsho...(12 bytes skipped)...

ChromSCape:inst/server.R: [ ]
2119: buttonType = "default", class = NULL) %>%
2986: MSIG.classes <- reactive({
54: req(input$options.bpparam_class, input$options.nb_workers)
55: BPPARAM = BiocParallel::bpparam(input$options.bpparam_class)
457: you have to input a single Dense Matrix."),
633: title = shiny::HTML(paste0("<span style='color: white'><h4> <i class='far fa-caret-square-down'",
645: title = shiny::HTML(paste0("<span style='color: white'><h4> <i class='far fa-caret-square-down'",
972: df = data.frame(coverage = sort(unname(Matrix::colSums(init$datamatrix))))
1006: df = data.frame(coverage = sort(unname(Matrix::rowSums(init$datamatrix)),decreasing = TRUE))
2376: return( as.character(icon("check-circle", class = "large_icon")))}
2378: return( as.character(icon("times-circle", class = "large_icon")))
2893: DT::datatable(diff, options = list(dom='tpi'), class = "display",
3064: output$enr_class_sel <- renderUI({
3067: inputId = "enr_class_sel", inline = TRUE,
3075: if(!is.null(scExp_cf()@metadata$enr) && !is.null(input$GSA_group_sel) && !is.null(input$enr_class_sel) &&
3081: enr_class_sel = input$enr_class_sel) %>%
3086: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3100: enr_class_sel = input$enr_class_sel) %>%
3105: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3120: enr_class_sel = input$enr_class_sel) %>%
3125: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3165: most_diff[,"qval"] = Matrix::rowMeans(as.matrix(most_diff[,-1]))
3375: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3390: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3405: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
189: if(input$data_choice_box == "DenseMatrix"){
197: else if (input$data_choice_box == "SparseMatrix"){
234: Example of folder structure (for scBED, but applies in a similar manner for SparseMatrix, Fragment File & scBAM) : <br>
266: if(input$data_choice_box != "DenseMatrix"){
285: if(input$data_choice_box != "DenseMatrix"){
286: if(input$data_choice_box == "SparseMatrix"){
338: if(input$data_choice_box %in% c("DenseMatrix", "SparseMatrix"))
450: if(type_file == "DenseMatrix" & !is.null(input$datafile_matrix)){
483: if(type_file == "SparseMatrix") {
498: if(type_file == "SparseMatrix"){
3042: GeneSetClasses = MSIG.classes(), progress = progress))
3065: req(MSIG.classes())
3068: label = "Select classes to display:",
3069: selected = MSIG.classes(), choiceNames = MSIG.classes(),
3070: choiceValues = MSIG.classes())
3219: req(pathways(), annotation_id(), MSIG.classes())
3228: database <- ChromSCape:::load_MSIGdb(annotation_id(), MSIG.classes())

psichomics:R/groups.R: [ ]
342: class="btn-group", role="group",
1674: panel <- tags$div(class="panel panel-info",
2039: class="table table-condensed",
168: div(class="alert alert-danger", role="alert",
335: id=ns("setOperations"), class="btn-group",
343: tags$button("More", id=ns(moreId), tags$span(class="caret"),
344: class="btn btn-default dropdown-toggle",
347: tags$ul(class="dropdown-menu dropdown-menu-right",
352: icon("user"), class=NULL, "Save selected groups",
356: icon("users"), class=NULL, "Save all groups", id=ns(saveAllGroupsId),
364: class="btn-group", role="group",
367: tags$span(class="caret"),
368: class="btn btn-default dropdown-toggle",
371: tags$ul(class="dropdown-menu dropdown-menu-right",
373: tags$li(role="separator", class="divider"), loadGroupsLink))
386: class="btn-group", role="group",
389: tags$span(class="caret"),
390: class="btn btn-default dropdown-toggle",
393: tags$ul(class="dropdown-menu dropdown-menu-right",
398: class="btn-danger",
403: class="li-danger",
409: class="pull-right", icon=icon("pencil-alt"))
421: colourSelector, class="groups-colourpicker",
424: class="pull-right", disable=FALSE,
428: id=ns("singleGroupSelected"), class="alert", role="alert",
429: class="alert-info",
434: div(class="input-group", nameField,
435: div(class="input-group-btn", renameButton))),
437: div(class="input-group", colourSelector,
438: div(class="input-group-btn", setColourButton)))))
441: class="btn-group pull-right",
444: tags$button(type="button", tags$span(class="caret"),
445: class="btn btn-danger dropdown-toggle",
449: tags$ul(class="dropdown-menu",
490: class ="btn-primary"))
510: class="btn-primary")
582: class="btn-primary")
615: class="btn-primary")
631: class="btn-primary"))
658: #' @param new Matrix: groups to which colours will be assigned
659: #' @param groups Matrix: groups to check which colours are already assigned
807: #' @return Matrix with the group names and respective elements
827: if ("simpleError" %in% class(set)) {
844: if ("simpleError" %in% class(set)) {
891: #' @param dataset Matrix or data frame: dataset
966: #' @param new Matrix: new groups
967: #' @param old Matrix: pre-existing groups
988: #' @param groups Matrix: groups
1003: #' @return Matrix containing groups (new group is in the first row)
1181: #' @return Matrix with groups ordered (or \code{NULL} if there are no groups)
1279: #' @param groups Matrix with groups
1379: #' @return Matrix with groups
1671: class="table table-condensed table-striped")
1676: tags$div(class="panel-heading", title), htmlTable)
1693: plus <- '<i class="fa fa-plus-circle" aria-hidden="true"></i>'
2033: class="well-sm",
2291: class(res) <- c(class(res), "groupIndependenceTest")
2375: class(df) <- c(class(df), "multiGroupIndependenceTest")
89: #' @importFrom shinyjs enable disable onclick toggleClass runjs
1710: orderable=FALSE, className='details-control', targets=0)),
1397: groups <- fread(file, colClasses="character")

Prostar:inst/ProstarApp/global.R: [ ]
462: class = "progress",
507: class = "progress-group",
644: textEl <- tags$p(class = "navbar-text", text)
654: form <- tags$form(class = "navbar-form", inputs)
271: classID = getOption("shiny.table.id", "example")
634: actionBtnClass <- "btn-primary"
636: PrevNextBtnClass <- "btn-info"
637: optionsBtnClass <- "info"
270: getOption("shiny.table.class", "table table-striped table-bordered")
280: paste("class=\"", classNames, "\" id=\"", classID, "\"", sep = ""),
416: ,div(class="busy-indicator",p(text),img(src=img))
463: class = if (!is.null(size)) paste0("progress-", size),
464: class = if (vertical) "vertical",
465: class = if (active) "active",
467: class = "progress-bar",
468: class = paste0("progress-bar-", color),
469: class = if (striped) "progress-bar-striped",
478: class = if (!label) "sr-only",
508: tags$span(class = "progress-text", text),
509: tags$span(class = "progress-number", sprintf("%d / %d", value, max)),
152: "Whole matrix" = "WholeMatrix",
158: gFiltersListAnaDiff[["Whole matrix"]] <- "WholeMatrix"
269: classNames <-
309: "corrMatrix"="corrMatrix",
320: # Original = list("boxplot", "densityplot", "heatmap", "CVDistr", "violinplot", "corrMatrix", "MVPlot1","MVPlot2","MVPlot3", "PCA_Ind", "PCA_Var", "PCA_Eigen"),
321: # Filtered = list("boxplot", "densityplot", "heatmap", "CVDistr", "violinplot", "corrMatrix", "MVPlot1","MVPlot2","MVPlot3", "PCA_Ind", "PCA_Var", "PCA_Eigen"),
322: # Normalized = list("boxplot", "densityplot", "heatmap", "CVDistr", "violinplot", "corrMatrix", "MVPlot1","MVPlot2","MVPlot3", "PCA_Ind", "PCA_Var", "PCA_Eigen", "compNorm"),
323: # Aggregated = list("boxplot", "densityplot", "heatmap", "CVDistr", "violinplot", "corrMatrix", "MVPlot1","MVPlot2","MVPlot3", "PCA_Ind", "PCA_Var", "PCA_Eigen"),
324: # Imputed.peptide = list("boxplot", "densityplot", "heatmap", "CVDistr", "violinplot", "corrMatrix", "MVPlot1","MVPlot2","MVPlot3", "PCA_Ind", "PCA_Var", "PCA_Eigen"),
325: # Imputed.protein = list("boxplot", "densityplot", "heatmap", "CVDistr", "violinplot", "corrMatrix", "MVPlot1","MVPlot2","MVPlot3", "PCA_Ind", "PCA_Var", "PCA_Eigen"),
326: # HypothesisTest = list("boxplot", "densityplot", "heatmap", "CVDistr", "violinplot", "corrMatrix", "MVPlot1","MVPlot2","MVPlot3", "PCA_Ind", "PCA_Var", "PCA_Eigen","logFCDistr" )
343: corrMatrix = "corrMatrix.png",
419: if ($('html').hasClass('shiny-busy')) {
421: if ($('html').hasClass('shiny-busy')) {
662: lstDescPlots <- c("intensity", "pca", "varDist", "corrMatrix", "heatmap", "mv", "quantiTable")
364: GOClassificationImg1 = "GOClassification_img1.png",
365: GOClassificationImg2 = "GOClassification_img2.png",
366: GOClassificationImg3 = "GOClassification_img3.png",

