Found 28210 results in 1516 files, showing top 50 files (show more).
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)
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]
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/repeats*folds, aggregated via '%s' (%s) ...", nrow(df_predictor_class), nfold*nrepeat, 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$classes,
818: 		df_full <- as.matrix(xSparseMatrix(df_full, verbose=FALSE))
GRaNIE:R/core.R: [ ]
5444:                     d = tibble::add_row(d, r_positive = r_pos, class = classCur, peak_gene.p.raw.class = pclassCur, n = 0)
707:                   peak.GC.class = cut(.data$`G|C`, breaks = seq(0,1,1/nBins), include.lowest = TRUE, ordered_result = TRUE)) %>%
916:         peaks_GC_class = cut(GC_data.df$peak.GC.perc, breaks = seq(0,1,1/nBins), include.lowest = TRUE, ordered_result = T...(4 bytes skipped)...
458: .storeAsMatrixOrSparseMatrix <- function (GRN, df, ID_column, slotName, threshold = 0.1) {
741: .normalizeCountMatrix <- function(GRN, data, normalization, additionalParams =list()) {
4745:         classPeaks = class(GRN@data$peaks$counts)
4758:         classRNA = class(GRN@data$RNA$counts)
5439:         for (classCur in networkType_details){
5492:                                classAll = c(as.character(d2$class), d3$r_positive),
2780:       GC_class.cur = GC_classes_foreground.df$GC_class[i]
5521:                       peak_gene.p.raw.class.bin = as.numeric(.data$peak_gene.p.raw.class)) %>%
719:     GC_classes.df = GC.data %>%
1585:     TFBS_bindingMatrix.df = tibble::as_tibble(res.l)
2040: AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05, 
2476:         GC_classes_foreground.df = peaksForeground %>%
2484:         GC_classes_background.df = peaksBackground %>%
2531:         GC_classes_all.df = dplyr::full_join(GC_classes_foreground.df, GC_classes_background.df, suffix = c(".fg",".bg"), by = "GC_class") %>%
5532: .classFreq_label <- function(tbl_freq) {
5440:             for (pclassCur in levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class)) {
3: #' Create and initialize an empty \code{\linkS4class{GRN}} object.
15: #' @return Empty \code{\linkS4class{GRN}} object
126: #' Add data to a \code{\linkS4class{GRN}} object.
128: #' This function adds both RNA and peak data to a \code{\linkS4class{GRN}} object, along with data normalization.
168: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function (e.g., slots \code{GRN@data$peaks} and \code{GRN@d...(9 bytes skipped)...
471:     fractionZero = (length(df.m) - Matrix::nnzero(df.m)) / length(df.m)
720:         dplyr::group_by(.data$peak.GC.class) %>%
723:         tidyr::complete(.data$peak.GC.class, fill = list(n = 0)) %>%
727:     #ggplot2::ggplot(GC.data, ggplot2::aes(GC.class)) + geom_histogram(stat = "count") + ggplot2::theme_bw()
729:     #ggplot2::ggplot(GC_classes.df , ggplot2::aes(GC.class, n_rel)) + geom_bar(stat = "identity") + ggplot2::theme_bw()
919:                          GC_class = peaks_GC_class,
963: .gcqn <- function(data, GC_class, summary='mean', round=FALSE){
967:     for(ii in 1:nlevels(GC_class)){
969:         id <- which(GC_class==levels(GC_class)[ii])
1003: #     for(ii in 1:nlevels(peaksAnnotation$peak.GC.class)){
1004: #         id <- which(peaksAnnotation$peak.GC.class==levels(peaksAnnotation$peak.GC.class)[ii])
1096: #' Filter RNA-seq and/or peak data from a \code{\linkS4class{GRN}} object
1102: ...(166 bytes skipped)...s_peak_gene}}. \strong{This function does NOT (re)filter existing connections when the \code{\linkS4class{GRN}} object already contains connections. Thus, upon re-execution of this function with different ...(60 bytes skipped)...
1127: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function.
1382: #' Add TFBS to a \code{\linkS4class{GRN}} object. 
1395: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function (\code{GRN@annotation$TFs} in pa...(9 bytes skipped)...
1520: #' Overlap peaks and TFBS for a \code{\linkS4class{GRN}} object
1525: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function (\code{GRN@data$TFs$TF_peak_overlap} in particular...(1 bytes skipped)...
1810: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function
1910: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function.  
2024: #' Run the activator-repressor classification for the TFs for a \code{\linkS4class{GRN}} object
2034: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function. 
2163:         fileCur = paste0(outputFolder, .getOutputFileName("plot_class_density"), "_", connectionTypeCur, suffixFile, ".pdf")
2173:         fileCur = paste0(outputFolder, .getOutputFileName("plot_class_medianClass"), "_", connectionTypeCur, suffixFile, ".pdf")
2186:         fileCur = paste0(outputFolder, .getOutputFileName("plot_class_densityClass"), "_", connectionTypeCur, suffixFile, ".pdf")
2247: #' Add TF-peak connections to a \code{\linkS4class{GRN}} object
2265: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function. 
2477:           dplyr::group_by(.data$GC_class) %>%
2480:           tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
2485:           dplyr::group_by(.data$GC_class) %>%
2488:           tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
2546:           peaksBackgroundGCCur =  peaksBackground %>% dplyr::filter(.data$GC_class == GC_classes_foreground.df$GC_class[i])
2785:         dplyr::filter(.data$GC_class == GC_class.cur) %>%
2787:       #futile.logger::flog.info(paste0(" GC.class ", GC.class.cur, ": Required: ", requiredNoPeaks, ", available: ", availableNoPeaks))
2792:         #futile.logger::flog.info(paste0(" Mimicking distribution FAILED (GC class ", GC.class.cur, " could not be mimicked"))
2815: #' Add peak-gene connections to a \code{\linkS4class{GRN}} object
2839: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function. 
3445: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function. 
4060: #' Add TF-gene correlations to a \code{\linkS4class{GRN}} object. 
4070: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
4324: ...(4 bytes skipped)...enerate a summary for the number of connections for different filtering criteria for a \code{\linkS4class{GRN}} object. 
4343: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
4642: #' @return An small example \code{\linkS4class{GRN}} object
4709: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object
4711: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object.
4712: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4726: ...(37 bytes skipped)... the type as indicated by the function parameters. This function does **NOT** return a \code{\linkS4class{GRN}} object.
4751:             message = paste0("Unsupported class for GRN@data$peaks$counts. Contact the authors.")
4764:             message = paste0("Unsupported class for GRN@data$RNA$counts. Contact the authors.")
4814: #' Extract connections or links from a \code{\linkS4class{GRN}} object as a data frame.
4818: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4831: ...(5 bytes skipped)...eturn A data frame with the requested connections. This function does **NOT** return a \code{\linkS4class{GRN}} object. Depending on the arguments, the
5033: ...(0 bytes skipped)...#' Retrieve parameters for previously used function calls and general parameters for a \code{\linkS4class{GRN}} object. 
5035: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
5041: #' @return The requested parameters. This function does **NOT** return a \code{\linkS4class{GRN}} object.
5087: #' @return An updated \code{\linkS4class{GRN}} object, with some slots being deleted (\code{GRN@data$TFs$classification} as well as \code{GRN@stats$connectionDetails.l})
5121: #' @return An updated \code{\linkS4class{GRN}} object, with the output directory being adjusted accordingly
5344:     "plot_class_density"             = "TF_classification_densityPlots",
5345:     "plot_class_medianClass"         = "TF_classification_stringencyThresholds",
5346:     "plot_class_densityClass"        = "TF_classification_summaryHeatmap",
5433:         dplyr::group_by(.data$r_positive, class, .data$peak_gene.p.raw.class) %>%
5442:                 row = which(d$r_positive == r_pos & d$class == classCur & as.character(d$peak_gene.p.raw.class) == as.character(pclassCur))
5450:     # Restore the "ordered" factor for class
5451:     d$peak_gene.p.raw.class = factor(d$peak_gene.p.raw.class, ordered = TRUE, levels =  levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class))
5456:         dplyr::group_by(.data$r_positive, .data$class) %>%
5462:         dplyr::group_by(class, .data$peak_gene.p.raw.class)%>%
5471:     normFactor_real = dplyr::filter(dsum, class ==  !! (networkType_details[1])) %>%  dplyr::pull(.data$sum_n) %>% sum() /
5472:         dplyr::filter(dsum, class ==  !! (networkType_details[2])) %>%  dplyr::pull(.data$sum_n) %>% sum()
5476:         dplyr::group_by(.data$peak_gene.p.raw.class, .data$r_positive) %>%
5477:         dplyr::summarise(n_real     = .data$n[class == !! (names(colors_vec)[1]) ],
5478:                          n_permuted = .data$n[class == !! (names(colors_vec)[2]) ]) %>%
5486:     stopifnot(identical(levels(d2$peak_gene.p.raw.class), levels(d3$peak_gene.p.raw.class)))
5489:     d_merged <- tibble::tibble(peak_gene.p.raw.class = c(as.character(d2$peak_gene.p.raw.class), 
5490:                                                          as.character(d3$peak_gene.p.raw.class)),
5495:                       peak_gene.p.raw.class = factor(.data$peak_gene.p.raw.class, levels = levels(d2$peak_gene.p.raw.class)))
5497:     d4 = tibble::tibble(peak_gene.p.raw.class = unique(d$peak_gene.p.raw.class), 
5504:         row_d2 = which(d2$class == networkType_details[1] & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5508:         row_d2 = which(d2$class == paste0("random_",range) & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5512:         row_d3 = which(d3$r_positive == TRUE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5514:         row_d3 = which(d3$r_positive == FALSE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
5522:         dplyr::arrange(.data$peak_gene.p.raw.class.bin)
5524:     d4_melt = reshape2::melt(d4, id  = c("peak_gene.p.raw.class.bin", "peak_gene.p.raw.class")) %>%
5577: ...(36 bytes skipped)...ources of biological and technical variation for features (TFs, peaks, and genes) in a \code{\linkS4class{GRN}} object
5597: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function to \code{GRN@stats$varianceParti...(111 bytes skipped)...