musicatk:inst/shiny/server.R: [ ]
15: class = "inactiveLink")
17: class = "inactiveLink")
19: class = "inactiveLink")
21: class = "inactiveLink")
23: class = "inactiveLink")
25: class = "inactiveLink")
27: class = "inactiveLink")
29: class = "inactiveLink")
31: class = "inactiveLink")
33: class = "inactiveLink")
35: class = "inactiveLink")
73: class = "inactiveLink")
118: class = "inactiveLink")
162: class = "inactiveLink")
164: class = "inactiveLink")
226: class = "inactiveLink")
228: class = "inactiveLink")
230: class = "inactiveLink")
232: class = "inactiveLink")
234: class = "inactiveLink")
246: class = "inactiveLink")
254: class = "inactiveLink")
256: class = "inactiveLink")
258: class = "inactiveLink")
262: class = "inactiveLink")
264: class = "inactiveLink")
272: class = "inactiveLink")
274: class = "inactiveLink")
276: class = "inactiveLink")
280: class = "inactiveLink")
282: class = "inactiveLink")
290: class = "inactiveLink")
292: class = "inactiveLink")
294: class = "inactiveLink")
296: class = "inactiveLink")
298: class = "inactiveLink")
300: class = "inactiveLink")
2299: label = "Method for Dissimilarity Matrix",
14: shinyjs::addCssClass(selector = "a[data-value='musica']",
16: shinyjs::addCssClass(selector = "a[data-value='annotations']",
18: shinyjs::addCssClass(selector = "a[data-value='tables']",
20: shinyjs::addCssClass(selector = "a[data-value='discover']",
22: shinyjs::addCssClass(selector = "a[data-value='predict']",
24: shinyjs::addCssClass(selector = "a[data-value='visualization']",
26: shinyjs::addCssClass(selector = "a[data-value='compare']",
28: shinyjs::addCssClass(selector = "a[data-value='differentialanalysis']",
30: shinyjs::addCssClass(selector = "a[data-value='cluster']",
32: shinyjs::addCssClass(selector = "a[data-value='heatmap']",
34: shinyjs::addCssClass(selector = "a[data-value='download']",
72: removeCssClass(selector = "a[data-value='musica']",
117: removeCssClass(selector = "a[data-value='tables']",
161: removeCssClass(selector = "a[data-value='discover']",
163: removeCssClass(selector = "a[data-value='predict']",
225: removeCssClass(selector = "a[data-value='visualization']",
227: removeCssClass(selector = "a[data-value='compare']",
229: removeCssClass(selector = "a[data-value='differentialanalysis']",
231: removeCssClass(selector = "a[data-value='cluster']",
233: removeCssClass(selector = "a[data-value='heatmap']",
245: removeCssClass(selector = "a[data-value='musica']",
253: removeCssClass(selector = "a[data-value='tables']",
255: removeCssClass(selector = "a[data-value='annotations']",
257: removeCssClass(selector = "a[data-value='download']",
261: removeCssClass(selector = "a[data-value='discover']",
263: removeCssClass(selector = "a[data-value='predict']",
271: removeCssClass(selector = "a[data-value='tables']",
273: removeCssClass(selector = "a[data-value='annotations']",
275: removeCssClass(selector = "a[data-value='download']",
279: removeCssClass(selector = "a[data-value='discover']",
281: removeCssClass(selector = "a[data-value='predict']",
289: removeCssClass(selector = "a[data-value='annotations']",
291: removeCssClass(selector = "a[data-value='visualization']",
293: removeCssClass(selector = "a[data-value='compare']",
295: removeCssClass(selector = "a[data-value='differentialanalysis']",
297: removeCssClass(selector = "a[data-value='cluster']",
299: removeCssClass(selector = "a[data-value='heatmap']",

hermes:R/differential.R: [ ]
227: Class = "HermesDataDiffExpr",
105: modelMatrixType = "standard",
222: Class = "HermesDataDiffExpr",
216: setOldClass("data.frame")
220: #' @exportClass HermesDataDiffExpr
221: .HermesDataDiffExpr <- setClass( # nolint

decoupleR:R/utils-dataset-converters.R: [ ]
89: class = "quo_missing_error"
209: class = "different_set_columns"
95: class = "quo_null_error"
231: #' @param mat Matrix in matrix format.

MotifDb:misc/hocomoco-v11/importV11.R: [ ]
382: class <- substr(x, 12, 12)
302: rawMatrixList <- readRawMatrices("./", dataDir)
384: tbl.secondary$dataSource[unmapped.secondary.only] <- paste0("HOCOMOCOv11-secondary-", class[unmapped.secondary.only])
385: tbl.secondary$dataSource[mapping] <- paste0("HOCOMOCOv11-core-", class[mapping])
303: length(rawMatrixList)
304: matrices <- extractMatrices (rawMatrixList)

Pi:R/xMLrandomforest.r: [ ]
89: class <- as.factor(gs_targets[!is.na(ind)])
88: df_predictor_class <- as.data.frame(df_predictor[ind[!is.na(ind)],])
3: ...(283 bytes skipped)... in rows and predictors in columns, with their predictive scores inside it. It returns an object of class 'sTarget'.
9: ...(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)...
20: #' an object of class "sTarget", a list with following components:
32: #' \item{\code{evidence}: an object of the class "eTarget", a list with following components "evidence" and "metag"}
86: ## predictors + class
90: df_predictor_class$class <- class
94: ...(40 bytes skipped)...ds (%d in GSP, %d in GSN) are used for supervised integration of %d predictors/features (%s).", sum(class==1), sum(class==0), ncol(df_predictor), as.character(now)), appendLF=TRUE)
104: message(sprintf("2. GS matrix of %d rows/genes X %d columns (predictors+class) are used as train set (%s) ...", nrow(df_predictor_class), ncol(df_predictor_class), as.character(now)), appendLF=TRUE)
106: message(sprintf("2. GS matrix of %d rows/genes X %d columns (predictors+class...(102 bytes skipped)...e remaining '1/%d' as test set. These spits are repeated over %d times (%s) ...", nrow(df_predictor_class), ncol(df_predictor_class), nfold, nfold-1, nfold, nfold, nrepeat, as.character(now)), appendLF=TRUE)
112: # preserve the overall class distribution
116: index_sets <- caret::createMultiFolds(y=df_predictor_class$class, k=nfold, times=nrepeat)
121: res_ls <- caret::createFolds(y=df_predictor_class$class, k=nfold, list=TRUE, returnTrain=TRUE)
143: trainset <- df_predictor_class[index_sets[[i]],]
146: message(sprintf("\tFold %d: %d GSP + %d GSN", i, table(trainset$class)[2], table(trainset$class)[1]), appendLF=TRUE)
159: #suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry, ...))
160: suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry))
166: trainset <- df_predictor_class[trainindex,]
169: message(sprintf("\tRepeatFold %d: %d GSP + %d GSN", i, table(trainset$class)[2], table(trainset$class)[1]), appendLF=TRUE)
183: #suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry, ...))
184: suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry))
194: ...(51 bytes skipped)...eature importance matrix of %d rows/predictors X %d columns/repeatsfolds (%s).", ncol(df_predictor_class)-1, nfoldnrepeat, as.character(now)), appendLF=TRUE)
229: trainset <- df_predictor_class
240: suppressMessages(rf.model.overall <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry))
241: rf.model.overall.importance <- randomForest::importance(rf.model.overall, type=NULL, class=NULL, scale=TRUE)[,3:4]
249: ...(38 bytes skipped)...OC matrix of %d rows (Supervised + predictors) X %d columns/repeatsfolds (%s).", ncol(df_predictor_class), nfoldnrepeat, as.character(now)), appendLF=TRUE)
259: testset <- df_predictor_class[-trainindex,]
351: ...(54 bytes skipped)...atrix of %d rows/genes X %d columns/repeatsfolds, aggregated via '%s' (%s) ...", nrow(df_predictor_class), nfoldnrepeat, fold.aggregateBy, as.character(now)), appendLF=TRUE)
473: class(sTarget) <- "sTarget"
37: #' @seealso \code{\link{xPierMatrix}}, \code{\link{xSparseMatrix}}, \code{\link{xPredictROCR}}, \code{\link{xPredictCompare}}, \code{\link{xSymbol2GeneID}}
58: df_predictor <- xPierMatrix(list_pNode, displayBy="score", combineBy="union", aggregateBy="none", RData.location=RData.location...(12 bytes skipped)...
62: eTarget <- xPierMatrix(list_pNode, displayBy="evidence", combineBy="union", aggregateBy="none", verbose=FALSE, RData.locat...(30 bytes skipped)...
206: df_res <- as.matrix(xSparseMatrix(df_res, verbose=FALSE))
294: df_res <- as.matrix(xSparseMatrix(df_res[,-4], verbose=FALSE))
317: df_res <- as.matrix(xSparseMatrix(df_res[,-3], verbose=FALSE))
362: df_full <- as.matrix(xSparseMatrix(df_full, verbose=FALSE))

pcaExplorer:R/pcaExplorer.R: [ ]
2684: class = "footer",
84: class = "btn_no_border",
225: # class = "btn btn-info"),
307: downloadButton("downloadData", "Download", class = "btn btn-success"),
318: actionButton("compute_pairwisecorr", "Run", class = "btn btn-primary"),
735: actionButton("composemat", "Compose the matrix", icon = icon("spinner"), class = "btn btn-primary"),
818: actionButton("updatepreview_button", "Update report", class = "btn btn-primary"), p()
820: column(3, downloadButton("saveRmd", "Generate & Save", class = "btn btn-success"))
971: class = "btn btn-primary", icon = icon("spinner")),
974: class = "btn btn-primary", icon = icon("spinner")),
977: class = "btn btn-primary", icon = icon("spinner"))
1130: actionButton(inputId = "show_cm", label = HTML("Show </br>count matrix"), class = "btn btn-success")
1139: actionButton(inputId = "show_metadata", label = HTML("Show </br>sample metadata"), class = "btn btn-success")
1148: actionButton(inputId = "show_dds", label = HTML("Show </br><code>dds</code> object"), class = "btn btn-success")
1157: actionButton(inputId = "show_annotation", label = HTML("Show </br>gene annotation"), class = "btn btn-success")
1207: actionButton("button_diydds", label = HTML("Generate the dds and </br>dst objects"), class = "btn btn-success"),
2008: actionButton("computepca2go", "Compute the PCA2GO object", icon = icon("spinner"), class = "btn btn-primary")
2140: class(input$pc_x)
2142: class(datatable(values$mypca2go[[paste0("PC", input$pc_x)]][["posLoad"]]))
2687: class = "foot-inner",
31: #' dds_airway <- DESeq2::DESeqDataSetFromMatrix(assay(airway),
1239: values$mydds <- DESeqDataSetFromMatrix(countData = values$mycountmatrix,
1295: values$mydds <- DESeqDataSetFromMatrix(countData = values$mycountmatrix,