5655:         coltypes = meta %>% dplyr::summarise_all(class)
87:   # Stringencies for AR classification
93:   # Colors for the different classifications
190:   checkmate::assertClass(GRN, "GRN")
306:     countsPeaks.norm.df  = .normalizeCountMatrix(GRN, counts_peaks %>% tibble::column_to_rownames("peakID") %>% as.matrix(), 
313:     countsRNA.norm.df    = .normalizeCountMatrix(GRN, counts_rna %>% tibble::column_to_rownames("ENSEMBL") %>% as.matrix(), 
375:     GRN@data$peaks$counts = .storeAsMatrixOrSparseMatrix(GRN, df = data.l[["peaks"]], ID_column = "peakID", slotName = "GRN@data$peaks$counts")
378:         GRN@data$peaks$counts_raw = .storeAsMatrixOrSparseMatrix(GRN, df = counts_peaks %>% dplyr::select("peakID", tidyselect::one_of(GRN@config$sharedSamples)), 
382:     GRN@data$RNA$counts   = .storeAsMatrixOrSparseMatrix(GRN, df =  data.l[["RNA"]], ID_column = "ENSEMBL", slotName = "GRN@data$RNA$counts")
385:         GRN@data$RNA$counts_raw = .storeAsMatrixOrSparseMatrix(GRN, df = counts_rna %>% dplyr::select("ENSEMBL", tidyselect::one_of(GRN@config$sharedSamples)), 
476:         df.m = .asSparseMatrix(df.m, convertNA_to_zero = FALSE, 
568:   countsPeaks.clean = getCounts(GRN, type = "peaks", permuted = FALSE, asMatrix = TRUE, includeFiltered = TRUE)
655:   countsRNA.m  = getCounts(GRN, type = "rna", permuted = FALSE, asMatrix = TRUE, includeFiltered = TRUE)
733:     GRN@stats$peaks$GC = GC_classes.df
743:     checkmate::assertMatrix(data)
754:         dd <- suppressMessages(DESeq2::DESeqDataSetFromMatrix(countData = data,
992: # counts = getCounts(GRN, type = "peaks", asMatrix = TRUE, includeFiltered = TRUE)
1056: #         dd <- DESeq2::DESeqDataSetFromMatrix(countData = countDataNew,
1144:   checkmate::assertClass(GRN, "GRN")
1402:   checkmate::assertClass(GRN, "GRN")
1535:   checkmate::assertClass(GRN, "GRN")
1587:     if (!all(colnames(TFBS_bindingMatrix.df) %in% GRN@config$allTF)) {
1599:     GRN@data$TFs$TF_peak_overlap = TFBS_bindingMatrix.df %>%
1605:     GRN@data$TFs$TF_peak_overlap = .asSparseMatrix(as.matrix(GRN@data$TFs$TF_peak_overlap), 
1815:   checkmate::assertClass(GRN, "GRN")
1842:     countsPeaks = .normalizeCountMatrix(GRN@data$peaks$counts_orig, normalization = normalization)
1913:   checkmate::assertClass(GRN, "GRN")
1968:     countsNorm = .normalizeCountMatrix(data %>% dplyr::select(-"ENSEMBL"), normalization = normalization)
2022: ######## AR classification ########
2027: ...(47 bytes skipped)...c[0,1]. Default 0.05. Significance threshold for Wilcoxon test that is run in the end for the final classification. See the Vignette and *diffTF* paper for details.
2038: #' # GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)
2048:   checkmate::assertClass(GRN, "GRN")
2062:   GRN@data$TFs$classification$TF.translation.orig = GRN@annotation$TFs %>%
2086:       if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]])) {
2087:         if (is.null(GRN@data$TFs$classification[[permIndex]])) {
2088:           GRN@data$TFs$classification[[permIndex]] = list()
2090:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
2093:       if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground) |
2094:           is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background) |
2095:           is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground) |
2096:           is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background) |
2097:           is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor) |
2101:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
2124:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = res.l[["median_foreground"]]...(0 bytes skipped)...
2125:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = res.l[["median_background"]]...(0 bytes skipped)...
2126:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground   = res.l[["foreground"]]
2127:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background   = res.l[["background"]]
2129:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor = TF_peak_cor
2132:       # Final classification: Calculate thresholds by calculating the quantiles of the background and compare the real ...(24 bytes skipped)...
2134:       if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l) | forceRerun) {
2135:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l = 
2136:           .calculate_classificationThresholds(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background), 
2140:       if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification) | forceRerun) {
2142:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification = 
2144:             output.global.TFs = GRN@data$TFs$classification$TF.translation.orig %>% dplyr::mutate(TF = .data$TF.name), 
2145:             median.cor.tfs = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground, 
2146:             act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l, 
2148:             t.cor.sel.matrix = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground), 
2149:             t.cor.sel.matrix.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background), 
2154:       # PLOTS FOR THE RNA-SEQ CLASSIFICATION
2165:           .plot_density(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground),
2166:                         .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background), 
2176:             median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground), 
2177:             median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background), 
2179:             act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l, 
2189:           TF_peak_cor = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor
2191:           .plot_heatmapAR(TF.peakMatrix.df = peak_TF_overlapCur.df, 
2196:                           median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground), 
2197:                           median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background), 
2198:                           act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l, 
2199:                           finalClassification = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification,
2209:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = NULL
2210:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = NULL
2211:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = NULL
2212:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = NULL
2213:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l = NULL
2217:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = 
2218:           .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground), convertNA_to_zero = TRUE)
2219:         GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = 
2220:           .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background), convertNA_to_zero = TRUE)
2282:   checkmate::assertClass(GRN, "GRN")
2500:           minPerc = .findMaxBackgroundSize(GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage =  threshold_percentage)
2544:         for (i in seq_len(nrow(GC_classes_foreground.df))) {
2552:           #Select the minimum, which for low % classes is smaller than the required number to mimic the foreground 100%
2554:             nPeaksCur = GC_classes_all.df$n.bg.needed[i]    
2556:             nPeaksCur = min(GC_classes_all.df$n.bg.needed[i], nrow(peaksBackgroundGCCur))
2559:           if (GC_classes_all.df$n.bg.needed[i] > nrow(peaksBackgroundGCCur)) {
2574:           plots_GC.l[[TFCur]] = .generateTF_GC_diagnosticPlots(TFCur, GC_classes_foreground.df, GC_classes_background.df, GC_classes_all.df, peaksForeground, peaksBackground, peaksBackgroundGC)
2760: .findMaxBackgroundSize <- function (GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage = 0.05) {
2777:     for (i in seq_len(nrow(GC_classes_foreground.df))) {
2779:       n_rel    = GC_classes_foreground.df$n_rel[i]
2784:       availableNoPeaks = GC_classes_background.df %>% 
2796:       if (i == nrow(GC_classes_foreground.df)) {
2802:     }  # end of  for (i in 1:nrow(GC_classes_foreground.df)) {
2857:   checkmate::assertClass(GRN, "GRN")
3481:   checkmate::assertClass(GRN, "GRN")
4079:   checkmate::assertClass(GRN, "GRN")  
4362:   checkmate::assertClass(GRN, "GRN")
4716: #' @param asMatrix Logical. \code{TRUE} or \code{FALSE}. Default \code{FALSE}. If set to \code{FALSE}, counts are retu...(167 bytes skipped)...
4717: ...(10 bytes skipped)...includeIDColumn Logical. \code{TRUE} or \code{FALSE}. Default \code{TRUE}. Only relevant if \code{asMatrix = FALSE}. If set to \code{TRUE}, an explicit ID column is returned (no row names). If set to \code{...(45 bytes skipped)...
4727: getCounts <- function(GRN, type,  permuted = FALSE, asMatrix = FALSE, includeIDColumn = TRUE, includeFiltered = FALSE) {
4729:     checkmate::assertClass(GRN, "GRN")
4746:         if ("matrix" %in% classPeaks) {
4748:         } else if ("dgCMatrix" %in% classPeaks) {
4759:         if ("matrix" %in% classRNA) {
4761:         } else if ("dgCMatrix" %in% classRNA) {
4793:     if (!asMatrix) {
4832: ...(2 bytes skipped)... data frame that is returned has different columns, which however can be divided into the following classes according to their name:
4887:     checkmate::assertClass(GRN, "GRN")  
5048:     checkmate::assertClass(GRN, "GRN")
5084: #' 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
5094:     checkmate::assertClass(GRN, "GRN")
5102:         GRN@data$TFs$classification[[permIndex]]$TF_cor_median_foreground = NULL
5103:         GRN@data$TFs$classification[[permIndex]]$TF_cor_median_background = NULL
5104:         GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_foreground = NULL
5105:         GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_background = NULL
5106:         GRN@data$TFs$classification[[permIndex]]$act.rep.thres.l = NULL
5129:     checkmate::assertClass(GRN, "GRN")
5215:             GRN@data$peaks[["counts"]] = .storeAsMatrixOrSparseMatrix(GRN, df = GRN@data$peaks$counts_norm %>% dplyr::select(-"isFiltered"), 
5243:             GRN@data$RNA[["counts"]] = .storeAsMatrixOrSparseMatrix(GRN, df = GRN@data$RNA$counts_norm.l[["0"]] %>% dplyr::select(-"isFiltered"), 
5437:     # Some classes might be missing, add them here with explicit zeros
5494:         dplyr::mutate(classAll = factor(.data$classAll, levels=c(paste0("real_",range), paste0("random_",range), "TRUE", "FALSE")),
5539:     checkmate::assertClass(GRN, "GRN")
5550:         expMeans.m = getCounts(GRN, type = "rna", permuted = FALSE, asMatrix = TRUE)
5561: ...(5 bytes skipped)...                                 mean = rowMeans(getCounts(GRN, type = "peaks", permuted = FALSE, asMatrix = TRUE))) %>%
5614:     checkmate::assertClass(GRN, "GRN")
88:   par.l$internal$allClassificationThresholds = c(0.1, 0.05, 0.01, 0.001)
1784:   peak_TF_overlapCur.df = .asMatrixFromSparse(GRN@data$TFs$TF_peak_overlap, convertZero_to_NA = FALSE) %>% 
2143:           .finalizeClassificationAndAppend(
4749:             result = .asMatrixFromSparse(GRN@data$peaks$counts, convertZero_to_NA = FALSE)
4762:             result = .asMatrixFromSparse(GRN@data$RNA$counts, convertZero_to_NA = 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::warn(warn, class = "missing_crit_tags")
218:         ), class = "missing_req_col_err")
249:         ), class = "missing_req_col_err")
437:                         rlang::inform(warn_empty_iss, class = "warn_empty_iss")
546:                                 class = "warn_empty_iss"
608:         rlang::abort(error_msg(tags_names), class = "missing_tags_err")
717:             rlang::abort(single_err, class = "tag_dupl_err")
782:         rlang::abort(compact_msg, class = "tag_type_err")
1135:         rlang::abort(add_types_err, class = "add_types_err")
1307:                 class = "xls_file"
1483:                     class = "na_concat"
1583:                     class = "filter_warn"
1956:             class = "missing_path_col"
2926:             rlang::inform(missing_msg, class = "auto_mode_miss")
3186:                 class = "coll_matrix_issues"
3251:         rlang::abort(not_date_err, class = "not_date_coll_err")
4246:                     rlang::inform(warn, class = "rec_unsupp_ext")
4752:         rlang::abort(err_msg, class = "genomic_file_char")
4842:         rlang::warn(warn_miss, class = "warn_miss_genes")
5203:             class = "missing_cols_key"
5253:                 rlang::inform(flag_msg, class = "flag_logic_long")
5571:         rlang::abort(format_err, class = "outlier_format_err")
5171:             KnownGeneClass = ifelse(
1015:         colClasses = col_types,
612:         rlang::abort(error_msg(missing_tags), class = "missing_tags_err")
735:             rlang::inform(single_warn, class = "tag_dupl_warn")
758:             err <- c(paste("Wrong col class for tag '", sub_df$tag[1], "'"),
906: # @param x A data.frame object (or any extending class)
925: # @param x A data.frame object (or any extending class)
977: #---- USED IN : import_single_Vispa2Matrix ----
1132:                 "?import_single_Vispa2Matrix"
1149:                     class = "unsup_comp_format"
1164:             class = "im_single_miss_mand_vars"
1266:             class = "ism_import_summary"
2949:             rlang::inform(dupl_msg, class = "auto_mode_dupl")
5222:                 class = "missing_cols_pool"
5276:                     class = "flag_logic_short"
5294:             rlang::abort(unknown_logi_op_err, class = "unsupp_logi_op")
5574:         rlang::abort(format_err, class = "outlier_format_err")
779:             "Wrong column classes for some tags",
1048:     col_types <- .mandatory_IS_types("classic")
1052:             .annotation_IS_types("classic")
1144:             ### If not, switch to classic for reading
1145:             mode <- "classic"
geNetClassifier:R/functions.public.R: [ ]
389:                             class <- names(x)[largest]
71:     classes <- factor(apply(prob, 2, function(x) {assignment.conditions(x, minProb, minDiff)}))
97:     classes<-unique(c(rownames(mxcf),colnames(mxcf)))
133:     byClass <- matrix(nrow=nclasses, ncol=4)
176: externalValidation.probMatrix<- function(queryResult, realLabels, numDecimals=2)
209:     probMatrix <- matrix(0,nrow=length(levels(realLabels)), ncol=length(predClasses))
215:         classAssignments <- globalQueryResult$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 <- 2*rand * 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 <- 2*rand * 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)))
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$new()
singleCellTK:inst/shiny/server.R: [ ]
4039:   shinyjs::addClass(id = "cv_button1", class = "btn-block")
4937:       classes <- names(colData(hmTemp$sce))
6037:       classCol <- colData(vals$counts)[[input$deC1Class]]
6038:       classChoices <- sort(as.vector(unique(classCol)))
98:     updateSelectInput(session, "deC1Class",
245:                          label = "Select Input Matrix:",
249:                          label = "Select Input Matrix:",
255:                          label = "Select Input Matrix:",
2670:       shinyjs::show(selector = ".dimRedPCAICA_plotTabset_class")
4040:   shinyjs::addClass(id = "cv_button2", class = "btn-block")
4041:   shinyjs::addClass(id = "cv_button3", class = "btn-block")
4461:       pltVars$class <- "factor"
4463:       pltVars$class <- "numeric"
4516:                             conditionClass = pltVars$class, defaultTheme = as.logical(pltVars$defTheme))
4532: ...(28 bytes skipped)...         legendSize = input$adjustlegendsize,legendTitleSize = input$adjustlegendtitlesize,conditionClass = pltVars$class)
4998: ...(7 bytes skipped)...            p("Since more than 12 unique values detected, discrete colors will be assigned for this class")
5021:                  p(paste0("Totally ", nUniq, " unique values in this class of annotation, which is too many to provide manual selection. Coloring will be provided by default....(3 bytes skipped)...
5054:                p("No effective category found for the class.")
5380:     selectInput("batchCheckCorrName", "Corrected Matrix",
6036:        !input$deC1Class == "None"){
6049:        !input$deC1Class == "None"){
6050:       classCol <- colData(vals$counts)[[input$deC1Class]]
6063:       g1Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G1
6077:       g2Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G2
6259:                                    class = input$deC1Class,
3377:                                     conditionClass = "factor",
3426:                                   conditionClass = "factor",
4938:       selectInput('hmCellAnn', 'Add cell annotation', classes,
4945:       classes <- names(rowData(hmTemp$sce))
4946:       selectInput('hmGeneAnn', 'Add feature annotation', classes,
5463:                                       conditionClass = "character",
5471:                                       conditionClass = "character",
6040:                   choices = classChoices, multiple = TRUE)
6051:       classChoices <- sort(as.vector(unique(classCol)))
6053:                   choices = classChoices, multiple = TRUE)
6260:                                    classGroup1 = input$deC1G1,
6261:                                    classGroup2 = input$deC1G2,
7251:       vals$effectSizes <- calcEffectSizes(countMatrix = expData(vals$counts, input$snapshotAssay), condition = colData(vals$counts)[, input$selectSnapsho...(12 bytes skipped)...
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/repeats*folds (%s).", ncol(df_predictor_class)-1, nfold*nrepeat, 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/repeats*folds (%s).", ncol(df_predictor_class), nfold*nrepeat, as.character(now)), appendLF=TRUE)
259: 		testset <- df_predictor_class[-trainindex,]
351: ...(54 bytes skipped)...atrix of %d rows/genes X %d columns/repeats*folds, aggregated via '%s' (%s) ...", nrow(df_predictor_class), nfold*nrepeat, 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,
spatialHeatmap:inst/extdata/shinyApp/R/global.R: [ ]
312:   lapply(ft, function(i) { tag("span", list(class = class(i), tags$span(class = "glyphicon glyphicon-move"), i)) }
321:  span(class = "panel panel-default",
322:    div(class = "panel-heading", x), 
323:    div(class = "panel-body", id = ns(x))
328:  span(class = "panel panel-default",
329:    div(class = "panel-heading", names(x)), 
330:    div(class = "panel-body", id = ns(names(x)), ft2tag(x[[1]]))
350:     span(class = "panel panel-default", style = 'margin-left:0px',
351:       div(class = "panel-heading", strong(to.div.tit)), 
352:       div(class = "panel-body", id = ns(to.div.id), ft2tag(to.ft)) 
354:     div(class = "panel panel-default", 
355:       div(class = "panel-heading", strong(from.div.tit)),  
181:   if (is(input, 'dgCMatrix')|is(input, 'matrix')) input <- as.data.frame(as.matrix(input))
ISAnalytics:R/utility-functions.R: [ ]
39:     purrr::walk(desc_msg, ~ rlang::inform(.x, class = "tag_inspect"))
494:         rlang::inform(warn_msg, class = "missing_quant_specs")
638:                         rlang::inform(err_msg, class = "skip_col_transform")
737:         rlang::inform(.nas_introduced_msg(), class = "comp_nas")
966:             rlang::inform(msg, class = "launch_af_empty")
1175:         rlang::inform(no_stats_msg, class = "no_stats_warn")
504:         rlang::abort(err_msg, class = "miss_annot_suff_specs")
516:         rlang::inform("Matrix suffixes specs successfully changed")
532:         rlang::inform("Matrix suffixes specs reset to default")
1304:         rlang::inform("Matrix suffixes specs successfully changed")
GRaNIE:R/plot.R: [ ]
973:                 dplyr::mutate(class = paste0("real_",range))) %>%
2412:         dplyr::mutate(Class = dplyr::if_else(.data$isTF, "TF", "gene"))
3187:     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)...