ISAnalytics:R/utility-functions.R: [ ]
39: purrr::walk(desc_msg, ~ rlang::inform(.x, class = "tag_inspect"))
494: rlang::inform(warn_msg, class = "missing_quant_specs")
637: rlang::inform(err_msg, class = "skip_col_transform")
736: rlang::inform(.nas_introduced_msg(), class = "comp_nas")
963: rlang::inform(msg, class = "launch_af_empty")
1172: rlang::inform(no_stats_msg, class = "no_stats_warn")
504: rlang::abort(err_msg, class = "miss_annot_suff_specs")
516: rlang::inform("Matrix suffixes specs successfully changed")
532: rlang::inform("Matrix suffixes specs reset to default")
1300: rlang::inform("Matrix suffixes specs successfully changed")

spatialHeatmap:inst/extdata/shinyApp/R/global.R: [ ]
301: lapply(ft, function(i) { tag("span", list(class = class(i), tags$span(class = "glyphicon glyphicon-move"), i)) }
310: span(class = "panel panel-default",
311: div(class = "panel-heading", x),
312: div(class = "panel-body", id = ns(x))
317: span(class = "panel panel-default",
318: div(class = "panel-heading", names(x)),
319: div(class = "panel-body", id = ns(names(x)), ft2tag(x[[1]]))
170: if (is(input, 'dgCMatrix')\|is(input, 'matrix')) input <- as.data.frame(as.matrix(input))

GRaNIE:R/plot.R: [ ]
990: dplyr::mutate(class = paste0("real_",range))) %>%
2388: dplyr::mutate(Class = dplyr::if_else(isTF, "TF", "gene"))
3114: dplyr::mutate(class = droplevels(factor(class, levels = c("TF", "peak (TF end)", "peak (gene end)", "gene")))) %>% # in case it was the TF-gene d...(50 bytes skipped)...
1002: class_levels = c(paste0("real_",range), paste0("random_",range))
1029: peak_gene.p.raw.class = cut(peak_gene.p_raw, breaks = seq(0,1,0.05), include.lowest = TRUE, ordered_result = TRUE),
1030: peak_gene.r.class = cut(peak_gene.r, breaks = seq(-1,1,0.05), include.lowest = TRUE, ordered_result = TRUE)) %>%
1048: colors_class = c("black", "black")
1052: r_pos_class = c("black", "darkgray")
1055: dist_class = c("dark red", "#fc9c9c")
1059: freq_class = paste0(gsub(names(freqs), pattern = "(.+)(_.*)", replacement = "\\1"), " (n=", .prettyNum(freqs) ...(6 bytes skipped)...
1065: xlabels_peakGene_r.class = levels(peakGeneCorrelations.all$peak_gene.r.class)
1074: xlabels_peakGene_praw.class = levels(peakGeneCorrelations.all$peak_gene.p.raw.class)
1157: customLabel_class = .customLabeler(table(peakGeneCorrelations.all[indexCur,]$class))
1027: peak_gene.distance_class_abs = forcats::fct_explicit_na(addNA(cut(abs(peak_gene.distance),
1070: xlabels_peakGene_r.class2 = levels(peakGeneCorrelations.all$peak_gene.r.class)
1432: dplyr::mutate(peak_gene.distance.class250k = factor(dplyr::if_else(peak_gene.distance <= 250000, "<=250k", ">250k"))) %>%
1469: distance_class_abund = table(peakGeneCorrelations.all[indexCur,]$peak_gene.distance_class_abs)
571: GC_classes_background_GC.df = peaksBackgroundGC %>%
579: GC_classes_all2.df = rbind(GC_classes_foreground.df, GC_classes_background.df, GC_classes_background_GC.df) %>%
1021: nClasses_distance = 10
5: #' Produce a PCA plot of the data from a \code{\linkS4class{GRN}} object
18: #' @return The same \code{\linkS4class{GRN}} object, without modifications. In addition, for each specified \code{type}, a PDF file is pro...(81 bytes skipped)...
351: # data: Matrix of your data, with column names. May or may not be vsd transformed, log2 transformed etc
470: #' Plot diagnostic plots for TF-peak connections for a \code{\linkS4class{GRN}} object
485: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
572: dplyr::group_by(GC_class) %>%
575: tidyr::complete(GC_class, fill = list(n = 0)) %>%
581: dplyr::left_join(GC_classes_all.df, by = "GC_class") %>%
595: g1 = ggplot(GC_classes_all2.df , aes(GC_class, n_rel, group = .data$type, fill = .data$type, label = n.bg.needed.relFreq)) +
603: xlab("GC class from peaks") +
608: g2 = ggplot(GC_classes_all2.df , aes(GC_class, log10(.data$n+1), group = .data$type, fill = .data$type)) +
614: xlab("GC class from peaks") +
858: #' Plot diagnostic plots for peak-gene connections for a \code{\linkS4class{GRN}} object
872: #' @return The same \code{\linkS4class{GRN}} object, with added data from this function.
992: dplyr::mutate(class = paste0("random_",range)))
1007: dplyr::mutate(class = factor(paste0("real_",range), levels = class_levels)),
1009: dplyr::mutate(class = factor(paste0("random_",range), levels = class_levels))) %>%
1025: peak_gene.distance_class =
1035: levels(peakGeneCorrelations.all$peak_gene.distance_class_abs)[1] =
1036: gsub("(-\\d+)", "0", levels(peakGeneCorrelations.all$peak_gene.distance_class_abs)[1], perl = TRUE)
1041: dplyr::mutate(peak_gene.p_raw.robust.class =
1049: names(colors_class)= unique(peakGeneCorrelations.all$class)
1050: colors_class[which(grepl("random", names(colors_class)))] = "darkgray"
1053: names(r_pos_class) =c("TRUE", "FALSE")
1056: names(dist_class) = class_levels
1058: freqs= table(peakGeneCorrelations.all$class)
1061: freq_class = gsub(freq_class, pattern = "random", replacement = "permuted")
1062: names(freq_class) <- names(freqs)
1066: nCur = length(xlabels_peakGene_r.class)
1067: xlabels_peakGene_r.class[setdiff(seq_len(nCur), c(1, floor(nCur/2), nCur))] <- ""
1071: nCur = length(xlabels_peakGene_r.class2)
1072: xlabels_peakGene_r.class2[setdiff(seq_len(nCur), c(1, floor(nCur/4), floor(nCur/2), floor(nCur/4*3), nCur))] <- ""
1075: nCur = length(xlabels_peakGene_praw.class)
1076: xlabels_peakGene_praw.class[setdiff(seq_len(nCur), c(1, floor(nCur/2), nCur))] <- ""
1146: indexCurReal = intersect(indexCur, which(peakGeneCorrelations.all$class == names(dist_class)[1]))
1156: # Produce the labels for the class-specific subtitles
1171: facet_wrap(~ class, labeller = labeller(class=freq_class) ) +
1173: scale_color_manual(labels = names(r_pos_class), values = r_pos_class) +
1183: xlabels = levels(tbl.l$d_merged$peak_gene.p.raw.class)
1186: gB3 = ggplot(tbl.l$d_merged, aes(peak_gene.p.raw.class, ratio, fill = classAll)) +
1190: scale_fill_manual("Class", values = c(dist_class, r_pos_class),
1193: scale_x_discrete(labels = xlabels_peakGene_praw.class) +
1207: sum_real = table(peakGeneCorrelations.all[indexCur,]$class)[names(dist_class)[1]]
1208: sum_rnd = table(peakGeneCorrelations.all[indexCur,]$class)[names(dist_class)[2]]
1210: dplyr::group_by(class) %>%
1211: dplyr::count(peak_gene.r.class) %>%
1212: dplyr::mutate(nnorm = dplyr::case_when(class == !! (names(dist_class)[1]) ~ .data$n / (sum_real / sum_rnd),
1217: gD = ggplot(binData.r, aes(peak_gene.r.class, nnorm, group = class, fill = class)) +
1219: geom_line(aes(peak_gene.r.class, nnorm, group = class, color= class), stat = "identity") +
1220: scale_fill_manual("Group", labels = names(dist_class), values = dist_class) +
1221: scale_color_manual("Group", labels = names(dist_class), values = dist_class) +
1222: scale_x_discrete(labels = xlabels_peakGene_r.class2, drop = FALSE) +
1269: allVars = c("peak.GC.class", "peak.width", "peak.mean","peak.median",
1283: dplyr::select(peak_gene.p_raw, tidyselect::all_of(varCur), class, r_positive, peak_gene.p.raw.class, peak_gene.distance)
1286: dplyr::select(peak_gene.p_raw, class, gene.CV, peak.CV, r_positive, peak_gene.p.raw.class, peak_gene.distance)
1292: if (varCur %in% c("peak.annotation","peak.GC.class")) {
1298: newColName = paste0(varCur, ".class")
1367: # Class in the facet_wrap has been removed, as this is only for real data here
1378: dplyr::group_by(class, .data[[newColName]], peak_gene.p.raw.class, r_positive) %>%
1381: tidyr::complete(class, .data[[newColName]], peak_gene.p.raw.class, r_positive, fill = list(n = 0)) %>% # SOme cases might be missing
1382: dplyr::group_by(class, .data[[newColName]], peak_gene.p.raw.class) %>% # dont group by r_positive because we want to calculate the ratio within each group
1386: dplyr::filter(r_positive, class == names(dist_class)[1])# Keep only one r_positive row per grouping as we operate via the ratio and this data is duplic...(129 bytes skipped)...
1392: gB3 = ggplot(freq, aes(peak_gene.p.raw.class, ratio_pos_raw, fill = .data[[newColName]])) +
1396: scale_x_discrete(labels = xlabels_peakGene_praw.class) +
1402: facet_wrap(~ factor(class), nrow = 2, scales = "free_y", strip.position = "left", labeller = labeller(class=freq_class))
1446: facet_wrap(~ peak_gene.distance.class250k + .data[[newColName]], nrow = nrows_plot, scales = "free_y") +
1449: scale_fill_manual("Class for r", values = mycolors, labels = r_positive_label, drop = FALSE ) +
1470: indexFilt = which(peakGeneCorrelations.all$peak_gene.distance_class_abs %in%
1471: names(distance_class_abund)[which(distance_class_abund > 50)])
1477: ...(11 bytes skipped)... g = ggplot(peakGeneCorrelations.all[indexFilt,], aes(peak_gene.p_raw, color = peak_gene.distance_class_abs)) + geom_density() +
1480: scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$peak_gene.distance_class_abs))) +
1490: ...(20 bytes skipped)...ggplot(peakGeneCorrelations.all[indexFilt,], aes(peak_gene.p_raw.robust, color = peak_gene.distance_class_abs)) + geom_density() +
1493: scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$peak_gene.distance_class_abs))) +
1513: facet_wrap(~ peak_gene.distance_class_abs, ncol = 2, labeller = .customLabeler(table(peakGeneCorrelations.all$peak_gene.distance_class_abs))) +
1514: scale_color_manual(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$r_positive)), values = r_pos_class) +
1529: ...(2 bytes skipped)... g = ggplot(peakGeneCorrelations.all[indexCur,], aes(peak_gene.r, color = peak_gene.distance_class_abs)) + geom_density() +
1532: scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexCur,]$peak_gene.distance_class_abs))) +
1570: #' Plot various network connectivity summaries for a \code{\linkS4class{GRN}} object
1581: #' @return The same \code{\linkS4class{GRN}} object, without modifications. In addition, for the specified \code{type}, a PDF file (defaul...(93 bytes skipped)...
1907: #' Plot general structure and connectivity statistics for a filtered \code{\linkS4class{GRN}}
1918: #' @return The same \code{\linkS4class{GRN}} object with no changes. The results are output to a file.
1960: totalVerteces = data.frame(Class = c("TF", "Peak", "Gene"),
1975: gVertexDist = ggplot(totalVerteces, aes(x="", y=Count, fill=Class)) + geom_bar(stat="identity") +
2133: #' @return The same \code{\linkS4class{GRN}} object, without modifications. A single PDF file is produced with the results.
2291: #' Plot general structure & connectivity statistics for each community in a filtered \code{\linkS4class{GRN}}
2307: #' @return The same \code{\linkS4class{GRN}} object, without modifications. A single PDF file is produced with the statistics.
2394: # Class: TF or gene
2395: gCommunityVertices = ggplot(communityVertices, aes(x = community, fill = Class)) +
2502: #' @return The same \code{\linkS4class{GRN}} object, without modifications. A single PDF file is produced with the results.
2832: #' @return The same \code{\linkS4class{GRN}} object, without modifications. A single PDF file is produced with the results.
3113: .id = "class") %>%
3115: dplyr::arrange(class, dplyr::desc(Degree))
3120: gDegrees = ggplot(degrees.table, aes(x=Degree, fill = class)) +
3126: facet_wrap(~class, labeller = labeller(class=.facetLabel(degrees.table)), scales = "free", ncol = 2) +
3131: dplyr::filter(class =="gene") %>%
3136: dplyr::filter(class =="TF") %>%
3243: dplyr::group_by(class) %>%
3245: dplyr::mutate(label = paste0(class, "\n(mean: ", round(mean, 1),
3251: names(labels) <- summary$class
31: checkmate::assertClass(GRN, "GRN")
56: transformation = dplyr::if_else(checkmate::testClass(GRN@data$RNA$counts_orig, "DESeqDataSet"), "vst", "log2")
112: transformation = dplyr::if_else(checkmate::testClass(GRN@data$peaks$counts_orig, "DESeqDataSet"), "vst", "log2")
169: checkmate::assertClass(counts, "DESeqDataSet")
175: checkmate::assertMatrix(counts)
179: checkmate::assertMatrix(counts)
502: checkmate::assertClass(GRN, "GRN")
569: .generateTF_GC_diagnosticPlots <- function(TFCur, GC_classes_foreground.df, GC_classes_background.df, GC_classes_all.df, peaksForeground, peaksBackground, peaksBackgroundGC) {
586: GC_classes_all2.df$n.bg.needed.relFreq[GC_classes_all2.df$type != "background_GC" \| GC_classes_all2.df$n == 0] = ""
892: checkmate::assertClass(GRN, "GRN")
1370: geom_density(aes(color = classNew), color = "black", linetype = "dotted", alpha = 1) +
1447: xlab(xlabel) + ylab(paste0("Abundance for classes with n>=", nGroupsMin)) + theme_bw() +
1466: # Here, we focus on distance and exclude distance classes with too few points and create a new subset of the data
1468: # Filter distance classes with too few points
1595: checkmate::assertClass(GRN, "GRN")
1934: checkmate::assertClass(GRN, "GRN")
2148: checkmate::assertClass(GRN, "GRN")
2324: checkmate::assertClass(GRN, "GRN")
2519: checkmate::assertClass(GRN, "GRN")
2853: checkmate::assertClass(GRN, "GRN")
1026: forcats::fct_explicit_na(addNA(cut(peak_gene.distance, breaks = nClasses_distance, include.lowest = TRUE)), "random"),
1028: breaks = nClasses_distance, include.lowest = TRUE, ordered_result = TRUE)), "random"),