985:       class_levels = c(paste0("real_",range), paste0("random_",range))
1017:                     peak_gene.p.raw.class = cut(.data$peak_gene.p_raw, breaks = seq(0,1,0.05), include.lowest = TRUE, ordered_result = TRUE),...(0 bytes skipped)...
1018:                     peak_gene.r.class = cut(.data$peak_gene.r, breaks = seq(-1,1,0.05), include.lowest = TRUE, ordered_result = TRUE)) %>...(1 bytes skipped)...
1036:     colors_class = c("black", "black")
1040:     r_pos_class = c("black", "darkgray")
1043:     dist_class = c("dark red", "#fc9c9c")
1047:     freq_class = paste0(gsub(names(freqs), pattern = "(.+)(_.*)", replacement = "\\1"), " (n=", .prettyNum(freqs) ...(6 bytes skipped)...
1053:     xlabels_peakGene_r.class = levels(peakGeneCorrelations.all$peak_gene.r.class)
1062:     xlabels_peakGene_praw.class = levels(peakGeneCorrelations.all$peak_gene.p.raw.class)
1148:           customLabel_class = .customLabeler(table(peakGeneCorrelations.all[indexCur,]$class))
1015:                     peak_gene.distance_class_abs = forcats::fct_explicit_na(addNA(cut(abs(.data$peak_gene.distance), 
1058:     xlabels_peakGene_r.class2 = levels(peakGeneCorrelations.all$peak_gene.r.class)
1425:             dplyr::mutate(peak_gene.distance.class250k = factor(dplyr::if_else(.data$peak_gene.distance <= 250000, "<=250k", ">250k"))) %>%
1462:       distance_class_abund = table(peakGeneCorrelations.all[indexCur,]$peak_gene.distance_class_abs)
547:   GC_classes_background_GC.df = peaksBackgroundGC %>%
555:   GC_classes_all2.df = rbind(GC_classes_foreground.df, GC_classes_background.df, GC_classes_background_GC.df) %>%
1009:     nClasses_distance  = 10
9: #' Produce a PCA plot of the data from a \code{\linkS4class{GRN}} object
23: #' @return An updated \code{\linkS4class{GRN}} object. 
321: # data: Matrix of your data, with column names. May or may not be vsd transformed, log2 transformed etc
440: #' Plot diagnostic plots for TF-peak connections for a \code{\linkS4class{GRN}} object
455: #' @return An updated \code{\linkS4class{GRN}} object.
548:     dplyr::group_by(.data$GC_class) %>%
551:     tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
557:     dplyr::left_join(GC_classes_all.df, by = "GC_class") %>%
571:   g1 = ggplot2::ggplot(GC_classes_all2.df , ggplot2::aes(.data$GC_class, .data$n_rel, group = .data$type, fill = .data$type, label = .data$n.bg.needed.relFreq)) + 
579:     ggplot2::xlab("GC class from peaks") +
584:   g2 = ggplot2::ggplot(GC_classes_all2.df , ggplot2::aes(.data$GC_class, log10(.data$n+1), group = .data$type, fill = .data$type)) + 
590:     ggplot2::xlab("GC class from peaks") +
834: #' Plot diagnostic plots for peak-gene connections for a \code{\linkS4class{GRN}} object
848: #' @return An updated \code{\linkS4class{GRN}} object.
975:                 dplyr::mutate(class = paste0("random_",range)))
994:             dplyr::mutate(class = factor(paste0("real_",range), levels = class_levels)),
996:             dplyr::mutate(class = factor(paste0("random_",range), levels = class_levels))) %>%
1013:                     peak_gene.distance_class = 
1023:     levels(peakGeneCorrelations.all$peak_gene.distance_class_abs)[1] = 
1024:       gsub("(-\\d+)", "0", levels(peakGeneCorrelations.all$peak_gene.distance_class_abs)[1], perl = TRUE)
1029:         dplyr::mutate(peak_gene.p_raw.robust.class = 
1037:     names(colors_class)= unique(peakGeneCorrelations.all$class)
1038:     colors_class[which(grepl("random", names(colors_class)))] = "darkgray"
1041:     names(r_pos_class) =c("TRUE", "FALSE")
1044:     names(dist_class) = class_levels
1046:     freqs= table(peakGeneCorrelations.all$class)
1049:     freq_class = gsub(freq_class, pattern = "random", replacement = "permuted")
1050:     names(freq_class) <- names(freqs)
1054:     nCur = length(xlabels_peakGene_r.class)
1055:     xlabels_peakGene_r.class[setdiff(seq_len(nCur), c(1, floor(nCur/2), nCur))] <- ""
1059:     nCur = length(xlabels_peakGene_r.class2)
1060:     xlabels_peakGene_r.class2[setdiff(seq_len(nCur), c(1, floor(nCur/4), floor(nCur/2), floor(nCur/4*3), nCur))] <- ""
1063:     nCur = length(xlabels_peakGene_praw.class)
1064:     xlabels_peakGene_praw.class[setdiff(seq_len(nCur), c(1, floor(nCur/2), nCur))] <- ""
1137:           indexCurReal = intersect(indexCur, which(peakGeneCorrelations.all$class == names(dist_class)[1]))
1147:           # Produce the labels for the class-specific subtitles
1162:             ggplot2::facet_wrap(~ .data$class, labeller = ggplot2::labeller(class=freq_class) ) +
1164:             ggplot2::scale_color_manual(labels = names(r_pos_class), values = r_pos_class) +
1176:           xlabels = levels(tbl.l$d_merged$peak_gene.p.raw.class)
1179:           gB3 = ggplot2::ggplot(tbl.l$d_merged, ggplot2::aes(.data$peak_gene.p.raw.class, .data$ratio, fill = .data$classAll)) + 
1183:             ggplot2::scale_fill_manual("Class", values = c(dist_class, r_pos_class), 
1186:             ggplot2::scale_x_discrete(labels = xlabels_peakGene_praw.class) +
1200:           sum_real = table(peakGeneCorrelations.all[indexCur,]$class)[names(dist_class)[1]]
1201:           sum_rnd  = table(peakGeneCorrelations.all[indexCur,]$class)[names(dist_class)[2]]
1203:             dplyr::group_by(class) %>%
1204:             dplyr::count(.data$peak_gene.r.class) %>%
1205:             dplyr::mutate(nnorm = dplyr::case_when(class == !! (names(dist_class)[1]) ~ .data$n / (sum_real / sum_rnd), 
1210:           gD = ggplot2::ggplot(binData.r, ggplot2::aes(.data$peak_gene.r.class, .data$nnorm, group = .data$class, fill = .data$class)) + 
1212:             ggplot2::geom_line(ggplot2::aes(.data$peak_gene.r.class, .data$nnorm, group = .data$class, color= .data$class), stat = "identity") +
1213:             ggplot2::scale_fill_manual("Group", labels = names(dist_class), values = dist_class) +
1214:             ggplot2::scale_color_manual("Group", labels = names(dist_class), values = dist_class) +
1215:             ggplot2::scale_x_discrete(labels = xlabels_peakGene_r.class2, drop = FALSE) +
1262:       allVars = c("peak.annotation", "peak.GC.class", "peak.width", "peak.mean","peak.median",
1275:             dplyr::select("peak_gene.p_raw", tidyselect::all_of(varCur), "class", "r_positive", "peak_gene.p.raw.class", "peak_gene.distance") 
1278:             dplyr::select("peak_gene.p_raw", "class", "gene.CV", "peak.CV", "r_positive", "peak_gene.p.raw.class", "peak_gene.distance")
1284:         if (varCur %in% c("peak.annotation","peak.GC.class")) {
1290:           newColName = paste0(varCur, ".class")
1360:           # Class in the ggplot2::facet_wrap has been removed, as this is only for real data here
1371:             dplyr::group_by(class, .data[[newColName]], .data$peak_gene.p.raw.class, .data$r_positive) %>%
1374:             tidyr::complete(class, .data[[newColName]], .data$peak_gene.p.raw.class, .data$r_positive, fill = list(n = 0)) %>% # SOme cases might be missing
1375:             dplyr::group_by(class, .data[[newColName]], .data$peak_gene.p.raw.class) %>% # dont group by r_positive because we want to calculate the ratio within each group
1379:             dplyr::filter(.data$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)...
1385:           gB3 = ggplot2::ggplot(freq, ggplot2::aes(.data$peak_gene.p.raw.class, .data$ratio_pos_raw, fill = .data[[newColName]])) + 
1389:             ggplot2::scale_x_discrete(labels = xlabels_peakGene_praw.class) +
1395:             ggplot2::facet_wrap(~ factor(class), nrow = 2, scales = "free_y", strip.position = "left", labeller = ggplot2::labeller(class=freq_class)) 
1439:             ggplot2::facet_wrap(~ peak_gene.distance.class250k  + .data[[newColName]], nrow = nrows_plot, scales = "free_y") +
1442:             ggplot2::scale_fill_manual("Class for r", values = mycolors, labels = r_positive_label, drop = FALSE ) +
1463:       indexFilt = which(peakGeneCorrelations.all$peak_gene.distance_class_abs %in% 
1464:                           names(distance_class_abund)[which(distance_class_abund > 50)])
1470: ...(37 bytes skipped)...eCorrelations.all[indexFilt,], ggplot2::aes(.data$peak_gene.p_raw, color = .data$peak_gene.distance_class_abs)) + ggplot2::geom_density() + 
1473:             ggplot2::scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$peak_gene.distance_class_abs))) +
1483: ...(46 bytes skipped)...ations.all[indexFilt,], ggplot2::aes(.data$peak_gene.p_raw.robust, color = .data$peak_gene.distance_class_abs)) + ggplot2::geom_density() + 
1486:               ggplot2::scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$peak_gene.distance_class_abs))) +
1506:             ggplot2::facet_wrap(~ peak_gene.distance_class_abs,  ncol = 2, labeller = .customLabeler(table(peakGeneCorrelations.all$peak_gene.distance_class_abs))) +
1507:             ggplot2::scale_color_manual(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$r_positive)), values = r_pos_class) +
1522: ...(30 bytes skipped)...akGeneCorrelations.all[indexCur,], ggplot2::aes(.data$peak_gene.r, color = .data$peak_gene.distance_class_abs)) + ggplot2::geom_density() + 
1525:           ggplot2::scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexCur,]$peak_gene.distance_class_abs))) +
1565: #' Plot various network connectivity summaries for a \code{\linkS4class{GRN}} object
1576: #' @return The same \code{\linkS4class{GRN}} object, without modifications. 
1908: #' Plot general structure and connectivity statistics for a filtered \code{\linkS4class{GRN}} object
1919: #' @return The same \code{\linkS4class{GRN}} object, without modifications. 
1965:     totalVerteces = data.frame(Class = c("TF", "Peak", "Gene"),
1980:       gVertexDist = ggplot2::ggplot(totalVerteces, ggplot2::aes(x="", y=.data$Count, fill=.data$Class)) + ggplot2::geom_bar(stat="identity") +
2139: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
2308: #' Plot general structure & connectivity statistics for each community in a filtered \code{\linkS4class{GRN}}
2324: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
2418:         # Class: TF or gene
2419: ...(11 bytes skipped)...mmunityVertices = ggplot2::ggplot(communityVertices, ggplot2::aes(x = .data$community, fill = .data$Class)) +
2516: #' Plot community-based enrichment results for a filtered \code{\linkS4class{GRN}} object
2529: #' @return  The same \code{\linkS4class{GRN}} object, without modifications.
2887: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
3186:                                     .id = "class") %>%
3188:     dplyr::arrange(class, dplyr::desc(.data$Degree))
3193:   gDegrees = ggplot2::ggplot(degrees.table, ggplot2::aes(x=.data$Degree, fill = class)) +
3199:     ggplot2::facet_wrap(~class, labeller = ggplot2::labeller(class=.facetLabel(degrees.table)), scales = "free", ncol = 2) +
3204:     dplyr::filter(class =="gene") %>%
3209:     dplyr::filter(class =="TF") %>%
3316:     dplyr::group_by(class) %>%
3318:     dplyr::mutate(label = paste0(class, "\n(mean: ", round(mean, 1),
3324:   names(labels) <- summary$class
3334: ...(1 bytes skipped)...' This function can visualize a filtered eGRN in a very flexible manner and requires a \code{\linkS4class{GRN}} object as generated by \code{\link{build_eGRN_graph}}. 
3358: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
35:   checkmate::assertClass(GRN, "GRN")
70:     matrixCur = getCounts(GRN, type = "rna", asMatrix = TRUE, includeFiltered = !removeFiltered)
93:     matrixCur = getCounts(GRN, type = "peaks", asMatrix = TRUE, includeFiltered = !removeFiltered)
126:     checkmate::assertClass(counts, "DESeqDataSet")
135:     checkmate::assertMatrix(counts)
140:     checkmate::assertMatrix(counts)
471:   checkmate::assertClass(GRN, "GRN")
545: .generateTF_GC_diagnosticPlots <- function(TFCur, GC_classes_foreground.df, GC_classes_background.df, GC_classes_all.df, peaksForeground, peaksBackground, peaksBackgroundGC) {
562:   GC_classes_all2.df$n.bg.needed.relFreq[GC_classes_all2.df$type != "background_GC" | GC_classes_all2.df$n == 0] = ""
867:   checkmate::assertClass(GRN, "GRN")
1363:             ggplot2::geom_density(ggplot2::aes(color = .data$classNew), color = "black",  linetype = "dotted", alpha = 1) + 
1440:             ggplot2::xlab(xlabel) + ggplot2::ylab(paste0("Abundance for classes with n>=", nGroupsMin)) +  ggplot2::theme_bw() +
1459:       # Here, we focus on distance and exclude distance classes with too few points and create a new subset of the data
1461:       # Filter distance classes with too few points
1589:   checkmate::assertClass(GRN, "GRN")
1933:   checkmate::assertClass(GRN, "GRN")
2156:   checkmate::assertClass(GRN, "GRN")
2340:   checkmate::assertClass(GRN, "GRN")
2548:   checkmate::assertClass(GRN, "GRN")
2908:   checkmate::assertClass(GRN, "GRN")
3369:     checkmate::assertClass(GRN, "GRN")
1014:                       forcats::fct_explicit_na(addNA(cut(.data$peak_gene.distance, breaks = nClasses_distance, include.lowest = TRUE)), "random"),
1016:                                                                                       breaks = nClasses_distance, include.lowest = TRUE, ordered_result = TRUE)), "random"),
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()
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
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$class == "0" &
360:             class <- "UP"
362:             class <- "DOWN"
364:             class <- "0"
368:                          class = class,
398:             class <- "DOWN" ## regarding DISEASE
400:             class <- "UP" ## regarding DISEASE
403:                 class <- "UP"
405:                 class <- "DOWN"
407:                 class <- 0
411:     result <- data.frame(pvalue, class, stat,stringsAsFactors=FALSE)
427: #' @return \code{do_pca} returns a list with class \code{princomp}.
77:         stop("Only SummarizedExperiment or matrix classes accepted as data")
288:         stop("Only SummarizedExperiment or matrix classes accepted as data")
443:         stop("Only SummarizedExperiment or matrix classes accepted as data")
469:         stop("Only SummarizedExperiment or matrix classes accepted as data")
interactiveDisplay:R/ExpressionSet.R: [ ]
9:     shiny::tags$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) {
HDF5Array:R/H5SparseMatrixSeed-class.R: [ ]
280:         ans_class <- "CSC_H5SparseMatrixSeed"
660: .from_CSC_H5SparseMatrixSeed_to_dgCMatrix <- function(from)
676: .from_CSR_H5SparseMatrixSeed_to_dgCMatrix <- function(from)
61: t.CSC_H5SparseMatrixSeed <- function(x)
70: t.CSR_H5SparseMatrixSeed <- function(x)
258: H5SparseMatrixSeed <- function(filepath, group, subdata=NULL)
486: .extract_array_from_H5SparseMatrixSeed <- function(x, index)
530: .extract_sparse_array_from_H5SparseMatrixSeed <- function(x, index)
553: .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 ",
149:     ## We pass 'shape' thru as.vector() to drop its class attribute in case
286:         ans_class <- "CSR_H5SparseMatrixSeed"
300:     new2(ans_class, filepath=filepath, group=group,
6: setClass("H5SparseMatrixSeed",
52: setClass("CSC_H5SparseMatrixSeed", contains="H5SparseMatrixSeed")
53: setClass("CSR_H5SparseMatrixSeed", contains="H5SparseMatrixSeed")
209:             msg2 <- c("H5ADMatrix() constructor if you are trying ",
657: ### Coercion to dgCMatrix
665:     sparseMatrix(i=row_indices, p=indptr, x=data, dims=dim(from),
669: setAs("CSC_H5SparseMatrixSeed", "dgCMatrix",
670:     .from_CSC_H5SparseMatrixSeed_to_dgCMatrix
672: setAs("CSC_H5SparseMatrixSeed", "sparseMatrix",
673:     .from_CSC_H5SparseMatrixSeed_to_dgCMatrix
681:     sparseMatrix(j=col_indices, p=indptr, x=data, dims=dim(from),
685: setAs("CSR_H5SparseMatrixSeed", "dgCMatrix",
686:     .from_CSR_H5SparseMatrixSeed_to_dgCMatrix
688: setAs("CSR_H5SparseMatrixSeed", "sparseMatrix",
689:     .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",
256: ### Returns an H5SparseMatrixSeed derivative (can be either a
257: ### CSC_H5SparseMatrixSeed or CSR_H5SparseMatrixSeed object).