debrowser:R/funcs.R: [ ]
278: tags$button(id = inputId, type = "button", class = paste("btn action-button",
264: btn.css.class <- paste("btn", styleclass, sep = "-")
268: btn.size.class <- paste("btn", size, sep = "-")
299: getNormalizedMatrix <- function(M = NULL, method = "TMM") {
246: #' @param styleclass The Bootstrap styling class of the button--options are
251: #' @param css.class Any additional CSS class one wishes to add to the action
261: block = FALSE, icon = NULL, css.class = "", ...) {
265: } else btn.css.class = ""
269: } else btn.size.class = ""
276: icon.code <- HTML(paste0("<i class='fa fa-", icon, "'></i>"))
279: btn.css.class, btn.size.class, btn.block, css.class, collapse = " "),
486: if($('#dataprepMenu').attr('class')!='active'){
491: if($('#dataprepMethod').attr('class')!='active'){
497: if($('#discoveryMenu').attr('class')!='active'){
181: actionButtonDE(paste0("show",tablename), "Show Data", styleclass = "primary", icon="show"),
260: actionButtonDE <- function(inputId, label, styleclass = "", size = "",
262: if (styleclass %in% c("primary", "info", "success", "warning",
284: #' getNormalizedMatrix
289: #' @note \code{getNormalizedMatrix}
295: #' x <- getNormalizedMatrix(mtcars)
313: dds <- DESeqDataSetFromMatrix(countData = as.matrix(M),
355: styleclass="info", size="small")

HiCBricks:R/backend_functions.R: [ ]
467: Matrix <- NULL
600: Matrix <- as.matrix(fread(file = Matrix.file, sep=delim, nrows=Iter,
374: Matrix.return <- rbind(Matrix.top,Matrix.bottom)
460: Matrix.range <- c(NA,NA)
563: Matrix.range <- c(NA,NA)
1: GenomicMatrix <- R6Class("GenomicMatrix",
548: ._ProcessMatrix_ <- function(Brick = NULL, Matrix.file = NULL, delim = NULL,
150: Matrix.range=NA,
357: ._Do_rbind_on_matrices_of_different_sizes_ <- function(Matrix.top = NULL,
358: Matrix.bottom = NULL, row.length = NULL, col.length = NULL,
360: if(is.null(Matrix.top)){
361: return(Matrix.bottom)
365: Matrix.top <- cbind(Matrix.top,matrix(NA,
366: nrow = nrow(Matrix.top), ncol = Makeup.col))
370: Matrix.bottom <- cbind(matrix(NA,
371: nrow = nrow(Matrix.bottom),
372: ncol = Makeup.col),Matrix.bottom)
375: return(Matrix.return)
388: ._Compute_various_matrix_metrics <- function(Matrix = NULL,
392: Bin.coverage <- vapply(seq_len(nrow(Matrix)),function(x){
393: Vec.sub <- Matrix[x,]
396: Row.sums <- vapply(seq_len(nrow(Matrix)),function(x){
397: Vec.sub <- Matrix[x,]
401: Sparsity.Index <- vapply(seq_len(nrow(Matrix)),function(x){
402: Vec.sub <- Matrix[x,]
411: Row.extent <- ._Do_on_vector_ComputeMinMax_(Matrix)
422: ._Compute_various_col_matrix_metrics <- function(Matrix = NULL,
424: Matrix[is.na(Matrix) \| is.infinite(Matrix)] <- 0
426: metrics.list[["bin.coverage"]] + colSums(Matrix > 0)
428: colSums(Matrix)
446: ._Process_matrix_by_distance <- function(Brick = NULL, Matrix.file = NULL,
454: Handler <- .create_file_connection(Filename = Matrix.file, mode = "r")
473: if(is.null(Matrix)){
478: Matrix <- matrix(data = 0, nrow = num.rows,
485: Matrix[Row.loc, Col.loc] <- Vector[Col.lower.limit:Col.upper.limit]
489: Count <- c(nrow(Matrix),ncol(Matrix))
490: Metrics.list <- ._Compute_various_matrix_metrics(Matrix = Matrix,
492: sparsity.bins = sparsity.bins, range = Matrix.range,
494: Matrix.range <- Metrics.list[["extent"]]
504: data = Matrix, Start = Start, Stride = Stride, Count = Count)
507: Object.size <- object.size(Matrix)
508: Matrix <- NULL
538: Attr.vals <- c(basename(Matrix.file),as.double(Matrix.range),
605: Metrics.list <- ._Compute_various_matrix_metrics(Matrix = Matrix,
607: range = Matrix.range, distance = distance,
610: Matrix = Matrix,
612: Matrix.range <- Metrics.list[["extent"]]
617: Cumulative.data <- rbind(Cumulative.data,Matrix)
661: Attr.vals <- c(basename(Matrix.file),
662: as.double(Matrix.range),
91: TerrificNumberOfHiCFormats = c("NxNMatrix","PAIRIX","Cooler","HOMER",
230: Reference.object <- GenomicMatrix$new()
255: Reference.object <- GenomicMatrix$new()
309: Reference.object <- GenomicMatrix$new()
349: Reference.object <- GenomicMatrix$new()
450: Reference.object <- GenomicMatrix$new()
552: Reference.object <- GenomicMatrix$new()
678: Reference.object <- GenomicMatrix$new()
772: # Reference.object <- GenomicMatrix$new()
781: # Reference.object <- GenomicMatrix$new()
787: # Reference.object <- GenomicMatrix$new()

pRolocGUI:R/pRolocVis_explore.R: [ ]
538: ui <- tags$body(class="skin-blue right-sidebar-mini control-sidebar-open",
919: profByClass <- plotFacetProfiles(profs, fcol, fd, pd, col = cols_user())
132: getMyClasses <- getMarkerClasses(object, fcol = fcol)
220: myclasses <- colnames(pmarkers)
22: ##' individual class profile plots should be displayed. Default is \code{FALSE}.
56: else stop(paste("Object must be of class MSnSet or matrix"))
84: # stop(paste("Matrix rownames and feature names don't match"))
254: or add the class labels on the spatial map click
256: name. All class labels can be added back to the plot
346: tabPanel("Profiles (by class)", value = "profilesPanel2",
451: # tabPanel("Profiles (by class)", value = "profilesPanel2",
566: ## Get coords for proteins according to selectized marker class(es)
703: ## Class specific/faceted plots
997: # addClass(selector = "body", class = "sidebar-collapse")
998: # removeClass(selector = "body", class = "control-sidebar-open")
1000: # removeClass(selector = "body", class = "sidebar-collapse")
1001: # addClass(selector = "body", class = "control-sidebar-open")
1004: # observeEvent(input$openright, {addClass(selector = "body", class = "control-sidebar-open")})
21: ##' @param classProfiles A \code{logical} indicating if a tab displaying
25: classProfiles = FALSE,
245: p(strong("Subcellular classes")),
250: buttonLabel = "classes",
253: belong to pre-defined subcellular classes. To remove
278: if (classProfiles) {
653: ## get quantiles for subcellular classes
654: ...(12 bytes skipped)...fs <- lapply(mrkSel(), function(z) profs[z, , drop = FALSE]) # 5% and 95% quantiles for all other classes
704: if (classProfiles) {
863: ## get quantiles for subcellular classes
864: ...(2 bytes skipped)... mrkProfs <- lapply(mrkSel(), function(z) profs[z, ]) # 5% and 95% quantiles for all other classes
920: ggsave(filename = file, plot = profByClass, device = "pdf", width = w, height = h)
133: cn <- sapply(getMyClasses,
144: diffNam1 <- setdiff(getMyClasses, cn)
145: diffNam2 <- setdiff(cn, getMyClasses)
221: colPicker <- function(x) {colourpicker::colourInput(col_ids[x], myclasses[x], value = appStockcol()[x])}
576: names(cols_user) <- myclasses

qmtools:R/reduceFeatures-functions.R: [ ]
113: res <- pcaMethods::nipalsPca(Matrix = x, nPcs = ncomp, ...)
48: ##' \linkS4class{SummarizedExperiment}-friendly wrapper for this function.
95: class(out) <- c("reduced.pca", class(out))
170: ##' \linkS4class{SummarizedExperiment}-friendly wrapper for this function.
214: class(out) <- c("reduced.tsne", class(out))
271: ##' \linkS4class{SummarizedExperiment}-friendly wrapper for this function.
331: class(out) <- c("reduced.plsda", "matrix", class(out))
223: ##' This function performs standard PLS for classification with the transpose of

fastreeR:R/tree2clusters.R: [ ]
58: class="ciat/agrobio/hcluster/HierarchicalCluster",
59: class.loader = .rJava.class.loader
90: distancesMatrix <- ape::cophenetic.phylo(tree)
92: distancesMatrix <- distancesReordered
95: rownames(distancesMatrix) <- rawLabels
96: colnames(distancesMatrix) <- colLabels
103: distM = distancesMatrix, deepSplit = 1, verbose = 2, indent = 0
109: distM = distancesMatrix, deepSplit = 1, maxCoreScatter = NULL,
119: treecut.hybrid, rownames(distancesMatrix)

metaseqR2:R/main.R: [ ]
2745: invisible(knitr::knit_meta(class=NULL,clean=TRUE))
1557: tempMatrix <- round(counts(normGenes,normalized=TRUE))
1586: classes <- asClassVector(sampleList)
1554: switch(class(normGenes)[1],
1585: # Dribble for taking a mtx out of SeqCountSet class
1636: switch(class(normGenes)[1],
1873: switch(class(normGenesExpr)[1],
1556: #tempMatrix <- round(DESeq::counts(normGenes,normalized=TRUE))
1560: tempMatrix <- round(DESeq2::counts(normGenes,normalized=TRUE))
1566: tempMatrix <- round(t(t(normGenes$counts)/scl)*mean(scl))
1569: tempMatrix <- normGenes
1572: tempMatrix <- as.matrix(normGenes)
1575: tempMatrix <- as.matrix(round(sweep(normGenes$counts,2,
1579: tempMatrix <- as.matrix(round(normGenes$pseudo.counts))
1582: tempMatrix <- as.matrix(round(excounts(normGenes)))
1587: theDesign <- data.frame(condition=classes,
1593: #tempMatrix <- as.matrix(round(DESeq::counts(cds,
1595: tempMatrix <- as.matrix(round(counts(cds,normalized=TRUE)))
1601: geneFilterOut <- filterGenes(tempMatrix,geneData,geneFilters,
1629: geneCountsDead <- tempMatrix[theDead,]
1635: theDeadInd <- match(theDead,rownames(tempMatrix))
1686: classes <- asClassVector(sampleList)
1687: theDesign <- data.frame(condition=classes,
1911: classes <- asClassVector(sampleList)
1912: theDesign <- data.frame(condition=classes,

hipathia:R/stats.R: [ ]
212: class <- "0"
354: class <- "0"
391: class <- "0"
51: #' @return Matrix of gene expression whose values are in [0,1].
218: class <- "DOWN" ## regarding DISEASE
220: class <- "UP" ## regarding DISEASE
223: class <- "UP"
225: class <- "DOWN"
227: class <- 0
231: result <- data.frame(pvalue, class, esti, stringsAsFactors = FALSE)
244: #' including the classes to compare, or a character vector with the class to
338: data2$class == "0" &
360: class <- "UP"
362: class <- "DOWN"
364: class <- "0"
368: class = class,
398: class <- "DOWN" ## regarding DISEASE
400: class <- "UP" ## regarding DISEASE
403: class <- "UP"
405: class <- "DOWN"
407: class <- 0
411: result <- data.frame(pvalue, class, stat,stringsAsFactors=FALSE)
427: #' @return \code{do_pca} returns a list with class \code{princomp}.
77: stop("Only SummarizedExperiment or matrix classes accepted as data")
288: stop("Only SummarizedExperiment or matrix classes accepted as data")
443: stop("Only SummarizedExperiment or matrix classes accepted as data")
469: stop("Only SummarizedExperiment or matrix classes accepted as data")

pcaMethods:R/pca.R: [ ]
122: Matrix <- as.matrix(object[,num])
125: Matrix <- t(exprs(object))
127: Matrix <- as.matrix(object, rownames.force=TRUE)
130: Matrix <- Matrix[,subset]
135: if (nPcs > ncol(Matrix)) {
137: nPcs <- min(dim(Matrix))
139: if (nPcs > nrow(Matrix)) {
141: nPcs <- min(dim(Matrix))
144: if (!checkData(Matrix, verbose=interactive()))
148: missing <- is.na(Matrix)
162: prepres <- prep(Matrix, scale=scale, center=center, simple=FALSE, ...)
196: rownames(res@scores) <- rownames(Matrix)
197: if(all(dim(loadings(res)) == c(ncol(Matrix), nPcs))) {
199: rownames(res@loadings) <- colnames(Matrix)
205: res@nObs <- nrow(Matrix)
206: res@nVar <- ncol(Matrix)
217: cObs <- Matrix
219: cObs[missing] <- fitted(res, Matrix, pre=TRUE, post=TRUE)[missing]
225: res@cvstat <- Q2(res, Matrix, nruncv=1, ...)
344: ##' @param Matrix Pre-processed (centered and possibly scaled)
364: svdPca <- function(Matrix, nPcs=2,
367: pcs <- prcomp(Matrix, center=FALSE, scale.=FALSE)
4: ##' methods based on the classical model where the fitted data is a
31: ##' \describe{\item{svd:}{Uses classical \code{prcomp}. See

HPiP:R/FSmethod.R: [ ]
185: x$class
2: function(features, class, cor.cutoff = 0.7) {
23: cbind(class, .)
33: .rfeFS <- function(features, class, cor.cutoff = 0.7,
49: y = as.factor(class),
68: dataset_rfe <- cbind(class, dataset_rfe)
79: #' @title Feature Selection via Matrix Correlation and
82: #' class labels and features.
164: if (all(colnames(x) != "class") == TRUE) {
165: stop("class attribute is absent from the data.frame")
182: dplyr::select(-PPI, -class)
184: class <-
188: result <- .filter.corr(features, class, cor.cutoff)
190: result <- .rfeFS(features, class,
197: .filter.corr(features, class, cor.cutoff)
204: dplyr::select(-PPI, -class)
206: df.class <-
207: df$class
210: .rfeFS(df.features, df.class,
42: caretFuncs$summary <- twoClassSummary
129: #' @importFrom caret twoClassSummary

splatter:R/SCE-functions.R: [ ]
312: class <- ifelse(is.logical(mat), "lgCMatrix", "dgCMatrix")
313: as(mat, class)
338: warning("matrix '", mat.name, "' is class '", class(mat),
28: checkmate::assertClass(sce, "SingleCellExperiment")
53: if (is(values, "dgCMatrix")) {
126: checkmate::assertClass(sce, "SingleCellExperiment")
160: checkmate::assertClass(sce, "SingleCellExperiment")
329: } else if (is(mat, "dgCMatrix")) {
332: "' as it is already a dgCMatrix")
349: mat <- as(mat, "lgCMatrix")
351: mat <- as(mat, "dgCMatrix")

tomoseqr:R/masker.R: [ ]
114: tags$div(class = "table_button",
130: tags$div(class="tmp_canvas",
156: dataMatrix = data_matrix,
170: dataMatrix = data_matrix,
183: dataMatrix = data_matrix,
210: dataMatrix = data_matrix,
223: dataMatrix = data_matrix,
235: dataMatrix = data_matrix,

interactiveDisplay:R/ExpressionSet.R: [ ]
9: shiny::tags$input(id = inputId1, class = "color", value = "EDF8B1",
16: shiny::tags$input(id = inputId2, class = "color", value = "7FCDBB",
23: shiny::tags$input(id = inputId3, class = "color", value = "2C7FB8",
252: if(class(pkg)=="ChipDb"){
285: if(class(pkg)=="ChipDb"){
483: "<div id=\"net\" class=\"shiny-network-output\"><svg /></div>",
554: # Distance Matrix