369: ### H5SparseMatrixSeed objects.
465: setMethod(".load_sparse_data", "CSC_H5SparseMatrixSeed",
473: setMethod(".load_sparse_data", "CSR_H5SparseMatrixSeed",
499: setMethod("extract_array", "H5SparseMatrixSeed",
500:     .extract_array_from_H5SparseMatrixSeed
509: setMethod("chunkdim", "CSC_H5SparseMatrixSeed", function(x) c(nrow(x), 1L))
511: setMethod("chunkdim", "CSR_H5SparseMatrixSeed", function(x) c(1L, ncol(x)))
518: setMethod("sparsity", "H5SparseMatrixSeed",
528: setMethod("is_sparse", "H5SparseMatrixSeed", function(x) TRUE)
536: setMethod("extract_sparse_array", "H5SparseMatrixSeed",
537:     .extract_sparse_array_from_H5SparseMatrixSeed
541: ### work just fine on an H5SparseMatrixSeed derivative (thanks to the
542: ### extract_sparse_array() method for H5SparseMatrixSeed objects defined
548: ### extract_sparse_array() method for H5SparseMatrixSeed objects would
557:     ## Unlike the extract_sparse_array() method for H5SparseMatrixSeed
563: setMethod("read_sparse_block", "H5SparseMatrixSeed",
564:     .read_sparse_block_from_H5SparseMatrixSeed
631: setMethod("extractNonzeroDataByCol", "CSC_H5SparseMatrixSeed",
646: setMethod("extractNonzeroDataByRow", "CSR_H5SparseMatrixSeed",
697: 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)
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)
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,
S4Vectors:R/DataFrame-class.R: [ ]
891:         to_class <- class(to)
953: make_class_info_for_DataFrame_display <- function(x)
20: ## class, as well as some of its methods e.g. names(), as.list() and lapply().
58:         ## class attribute.
59:         if (class(object) == "DataFrame") {
62:             ## change of the internals, only a change of the class attribute.
64:                 message("[updateObject] Setting class attribute of DataFrame ",
66:             class(object) <- class(new("DFrame"))
71:                 message("[updateObject] ", class(object), " object ",
210:     "Note that starting with BioC 3.10, the class attribute ",
219:   ## class() is broken when used within a validity method. See:
221:   #if (class(x) == "DataFrame")
319:         stop("cannot coerce class \"", class(var)[1L], "\" to a DataFrame")
498:     if (class(x)[[1L]] == "DataFrame")
713:     hasNonDefaultMethod(droplevels, class(xi)[1L]) ||
714:       hasS3Method("droplevels", class(xi))
779:                 col <- I(col)  # set AsIs class to protect column
782:                 df[[1L]] <- unclass(df[[1L]])  # drop AsIs class
812:         class(from) <- NULL
837:         class(from) <- "table"
892:         if (class(from) == "list") {
896:             if (is(ans, to_class))
898:             ans <- as(ans, to_class, strict=FALSE)
899:             ## Even though coercion from DataFrame to 'class(to)' "worked", it
916:             if (is(from, to_class))
918:             ans <- as(from, to_class, strict=FALSE)
920:                 stop(wmsg("coercion of ", class(from), " object to ", to_class,
941:     function(x) if (class(x) == "DFrame") "DataFrame" else class(x)
984:         m <- rbind(make_class_info_for_DataFrame_display(x), m)
993:         if (class(object) == "DataFrame") {
9: setClass("DataFrame",
18: ## DFrame is a concrete DataFrame subclass for representation of in-memory
24: setClass("DFrame",
305:     if (is(listData[[1L]], getClass("Annotated")))
810: ### But unclass() causes deep copy
834: setOldClass(c("xtabs", "table"))
905:             ## DataFrame to a DataFrame subclass is that it will set the
940: setMethod("classNameForDisplay", "DFrame",
955:     vapply(x, function(xi) paste0("<", classNameForDisplay(xi), ">"),
965:     cat(classNameForDisplay(x), " with ",
944: setMethod("makeNakedCharacterMatrixForDisplay", "DataFrame",
973:             m <- makeNakedCharacterMatrixForDisplay(x)
977:             m <- rbind(makeNakedCharacterMatrixForDisplay(head(x, nhead)),
979:                        makeNakedCharacterMatrixForDisplay(tail(x, ntail)))
IRanges:R/IPos-class.R: [ ]
495:     object_class <- classNameForDisplay(object)
534:         .COL2CLASS <- c(pos="integer")
535:         classinfo <- makeClassinfoRowForCompactPrinting(x, .COL2CLASS)
15: ### Combine the new "parallel slots" with those of the parent class. Make
16: ### sure to put the new parallel slots **first**. See R/Vector-class.R file
30: ### Combine the new "parallel slots" with those of the parent class. Make
31: ### sure to put the new parallel slots **first**. See R/Vector-class.R file
52:     "Starting with BioC 3.10, the class attribute of all ",
60:     if (class(x) == "IPos")
110:     if (class(object) != "IPos")
121:             message("[updateObject] ", class(object), " object is current.\n",
127:         message("[updateObject] ", class(object), " object ",
132:     if (class(object) == "UnstitchedIPos") {
379:         stop(wmsg("all the ranges in the ", class(from), " object to ",
499:     paste0(object_class, " object with ", object_len, " ",
522:         stop(c(wmsg("This ", class(x), " object uses internal representation ",
7: setClass("IPos",
23: setClass("UnstitchedIPos",
38: setClass("StitchedIPos",
518: show_IPos <- function(x, margin="", print.classinfo=FALSE)
533:     if (print.classinfo) {
537:         stopifnot(identical(colnames(classinfo), colnames(out)))
538:         out <- rbind(classinfo, out)
549:     function(object) show_IPos(object, print.classinfo=TRUE)
514: setMethod("makeNakedCharacterMatrixForDisplay", "IPos",
529:     ## makePrettyMatrixForCompactPrinting() assumes that head() and tail()
532:     out <- makePrettyMatrixForCompactPrinting(xx)
BSgenome:R/BSgenomeViews-class.R: [ ]
164:         .COL2CLASS <- c(
5: ### The BSgenomeViews class is a container for storing a set of genomic
13: ### TODO: A cleaner class design would be to have 2 abstractions: IViews and
15: ### IRanges object. Note that this is how the current Views class is defined.
18: ### more general Views class that contains List and has a subject slot.
156:     cat(class(x), " object with ",
171:             S4Vectors:::makeClassinfoRowForCompactPrinting(x, .COL2CLASS)
11: ### reasons that we didn't make GRanges a subclass of IRanges.
19: ### BSgenomeViews below then should become a subclass of GViews.
21: setClass("BSgenomeViews",
151:                                  print.classinfo=FALSE,
163:     if (print.classinfo) {
170:         classinfo <-
173:         stopifnot(identical(colnames(classinfo), colnames(out)))
174:         out <- rbind(classinfo, out)
191:                           print.classinfo=TRUE, print.seqinfo=TRUE)
388: #setMethod("consensusMatrix", "BSgenomeViews",
396: setMethod("consensusMatrix", "BSgenomeViews",
17: ### GRanges object. Both IViews and GViews would be direct subclasses of a
161:     out <- S4Vectors:::makePrettyMatrixForCompactPrinting(x,
SummarizedExperiment:R/Assays-class.R: [ ]
190:         ans_class <- class(x)
207:         ans_class <- class(x)
13: ###       - Matrix-like methods: dim, [, [<-, rbind, cbind
46: ### Assays class
192:         as(callGeneric(), ans_class)
209:         ans <- as(callGeneric(), ans_class)
241:     as(endoapply(assays, extract_assay_subset), class(x))
264:     as(mendoapply(replace_assay_subset, assays, values), class(x))
314:     as(SimpleList(res), class(getListElement(objects, 1L)))
333: ### Having "arbind" and "acbind" methods for Matrix objects will make rbind()
334: ### and cbind() work on Assays objects with Matrix list elements.
337: ### to make IRanges depend on the Matrix package.
338: setMethod("arbind", "Matrix", function(...) rbind(...))
339: setMethod("acbind", "Matrix", function(...) cbind(...))