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

DiscoRhythm:inst/app/code/ui/rowReplicateAnalysis.R: [ ]
66: ", class = "text-muted")
39: p(class="text-muted","Results for this section were
80: class = "text-muted")
91: title = "Inspect Final Averaged Data Matrix",
102: class = "text-muted"
89: id = "rowRepMatrixDiv",

HDF5Array:R/H5SparseMatrixSeed-class.R: [ ]
276: ans_class <- "CSC_H5SparseMatrixSeed"
656: .from_CSC_H5SparseMatrixSeed_to_dgCMatrix <- function(from)
672: .from_CSR_H5SparseMatrixSeed_to_dgCMatrix <- function(from)
61: t.CSC_H5SparseMatrixSeed <- function(x)
70: t.CSR_H5SparseMatrixSeed <- function(x)
255: H5SparseMatrixSeed <- function(filepath, group, subdata=NULL)
482: .extract_array_from_H5SparseMatrixSeed <- function(x, index)
526: .extract_sparse_array_from_H5SparseMatrixSeed <- function(x, index)
549: .read_sparse_block_from_H5SparseMatrixSeed <- function(x, viewport)
64: class(x) <- "CSR_H5SparseMatrixSeed"
73: class(x) <- "CSC_H5SparseMatrixSeed"
95: stop(wmsg("changing the path of a ", class(object), " object ",
282: ans_class <- "CSR_H5SparseMatrixSeed"
296: new2(ans_class, filepath=filepath, group=group,
6: setClass("H5SparseMatrixSeed",
52: setClass("CSC_H5SparseMatrixSeed", contains="H5SparseMatrixSeed")
53: setClass("CSR_H5SparseMatrixSeed", contains="H5SparseMatrixSeed")
206: msg2 <- c("H5ADMatrix() constructor if you are trying ",
653: ### Coercion to dgCMatrix
661: sparseMatrix(i=row_indices, p=indptr, x=data, dims=dim(from),
665: setAs("CSC_H5SparseMatrixSeed", "dgCMatrix",
666: .from_CSC_H5SparseMatrixSeed_to_dgCMatrix
668: setAs("CSC_H5SparseMatrixSeed", "sparseMatrix",
669: .from_CSC_H5SparseMatrixSeed_to_dgCMatrix
677: sparseMatrix(j=col_indices, p=indptr, x=data, dims=dim(from),
681: setAs("CSR_H5SparseMatrixSeed", "dgCMatrix",
682: .from_CSR_H5SparseMatrixSeed_to_dgCMatrix
684: setAs("CSR_H5SparseMatrixSeed", "sparseMatrix",
685: .from_CSR_H5SparseMatrixSeed_to_dgCMatrix
2: ### H5SparseMatrixSeed objects
33: ## ------------- populated by specialized subclasses -------------
60: ### S3/S4 combo for t.CSC_H5SparseMatrixSeed
67: setMethod("t", "CSC_H5SparseMatrixSeed", t.CSC_H5SparseMatrixSeed)
69: ### S3/S4 combo for t.CSR_H5SparseMatrixSeed
76: setMethod("t", "CSR_H5SparseMatrixSeed", t.CSR_H5SparseMatrixSeed)
84: setMethod("path", "H5SparseMatrixSeed", function(object) object@filepath)
88: setReplaceMethod("path", "H5SparseMatrixSeed",
108: setMethod("dim", "H5SparseMatrixSeed", function(x) x@dim)
110: setMethod("dimnames", "H5SparseMatrixSeed",
253: ### Returns an H5SparseMatrixSeed derivative (can be either a
254: ### CSC_H5SparseMatrixSeed or CSR_H5SparseMatrixSeed object).
365: ### H5SparseMatrixSeed objects.
461: setMethod(".load_sparse_data", "CSC_H5SparseMatrixSeed",
469: setMethod(".load_sparse_data", "CSR_H5SparseMatrixSeed",
495: setMethod("extract_array", "H5SparseMatrixSeed",
496: .extract_array_from_H5SparseMatrixSeed
505: setMethod("chunkdim", "CSC_H5SparseMatrixSeed", function(x) c(nrow(x), 1L))
507: setMethod("chunkdim", "CSR_H5SparseMatrixSeed", function(x) c(1L, ncol(x)))
514: setMethod("sparsity", "H5SparseMatrixSeed",
524: setMethod("is_sparse", "H5SparseMatrixSeed", function(x) TRUE)
532: setMethod("extract_sparse_array", "H5SparseMatrixSeed",
533: .extract_sparse_array_from_H5SparseMatrixSeed
537: ### work just fine on an H5SparseMatrixSeed derivative (thanks to the
538: ### extract_sparse_array() method for H5SparseMatrixSeed objects defined
544: ### extract_sparse_array() method for H5SparseMatrixSeed objects would
553: ## Unlike the extract_sparse_array() method for H5SparseMatrixSeed
559: setMethod("read_sparse_block", "H5SparseMatrixSeed",
560: .read_sparse_block_from_H5SparseMatrixSeed
627: setMethod("extractNonzeroDataByCol", "CSC_H5SparseMatrixSeed",
642: setMethod("extractNonzeroDataByRow", "CSR_H5SparseMatrixSeed",
693: setMethod("show", "H5SparseMatrixSeed",

GenomicTuples:R/GTuples-class.R: [ ]
280: ans_class <- class(x[[1L]])
252: ### From GenomicRanges-class.R "For an object with a pure S4 slot
292: new(ans_class,
314: # NOTE: "c" will error if there is no common class, e.g.
318: # stop("Cannot combine ", paste0(unique(sapply(args, class)),
324: paste0(unique(vapply(args, class, character(1L))),
328: # "c" silently coerces to lowest common class, e.g., c(1, "next")
331: # if (!all(sapply(args, class) == class(args[[1]]))) {
333: # paste0("Not all elements are same class: ",
334: # paste0(unique(sapply(args, class)),
337: # "class: ",
454: stop("Cannot compute IPD from an empty '", class(x), "'.")
480: # (copied from GenomicRanges/GenomicRanges-class.R).
500: stop("replacement value must be a '", class(x), "' object")
647: cat(class(x), " object with ", lx, " x ",
653: cat(class(x), " with 0 tuples and 0 metadata columns:\n", sep = "")
9: setClass("GTuples",
253: ### representation, these both map to initialize. Reference classes will want
635: # the print.classinfo argument.
638: showGTuples <- function(x, margin = "", print.classinfo = FALSE,
640: if (!identical(print.classinfo, FALSE)) {
641: stop("'print.classinfo' not implemented")
6: setClassUnion(name = "matrixOrNULL", members = c("matrix", "NULL"))
270: #' @importClassesFrom S4Vectors DataFrame
637: #' @importFrom S4Vectors makePrettyMatrixForCompactPrinting
656: out <- makePrettyMatrixForCompactPrinting(x, .makeNakedMatFromGTuples)

DaMiRseq:R/Classif_2_Classes.R: [ ]
156: class_level <- levels(classes)
160: min_sample_each_class <- min(min_sub)
168: acc.Class<-matrix()
236: class_level2 <- levels(trainingSetClasses)
276: model_class <- list()
471: MCC.Class <- acc.Class
472: TP.Class <- acc.Class
473: TN.Class <- acc.Class
474: FP.Class <- acc.Class
475: FN.Class <- acc.Class
476: Sensit.Class <- acc.Class
477: Specif.Class <- acc.Class
478: PPV.Class <- acc.Class
479: NPV.Class <- acc.Class
486: TP_Class <- colSums(tPred[
488: TN_Class <- colSums(tPred[
490: FP_Class <- colSums(tPred[
492: FN_Class <- colSums(tPred[
183: independentClasses <- classes[index_indep, drop=FALSE]
188: datasetClasses <- classes[sample_index_list, drop=FALSE]
215: trainingSetClasses <- datasetClasses[sample_index_train,
223: testSetClasses <- datasetClasses[sample_index_test]
248: trainingSetClasses2 <- trainingSetClasses[sample_index_train2,
256: testSetClasses2 <- trainingSetClasses[sample_index_test2,
12: #' @param classes A class vector with \code{nrow(data)} elements.
13: #' Each element represents the class label for each observation.
14: #' Two different class labels are allowed
16: #' (but without 'class' column). Each column represents a different
72: #' # classes=df$class, fSample.tr=0.6, fSample.tr.w=0.6, iter=3,
154: # find the min number of sample per class
157: for (i in seq_len(length(class_level))){
158: min_sub[i] <- length(which(class_level[i]==classes))
161: if (min_sample_each_class < 3 )
172: for (i in seq_len(length(class_level))){
173: sample_index <- sample(which(class_level[i]==classes),replace = FALSE)
174: sample_index <- sample_index[seq_len(min_sample_each_class)]
198: acc.Class<- matrix()
204: class_level <- levels(datasetClasses)
205: tr_sample <- round(dim(dataset)[1]fSample.tr/length(class*_level))
206: for (i in seq_len(length(class_level))){
207: sample_index <- sample(which(class_level[i]==datasetClasses),
238: dim(trainingSet)[1]fSample.tr.w/length(class*_level2))
239: for (i in seq_len(length(class_level2))){
240: sample_index2 <- sample(which(class_level2[i]==trainingSetClasses),
291: model_class[[kk]] <- model_rf
316: model_class[[kk]] <- model_svm
331: model_class[[kk]] <- model_nb
341: predict(model_lda, testSet_DM)$class,
345: model_class[[kk]] <- model_lda
361: model_class[[kk]] <- model_lr
376: model_class[[kk]] <- model_nn
390: model_class[[kk]] <- model_pls
432: if(predict(model_lda,testSet[ii,])$class == levels(testSetClasses)[1])
470: acc.Class <- matrix(nrow=iter, ncol = length(colMeans(tPred)))
495: TP.Class[jj,] <- TP_Class
496: TN.Class[jj,] <- TN_Class
497: FP.Class[jj,] <- FP_Class
498: FN.Class[jj,] <- FN_Class
500: acc.Class[jj,] <- (TP_Class + TN_Class)/(
501: TP_Class + TN_Class + FP_Class + FN_Class)
503: MCC.Class[jj,] <- (TP_Class * TN_Class - FP_Class * FN_Class) /
504: sqrt((TP_Class + FP_Class) * (TP_Class + FN_Class) *
505: (TN_Class + FP_Class) * (TN_Class + FN_Class))
508: Sensit.Class[jj,] <- TP_Class / (TP_Class + FN_Class)
509: Specif.Class[jj,] <- TN_Class / (TN_Class + FP_Class)
512: PPV.Class[jj,] <- TP_Class / (TP_Class + FP_Class)
513: NPV.Class[jj,] <- TN_Class / (TN_Class + FN_Class)
517: MCC.Class[which(is.nan(MCC.Class))] <- 0
519: colnames(acc.Class) <- colnames(tPred)
520: colnames(MCC.Class) <- colnames(tPred)
521: colnames(Sensit.Class) <- colnames(tPred)
522: colnames(Specif.Class) <- colnames(tPred)
523: colnames(PPV.Class) <- colnames(tPred)
524: colnames(NPV.Class) <- colnames(tPred)
526: acc.Class<-round(acc.Class,2)
527: MCC.Class<-round(MCC.Class,2)
528: Sensit.Class<-round(Sensit.Class,2)
529: Specif.Class<-round(Specif.Class,2)
530: PPV.Class<-round(PPV.Class,2)
531: NPV.Class<-round(NPV.Class,2)
534: acc_dotplot <- melt(as.data.frame(acc.Class),
535: measure.vars = colnames(acc.Class))
549: coord_cartesian(ylim=c(min(acc.Class)-0.05,1))
553: mcc_dotplot <- melt(as.data.frame(MCC.Class),
554: measure.vars = colnames(MCC.Class))
568: coord_cartesian(ylim=c(min(MCC.Class)-0.05,1))
572: spe_dotplot <- melt(as.data.frame(Specif.Class),
573: measure.vars = colnames(Specif.Class))
588: coord_cartesian(ylim=c(min(Specif.Class)-0.05,1))
592: sen_dotplot <- melt(as.data.frame(Sensit.Class),
593: measure.vars = colnames(Sensit.Class))
608: coord_cartesian(ylim=c(min(Sensit.Class)-0.05,1))
613: ppv_dotplot <- melt(as.data.frame(PPV.Class),
614: measure.vars = colnames(PPV.Class))
629: coord_cartesian(ylim=c(min(PPV.Class)-0.05,1))
633: npv_dotplot <- melt(as.data.frame(NPV.Class),
634: measure.vars = colnames(NPV.Class))
649: coord_cartesian(ylim=c(min(NPV.Class)-0.05,1))
654: colnames(acc.Class),
656: "Mean:",round(colMeans(acc.Class),2),"\n","St.Dev.",
657: round(colSds(acc.Class),digits = 2),"\n")
660: colnames(MCC.Class),
662: "Mean:",round(colMeans(MCC.Class),2),"\n","St.Dev.",
663: round(colSds(MCC.Class),digits = 2),"\n")
666: colnames(Specif.Class),
668: "Mean:",round(colMeans(Specif.Class),2),"\n","St.Dev.",
669: round(colSds(Specif.Class),digits = 2),"\n")
672: colnames(Sensit.Class),
674: "Mean:",round(colMeans(Sensit.Class),2),"\n","St.Dev.",
675: round(colSds(Sensit.Class),digits = 2),"\n")
678: colnames(PPV.Class),
680: "Mean:",round(colMeans(PPV.Class),2),"\n","St.Dev.",
681: round(colSds(PPV.Class),digits = 2),"\n")
684: colnames(NPV.Class),
686: "Mean:",round(colMeans(NPV.Class),2),"\n","St.Dev.",
687: round(colSds(NPV.Class),digits = 2),"\n")
689: return(list(accuracy = acc.Class,
690: MCC = MCC.Class,
691: Specif = Specif.Class,
692: Sensit = Sensit.Class,
693: PPV = PPV.Class,
694: NPV = NPV.Class))
1: #' @title Build a Binary Classifier using 'Staking' Learning strategy.
7: #' classification model building. A 'two-classes' classification task is
22: #' @param iter Number of iterations to assess classification accuracy;
24: #' @param cl_type List of weak classifiers that will compose the
30: #' \item A matrix of accuracies of each classifier in each iteration.
31: #' \item A matrix of weights used for each classifier in each iteration.
47: #' Regression (LR) classifiers, whereas TS2 is used to test their accuracy
49: #' The decision rule of 'Stacking' classifier is made by a linear
51: #' product between weigths (w) and predictions (Pr) of each classifier;
57: #' Performance of 'Stacking' classifier is evaluated by using TS1. This
71: #' # Classification_res <- DaMiR.EnsembleLearning(selected_features,
79: classes,
95: if (missing(classes))
96: stop("'classes' argument must be provided")
110: if(!(is.factor(classes)))
111: stop("'classes' must be a factor")
124: stop("A Test Set is not available to weight classifiers.
126: if(length(classes) != dim(data)[1])
127: stop("length(classes) must be equal to dim(data)[1]")
192: cat("You select:",cl_type, "weak classifiers for creating
194: cat("Ensemble classification is running. ",
264: colnames(trainingSet_DM) <- c(varNames, "classes")
266: formula_DM <- as.formula(paste("classes", varNames1, sep = " ~ "))
270: colnames(testSet_DM) <- c(varNames, "classes")
274: ############### Weak classifiers ##################
286: acc_model[kk] <- caret::confusionMatrix(
287: table(predict(model_rf, testSet_DM),testSet_DM$classes),
288: reference = testSet_DM$classes)$overall['Accuracy']
311: acc_model[kk] <- caret::confusionMatrix(
313: reference = testSet_DM$classes)$overall['Accuracy']
326: acc_model[kk] <- caret::confusionMatrix(
328: reference = testSet_DM$classes)$overall['Accuracy']
340: acc_model[kk] <- caret::confusionMatrix(
342: reference = testSet_DM$classes)$overall['Accuracy']
356: acc_model[kk] <- caret::confusionMatrix(
358: reference = testSet_DM$classes)$overall['Accuracy']
371: acc_model[kk] <- caret::confusionMatrix(
373: reference = testSet_DM$classes)$overall['Accuracy']
385: acc_model[kk] <- caret::confusionMatrix(
387: reference = testSet_DM$classes)$overall['Accuracy']
397: acc_model[kk] <- caret::confusionMatrix(
399: reference = testSet_DM$classes)$overall['Accuracy']
537: colnames(acc_dotplot) <- c("Classifiers","Accuracy")
538: print(ggplot(acc_dotplot, aes(x=Classifiers,y=Accuracy)) +
539: geom_violin(aes(fill=factor(Classifiers)),na.rm = TRUE)+
556: colnames(mcc_dotplot) <- c("Classifiers","MCC")
557: print(ggplot(mcc_dotplot, aes(x=Classifiers,y=MCC)) +
558: geom_violin(aes(fill=factor(Classifiers)),na.rm = TRUE)+
575: colnames(spe_dotplot) <- c("Classifiers","Specificity")
576: print(ggplot(spe_dotplot, aes(x=Classifiers,y=Specificity)) +
578: geom_violin(aes(fill=factor(Classifiers)),na.rm = TRUE)+
595: colnames(sen_dotplot) <- c("Classifiers","Sensitivity")
596: print(ggplot(sen_dotplot, aes(x=Classifiers,y=Sensitivity)) +
598: geom_violin(aes(fill=factor(Classifiers)),na.rm = TRUE)+
616: colnames(ppv_dotplot) <- c("Classifiers","PPV")
617: print(ggplot(ppv_dotplot, aes(x=Classifiers,y=PPV)) +
619: geom_violin(aes(fill=factor(Classifiers)),na.rm = TRUE)+
636: colnames(npv_dotplot) <- c("Classifiers","NPV")
637: print(ggplot(npv_dotplot, aes(x=Classifiers,y=NPV)) +
639: geom_violin(aes(fill=factor(Classifiers)),na.rm = TRUE)+
184: independentClasses <- droplevels(independentClasses)
189: datasetClasses <- droplevels(datasetClasses)
217: trainingSetClasses <- droplevels(trainingSetClasses)
224: testSetClasses <- droplevels(testSetClasses)
229: testSetClasses <- as.factor(c(as.character(independentClasses),
230: as.character(testSetClasses)))
231: testSetClasses <- droplevels(testSetClasses)
250: trainingSetClasses2 <- droplevels(trainingSetClasses2)
258: testSetClasses2 <- droplevels(testSetClasses2)
262: trainingSet_DM <- cbind(trainingSet2,trainingSetClasses2)
268: testSet_DM <- cbind(testSet2,testSetClasses2)
417: if(predict(model_rf,testSet[ii,]) == levels(testSetClasses)[1])
422: if(predict(model_svm,testSet[ii,]) == levels(testSetClasses)[1])
427: if(predict(model_nb,testSet[ii,]) == levels(testSetClasses)[1])
437: if(predict(model_lr,testSet[ii,]) == levels(testSetClasses)[1])
442: if(predict(model_nn,testSet[ii,]) == levels(testSetClasses)[1])
447: if(predict(model_pls,testSet[ii,]) == levels(testSetClasses)[1])
456: k=3)$CL[,3] == levels(testSetClasses)[1])
487: which(testSetClasses == levels(testSetClasses)[1]),,drop=FALSE] == 1)
489: which(testSetClasses == levels(testSetClasses)[2]),,drop=FALSE] == 0)
491: which(testSetClasses == levels(testSetClasses)[1]),,drop=FALSE] == 0)
493: which(testSetClasses == levels(testSetClasses)[2]),,drop=FALSE] == 1)