343: ### SimpleAssays class
365: ### ShallowSimpleListAssays class
394: ### AssaysInEnv class
418:         value <- S4Vectors:::normarg_names(value, class(x), length(x))
15: ### An Assays concrete subclass needs to implement (b) (required) plus
49: setClass("Assays", contains="RectangularData", representation("VIRTUAL"))
350: setClass("SimpleAssays",
367: ### WARNING: Looks like reference classes as implemented in the methods
377: .ShallowData <- setRefClass("ShallowData",
380: .ShallowSimpleListAssays0 <- setRefClass("ShallowSimpleListAssays",
403: setClass("AssaysInEnv",
GenomicAlignments:R/OverlapEncodings-class.R: [ ]
242:     x_class <- class(x)
253:         .COL2CLASS <- c(
259:         classinfo <- S4Vectors:::makeClassinfoRowForCompactPrinting(x,
22: ### Combine the new "parallel slots" with those of the parent class. Make
23: ### sure to put the new parallel slots **first**. See R/Vector-class.R file
260:                                      .COL2CLASS)
7: setClass("OverlapEncodings",
240: showOverlapEncodings <- function(x, margin="", print.classinfo=FALSE)
246:     cat(classNameForDisplay(x), " object of length ", x_len,
252:     if (print.classinfo) {
262:         stopifnot(identical(colnames(classinfo), colnames(out)))
263:         out <- rbind(classinfo, out)
275:         showOverlapEncodings(object, margin="  ", print.classinfo=TRUE)
250:     out <- S4Vectors:::makePrettyMatrixForCompactPrinting(x,
scClassify:R/train_scClassify.R: [ ]
437:       class_tmp <- currentClass(cellTypes, cutree_list[[i]])
412: currentClass <- function(cellTypes, cutree_res){
442:         trainClass <- class_tmp[trainIdx]
47: train_scClassify <- function(exprsMat_train,
176:       trainClassList <- list()
212: train_scClassifySingle <- function(exprsMat_train,
20: #' @param BPPARAM  A \code{BiocParallelParam} class object
23: #' @param returnList A logical input indicates whether the output will be class of list
274:       Matrix::rowMeans(as.matrix(exprsMat_train[de, cellTypes_train == x]))))
438:       names(class_tmp) <- colnames(exprsMat)
452:           model[[j]] <- list(train = Matrix::t(exprsMat[na.omit(hvg[[j]]),
33: #' trainClass <- train_scClassify(exprsMat_train = exprsMat_xin_subset,
108:   if (any(c("matrix", "dgCMatrix") %in% is(exprsMat_train))) {
443:         if (length(unique(trainClass)) != 1) {
446:                                        trainClass,
455:                              y = as.factor(trainClass))
1: #' Training scClassify model
24: #' @param ... Other input for predict_scClassify for the case when weights calculation
26: #' @return list of results or an object of \code{scClassifyTrainModel}
30: #' data("scClassify_example")
31: #' xin_cellTypes <- scClassify_example$xin_cellTypes
32: #' exprsMat_xin_subset <- scClassify_example$exprsMat_xin_subset
130:   ### train_scClassify
134:       trainRes[[train_list_idx]] <- train_scClassifySingle(exprsMat_train[[train_list_idx]],
150:     trainRes <- train_scClassifySingle(exprsMat_train,
178:         trainClassList[[train_list_idx]] <- .scClassifyTrainModel(
188:       trainClassList <- scClassifyTrainModelList(trainClassList)
190:       trainClassList <- .scClassifyTrainModel(
200:     return(trainClassList)
242:             (scClassify requires a log-transformed normalised input)")
343:     selfTrainRes <- predict_scClassify(exprsMat_test = exprsMat_train,
rhdf5client:inst/bad/HSDS_Matrix.R: [ ]
41: HSDS_Matrix = function(url, path, title) {
16: HSDS_Matrix_OLD = function(url, path) {
6: #' This class is deprecated and will be defunct in the next release.
13: #' HSDS_Matrix
30: #' This class is deprecated and will be defunct in the next release.
38: #' HSDS_Matrix(URL_hsds(), "/shared/bioconductor/darmgcls.h5")
4: #' simplify construction of DelayedMatrix from url and path in HSDS
28: #' simplify construction of DelayedMatrix from url and path in HSDS
IRanges:R/IPosRanges-class.R: [ ]
122:     object_class <- classNameForDisplay(object)
159:         .COL2CLASS <- c(
164:         classinfo <- makeClassinfoRowForCompactPrinting(x, .COL2CLASS)
38:         target <- new(class(object))@elementType
43:                         class(object), " object is current.\n",
47:                 message("[updateObject] elementType slot of ", class(object),
126:     paste0(object_class, " object with ", object_len, " ",
193:         stop(wmsg(class(x), " objects don't support [[, as.list(), ",
12: setClass("IPosRanges",
147: show_IPosRanges <- function(x, margin="", print.classinfo=FALSE)
158:     if (print.classinfo) {
166:         stopifnot(identical(colnames(classinfo), colnames(out)))
167:         out <- rbind(classinfo, out)
178:     function(object) show_IPosRanges(object, print.classinfo=TRUE)
8: ### The direct IPosRanges subclasses defined in the IRanges package are:
143: setMethod("makeNakedCharacterMatrixForDisplay", "IPosRanges",
150:     ## makePrettyMatrixForCompactPrinting() assumes that 'x' is subsettable
157:     out <- makePrettyMatrixForCompactPrinting(x)
EnMCB:R/utils.R: [ ]
779:   class.vector <- as.factor(class_vector)
51: removeobjectclass<- function(x){
827: buildIncidenceMatrix<-function (gene.ids, annotation) 
42:   class(x) <- c("ridgemat", class(x))
47:   class(x) <- c("mcb.coxph.penal", class(x))
52:   class(x) <- setdiff(class(x),"mcb.coxph.penal")
488:   if (class(ensemble_model$stacking)[1] == "cv.glmnet")
490:   else if (class(ensemble_model$stacking)[1] == "cph")
728: #error metrics -- Confusion Matrix
772: findAttractors<-function (methylation_matrix, annotation, class_vector, 
787:   new.order <- order(class.vector, colnames(dat.detect.w))
789:   class.vector <- class.vector[new.order]
803:     }, x = class.vector)
809:     }, x = class.vector)
82:     object = removeobjectclass(object)
784:   incidence.matrix <- buildIncidenceMatrix(rownames(dat.detect.w), annotation)
canceR:R/GSEA.1.0.R: [ ]
65:      class.labels1 <- matrix(0, nrow=Ns, ncol=nperm)
66:      class.labels2 <- matrix(0, nrow=Ns, ncol=nperm)
85:      class1.size <- length(C[[1]])
86:      class2.size <- length(C[[2]])
87:      class1.index <- seq(1, class1.size, 1)
88:      class2.index <- seq(class1.size + 1, class1.size + class2.size, 1)
91:         class1.subset <- sample(class1.index, size = ceiling(class1.size*fraction), replace = replace)
92:         class2.subset <- sample(class2.index, size = ceiling(class2.size*fraction), replace = replace)
93:         class1.subset.size <- length(class1.subset)
94:         class2.subset.size <- length(class2.subset)
134:            class1.label1.subset <- sample(class1.subset, size = ceiling(class1.subset.size*fraction.class1))
135:            class2.label1.subset <- sample(class2.subset, size = floor(class2.subset.size*fraction.class1))
500:         class1.size <- length(C[[1]])
501:         class2.size <- length(C[[2]])
612:       class.list <- unlist(strsplit(cls.cont[[3]], " "))
618:       class.v <- vector(length=s, mode="numeric")
995:   class.labels <- CLS$class.v
996:   class.phen <- CLS$phen
63:      reshuffled.class.labels1 <- matrix(0, nrow=Ns, ncol=nperm)
64:      reshuffled.class.labels2 <- matrix(0, nrow=Ns, ncol=nperm)
95:         subset.class1 <- rep(0, class1.size)
101:         subset.class2 <- rep(0, class2.size)
108:         fraction.class1 <- class1.size/Ns
109:         fraction.class2 <- class2.size/Ns
16: GSEA.GeneRanking <- function(A, class.labels, gene.labels, nperm, permutation.type = 0, sigma.correction = "GeneCluster", fraction=1.0, r...(38 bytes skipped)...
31: #   A: Matrix of gene expression values (rows are genes, columns are samples) 
32: #   class.labels: Phenotype of class disticntion of interest. A vector of binary labels having first the 1's and then the 0's 
42: #   s2n.matrix: Matrix with random permuted or bootstraps signal to noise ratios (rows are genes, columns are permutations...(26 bytes skipped)...
43: #   obs.s2n.matrix: Matrix with observed signal to noise ratios (rows are genes, columns are boostraps subsamplings. If fracti...(58 bytes skipped)...