GenomicRanges:R/GenomicRanges-class.R: [ ]
664: .COL2CLASS <- c(
671: classinfo <- makeClassinfoRowForCompactPrinting(x, .COL2CLASS)
6: ### TODO: The 'constraint' slot could be moved to the Vector class (or to the
7: ### Annotated class) so any Vector object could be constrained.
116: msg <- c(class(x), " object contains ", length(idx), " out-of-bound ",
134: ### class(ranges(x) == "IRanges")) instead of just an IRanges object (i.e.
155: if (!(class(ranges(x)) %in% c("IRanges", "StitchedIPos", "UnstitchedIPos")))
351: if (class(value) != "IRanges")
384: class(x), " objects"))
511: ### (d) 'class(ans)' is 'relistToClass(x[[1]])' e.g. CompressedRleList if
582: stop(wmsg(class(x), " objects don't support [[, as.list(), ",
670: .COL2CLASS <- c(.COL2CLASS, getSlots(class(x))[extra_col_names])
708: class(x), " object"))
8: setClass("GenomicRanges",
27: setClass("GenomicPos",
454: ### initialize. Reference classes will want to override 'update'. Other
651: print.classinfo=FALSE, print.seqinfo=FALSE,
663: if (print.classinfo) {
673: stopifnot(identical(colnames(classinfo), colnames(out)))
674: out <- rbind(classinfo, out)
690: show_GenomicRanges(object, print.classinfo=TRUE, print.seqinfo=TRUE)
21: setClassUnion("GenomicRanges_OR_missing", c("GenomicRanges", "missing"))
48: ### Extra column slots (added by GRanges subclasses)
50: ### The "extra column slots" are parallel slots added by GRanges subclasses
640: setMethod("makeNakedCharacterMatrixForDisplay", "GenomicRanges",
655: ## makePrettyMatrixForCompactPrinting() assumes that head() and tail()
662: out <- makePrettyMatrixForCompactPrinting(xx)

DaMiRseq:R/Classif_2_Classes_Training.R: [ ]
121: class_lev_ckeck <- levels(classes)
164: acc.Class<- matrix()
181: class_level2 <- levels(classes)
222: model_class <- list()
419: MCC.Class <- acc.Class
420: TP.Class <- acc.Class
421: TN.Class <- acc.Class
422: FP.Class <- acc.Class
423: FN.Class <- acc.Class
424: Sensit.Class <- acc.Class
425: Specif.Class <- acc.Class
426: PPV.Class <- acc.Class
427: NPV.Class <- acc.Class
430: TP_Class <- colSums(tPred[
433: TN_Class <- colSums(tPred[
436: FP_Class <- colSums(tPred[
439: FN_Class <- colSums(tPred[
193: trainingSetClasses2 <- classes[sample_index_train2,
201: testSetClasses2 <- classes[sample_index_test2,
358: testSetClasses <- testSet_DM$classes
14: #' @param classes A class vector with \code{nrow(data)} elements.
15: #' Each element represents the class label for each observation.
16: #' Two different class labels are allowed. Note. this argument should
19: #' (but without 'class' column). Each column represents a different
50: #' # selected_features,classes=df$class, fSample.tr.w=0.6, iter=3,
86: if(!("class" %in% colnames(colData(data))))
87: stop("'class' info is lacking! Include the variable 'class'
88: in colData(data) and label it 'class'!")
90: classes <- colData(data)$class
122: count_cl1 <- length(which(classes %in% class_lev_ckeck[1]))
123: count_cl2 <- length(which(classes %in% class_lev_ckeck[2]))
183: dim(data)[1]fSample.tr.w/length(class*_level2))
184: for (i in seq_len(length(class_level2))){
185: sample_index2 <- sample(which(class_level2[i]==classes),
237: model_class[[kk]] <- model_rf
263: model_class[[kk]] <- model_svm
278: model_class[[kk]] <- model_nb
287: predict(model_lda, testSet_DM)$class,
291: model_class[[kk]] <- model_lda
306: model_class[[kk]] <- model_lr
321: model_class[[kk]] <- model_nn
336: model_class[[kk]] <- model_pls
381: if(predict(model_lda,testSet_DM[ii,])$class == levels(testSetClasses)[1])
418: acc.Class <- matrix(nrow=1, ncol = ncol(tPred))
443: TP.Class <- TP_Class
444: TN.Class <- TN_Class
445: FP.Class <- FP_Class
446: FN.Class <- FN_Class
448: acc.Class <- 100 * (TP_Class + TN_Class)/(
449: TP_Class + TN_Class + FP_Class + FN_Class)
451: MCC.Class <- (TP_Class * TN_Class - FP_Class * FN_Class) /
452: sqrt((TP_Class + FP_Class) * (TP_Class + FN_Class) *
453: (TN_Class + FP_Class) * (TN_Class + FN_Class))
455: Sensit.Class <- TP_Class / (TP_Class + FN_Class)
456: Specif.Class <- TN_Class / (TN_Class + FP_Class)
457: PPV.Class <- TP_Class / (TP_Class + FP_Class)
458: NPV.Class <- TN_Class / (TN_Class + FN_Class)
461: acc.Class[which(is.nan(acc.Class))] <- 0
462: MCC.Class[which(is.nan(MCC.Class))] <- 0
463: Sensit.Class[which(is.nan(Sensit.Class))] <- 0
464: Specif.Class[which(is.nan(Specif.Class))] <- 0
465: PPV.Class[which(is.nan(PPV.Class))] <- 0
466: NPV.Class[which(is.nan(NPV.Class))] <- 0
468: acc.Class <- as.data.frame(t(acc.Class))
469: MCC.Class <- as.data.frame(t(MCC.Class))
470: Sensit.Class <- as.data.frame(t(Sensit.Class))
471: Specif.Class <- as.data.frame(t(Specif.Class))
472: PPV.Class <- as.data.frame(t(PPV.Class))
473: NPV.Class <- as.data.frame(t(NPV.Class))
541: output_data_list$classes <- as.factor(class_level2)
1: #' @title Train a Binary Classifier using 'Staking' Learning strategy.
8: #' classification model building. A 'two-classes' classification task
23: #' @param cl_type List of weak classifiers that will compose the
29: #' \item The models of each classifier used to build the Ensemble
31: #' (over the iteration) for the Ensemble classifier;
32: #' \item the weights associated to each weak classifier;
49: #' # Classification_res <- DaMiR.EnsL_Train(
57: classes,
71: # if (missing(classes))
72: # stop("'classes' argument must be provided")
94: if(missing(classes))
95: stop("'classes' argument must be provided when
105: if(!(is.factor(classes)))
106: stop("'classes' must be a factor")
117: if(length(classes) != dim(data)[1])
118: stop("length(classes) must be equal to dim(data)[1]")
126: # both classes)")
159: # cat("You select:",cl_type, "weak classifiers for creating
209: colnames(trainingSet_DM) <- c(varNames, "classes")
211: formula_DM <- as.formula(paste("classes", varNames1, sep = " ~ ")
216: colnames(testSet_DM) <- c(varNames, "classes")
220: ############### Weak classifiers ##################
232: acc_model[kk] <- caret::confusionMatrix(
233: table(predict(model_rf, testSet_DM),testSet_DM$classes),
234: reference = testSet_DM$classes)$overall['Accuracy']
258: acc_model[kk] <- caret::confusionMatrix(
260: reference = testSet_DM$classes)$overall['Accuracy']
273: acc_model[kk] <- caret::confusionMatrix(
275: reference = testSet_DM$classes)$overall['Accuracy']
286: acc_model[kk] <- caret::confusionMatrix(
288: reference = testSet_DM$classes)$overall['Accuracy']
301: acc_model[kk] <- caret::confusionMatrix(
303: reference = testSet_DM$classes)$overall['Accuracy']
316: acc_model[kk] <- caret::confusionMatrix(
318: reference = testSet_DM$classes)$overall['Accuracy']
331: acc_model[kk] <- caret::confusionMatrix(
333: reference = testSet_DM$classes)$overall['Accuracy']
344: # acc_model[kk] <- caret::confusionMatrix(
347: # reference = testSet_DM$classes)$overall['Accuracy']
542: output_data_list$positiveClass <- levels(classes)[1]
195: trainingSetClasses2 <- droplevels(trainingSetClasses2)
203: testSetClasses2 <- droplevels(testSetClasses2)
207: trainingSet_DM <- cbind(trainingSet2,trainingSetClasses2)
214: testSet_DM <- cbind(testSet2,testSetClasses2)
366: if(predict(model_rf,testSet_DM[ii,]) == levels(testSetClasses)[1])
371: if(predict(model_svm,testSet_DM[ii,]) == levels(testSetClasses)[1])
376: if(predict(model_nb,testSet_DM[ii,]) == levels(testSetClasses)[1])
386: if(predict(model_lr,testSet_DM[ii,]) == levels(testSetClasses)[1])
391: if(predict(model_nn,testSet_DM[ii,]) == levels(testSetClasses)[1])
396: if(predict(model_pls,testSet_DM[ii,]) == levels(testSetClasses)[1])
405: k=3)$CL[,3] == levels(testSetClasses)[1])
431: which(testSetClasses == levels(testSetClasses)[1]),
434: which(testSetClasses == levels(testSetClasses)[2]),
437: which(testSetClasses == levels(testSetClasses)[1]),
440: which(testSetClasses == levels(testSetClasses)[2]),

GenomicAlignments:R/GAlignmentPairs-class.R: [ ]
700: .PAIR_COL2CLASS <- c(
704: .HALVES_COL2CLASS <- c(
707: .COL2CLASS <- c(.PAIR_COL2CLASS,
23: ### Combine the new "parallel slots" with those of the parent class. Make
24: ### sure to put the new parallel slots first. See R/Vector-class.R file
53: ### x[i] - GAlignmentPairs object of the same class as 'x'
335: ### GAlignmentPairs class is changed to derive from CompressedList.
355: ### class is changed to derive from CompressedList.
691: cat(class(x), " object with ",
709: .HALVES_COL2CLASS,
711: .HALVES_COL2CLASS)
713: S4Vectors:::makeClassinfoRowForCompactPrinting(x, .COL2CLASS)
7: setClass("GAlignmentPairs",
686: print.classinfo=FALSE,
699: if (print.classinfo) {
712: classinfo <-
715: stopifnot(identical(colnames(classinfo), colnames(out)))
716: out <- rbind(classinfo, out)
733: print.classinfo=TRUE, print.seqinfo=TRUE)
697: out <- S4Vectors:::makePrettyMatrixForCompactPrinting(x,

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