44: #   order.matrix: Matrix with the orderings that will sort the columns of the obs.s2n.matrix in decreasing s2n order
45: #   obs.order.matrix: Matrix with the orderings that will sort the columns of the s2n.matrix in decreasing s2n order
84:      C <- split(class.labels, class.labels)
96:         for (i in 1:class1.size) {
97:             if (is.element(class1.index[i], class1.subset)) {
98:                 subset.class1[i] <- 1
102:         for (i in 1:class2.size) {
103:             if (is.element(class2.index[i], class2.subset)) {
104:                 subset.class2[i] <- 1
107:         subset.mask[, r] <- as.numeric(c(subset.class1, subset.class2))
112:            full.subset <- c(class1.subset, class2.subset)
113:            label1.subset <- sample(full.subset, size = Ns * fraction.class1)
114:            reshuffled.class.labels1[, r] <- rep(0, Ns)
115:            reshuffled.class.labels2[, r] <- rep(0, Ns)
116:            class.labels1[, r] <- rep(0, Ns)
117:            class.labels2[, r] <- rep(0, Ns)
121:                reshuffled.class.labels1[i, r] <- m1
122:                reshuffled.class.labels2[i, r] <- m2 - m1
123:                if (i <= class1.size) {
124:                  class.labels1[i, r] <- m2
125:                  class.labels2[i, r] <- 0
127:                   class.labels1[i, r] <- 0
128:                   class.labels2[i, r] <- m2
136:            reshuffled.class.labels1[, r] <- rep(0, Ns)
137:            reshuffled.class.labels2[, r] <- rep(0, Ns)
138:            class.labels1[, r] <- rep(0, Ns)
139:            class.labels2[, r] <- rep(0, Ns)
141:                if (i <= class1.size) {
142:                   m1 <- sum(!is.na(match(class1.label1.subset, i)))
143:                   m2 <- sum(!is.na(match(class1.subset, i)))
144:                   reshuffled.class.labels1[i, r] <- m1
145:                   reshuffled.class.labels2[i, r] <- m2 - m1
146:                   class.labels1[i, r] <- m2
147:                   class.labels2[i, r] <- 0
149:                   m1 <- sum(!is.na(match(class2.label1.subset, i)))
150:                   m2 <- sum(!is.na(match(class2.subset, i)))
151:                   reshuffled.class.labels1[i, r] <- m1
152:                   reshuffled.class.labels2[i, r] <- m2 - m1
153:                   class.labels1[i, r] <- 0
154:                   class.labels2[i, r] <- m2
162:      P <- reshuffled.class.labels1 * subset.mask
172:      P <- reshuffled.class.labels2 * subset.mask
213:      P <- class.labels1 * subset.mask
223:      P <- class.labels2 * subset.mask
475: ...(159 bytes skipped)...", "#FF0D1D", "#FF0000") # blue-pinkogram colors. The first and last are the colors to indicate the class vector (phenotype). This is the 1998-vintage, pre-gene cluster, original pinkogram color map
491:             row.names <- c(row.names[seq(n.rows, 1, -1)], "Class")
502:         axis(3, at=c(floor(class1.size/2),class1.size + floor(class2.size/2)), labels=col.classes, tick=FALSE, las = 1, cex.axis=1.25, font.axis=2, line=-1)
598: # Reads a class vector CLS file and defines phenotype and class labels vectors for the samples in a gene expression file (RES or GCT format)
613:       s <- length(class.list)
614:       t <- table(class.list)
625:              if (class.list[i] == phen[j]) {
626:                 class.v[i] <- phen.label[j]
630:       return(list(phen = phen, class.v = class.v))
795: #   input.cls:  Input class vector (phenotype) file in CLS format 
988:   # Read input class vector
999:      phen1 <- class.phen[2]
1000:      phen2 <- class.phen[1]
1002:      phen1 <- class.phen[1]
1003:      phen2 <- class.phen[2]
1008:  col.index <- order(class.labels, decreasing= FALSE)
1009:  class.labels <- class.labels[col.index]
1166:    O <- GSEA.GeneRanking(A, class.labels, gene.labels, call.nperm, permutation.type = perm.type, sigma.correction = "GeneCluster", fr...(62 bytes skipped)...
1831:       GSEA.HeatMapPlot(V = C, col.labels = class.labels, col.classes = class.phen, main = "Heat Map for Genes in Dataset")
2032:             GSEA.HeatMapPlot(V = pinko, row.names = pinko.gene.names, col.labels = class.labels, col.classes = class.phen, col.names = sample.names, main =" Heat Map for Genes in Gene Set", xlab=" ", ylab=" ")
446: GSEA.HeatMapPlot <- function(V, row.names = FALSE, col.labels, col.classes, col.names = FALSE, main = " ", xlab=" ", ylab=" ") {
GeneGeneInteR:R/PLSPM.R: [ ]
859:     mvs_class = vapply(MV, class,FUN.value="character")
964:     mvs_class = vapply(DF, class,FUN.VALUE="character")
975:   mvs_class = vapply(MV, class,FUN.VALUE="character")
60: 	class(res) <- "GGItest"
69: 	class(res) <- "GGItest"
111: #	class(res) <- "GGItest"
129: 	class(res) <- "htest"
281:     class(res) = "plspm"
860:     mvs_as_factors <- mvs_class == "factor"
965:     mvs_as_factors <- mvs_class == "factor"
976:   mvs_as_factors <- mvs_class == "factor"
14:   } else if(!is(G1,"SnpMatrix")){
15:     stop("G1 must be a SnpMatrix object.")
16:   } else if(!is(G2,"SnpMatrix")){
17:     stop("G2 must be a SnpMatrix object")
21:     stop("Y and both SnpMatrix objects must contain the same number of individuals.")
23:     stop("The snpMatrix must be complete. No NAs are allowed.")
25:     stop("The snpMatrix must be complete. No NAs are allowed.")
MIGSA:R/MGSZ.R: [ ]
437:   ev.p.val.class <- rep(0, length(realES))
440:     ev.param.class <- ismev::gum.fit(as.vector(permESs[, k]),
443:     logEVcdf(realES[[k]], ev.param.class)
239: mGszEbayes <- function(exprMatrix, fit_options) {
241:   design <- designMatrix(fit_options)
243:   fit1 <- lmFit(exprMatrix, design)
253: voomLimaRank <- function(exprMatrix, fit_options) {
255:   design <- designMatrix(fit_options)
257:   newExpr <- voom(exprMatrix, design)
309:     bplapply(seq_len(nPerm), function(i, designMatrix) {
311:       permDesign <- designMatrix(fitOptions)[perms[i, ], ]
317:     }, designMatrix = designMatrix)
73: #' @importClassesFrom edgeR DGEList
kebabs:src/svm.cpp: [ ]
1595: static void solve_one_class(
2570: int svm_get_nr_class(const svm_model *model)
201: class QMatrix {
206: 	virtual ~QMatrix() {}
1381: class ONE_CLASS_Q: public Kernel
1384: 	ONE_CLASS_Q(const svm_problem& prob, const svm_parameter& param)
1417: 	~ONE_CLASS_Q()
1894: static void multiclass_probability(int k, double **r, double *p)
2089: static void svm_group_classes(const svm_problem *prob, int *nr_class_ret, int **label_ret, int **start_ret, int **count_ret, int *perm)
18: template <class T> static inline T min(T x,T y) { return (x<y)?x:y; }
21: template <class T> static inline T max(T x,T y) { return (x>y)?x:y; }
23: template <class T> static inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
24: template <class S, class T> static inline void clone(T*& dst, S* src, int n)
74: class Cache
199: // the member function get_Q is for getting one column from the Q Matrix
209: class Kernel: public QMatrix {
458: class Solver {
1074: class Solver_NU: public Solver
1331: class SVC_Q: public Kernel
1427: class SVR_Q: public Kernel
1620: 	s.Solve(l, ONE_CLASS_Q(*prob,*param), zeros, ones,
1726: 		case ONE_CLASS:
1727: 			solve_one_class(prob,param,alpha,&si);
1873: 			info("Line search fails in two-class probability estimates\n");
1879: 		info("Reaching maximal iterations in two-class probability estimates\n");
2087: // label: label name, start: begin of each class, count: #data of classes, perm: indices to the original data
2092: 	int max_nr_class = 16;
2093: 	int nr_class = 0;
2094: 	int *label = Malloc(int,max_nr_class);
2095: 	int *count = Malloc(int,max_nr_class);
2103: 		for(j=0;j<nr_class;j++)
2112: 		if(j == nr_class)
2114: 			if(nr_class == max_nr_class)
2116: 				max_nr_class *= 2;
2117: 				label = (int *)realloc(label,max_nr_class*sizeof(int));
2118: 				count = (int *)realloc(count,max_nr_class*sizeof(int));
2120: 			label[nr_class] = this_label;
2121: 			count[nr_class] = 1;
2122: 			++nr_class;
2128: 	// However, for two-class sets with -1/+1 labels and -1 appears first, 
2131: 	if (nr_class == 2 && label[0] == -1 && label[1] == 1)
2144: 	int *start = Malloc(int,nr_class);
2146: 	for(i=1;i<nr_class;i++)
2154: 	for(i=1;i<nr_class;i++)
2157: 	*nr_class_ret = nr_class;
2173: 	if(param->svm_type == ONE_CLASS ||
2177: 		// regression or one-class-svm
2178: 		model->nr_class = 2;
2224: 		int nr_class;
2230: 		// group training data of the same class
2231: 		svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
2232: 		if(nr_class == 1) 
2233: 			info("WARNING: training data in only one class. See README for details.\n");
2246: 		double *weighted_C = Malloc(double, nr_class);
2247: 		for(i=0;i<nr_class;i++)
2252: 			for(j=0;j<nr_class;j++)
2255: 			if(j == nr_class)
2256: 				REprintf("WARNING: class label %d specified in weight is not found\n", param->weight_label[i]);
2266: 		decision_function *f = Malloc(decision_function,nr_class*(nr_class-1)/2);
2271: 			probA=Malloc(double,nr_class*(nr_class-1)/2);
2272: 			probB=Malloc(double,nr_class*(nr_class-1)/2);
2276: 		for(i=0;i<nr_class;i++)
2277: 			for(int j=i+1;j<nr_class;j++)
2318: 		model->nr_class = nr_class;
2320: 		model->label = Malloc(int,nr_class);
2321: 		for(i=0;i<nr_class;i++)
2324: 		model->rho = Malloc(double,nr_class*(nr_class-1)/2);
2325: 		for(i=0;i<nr_class*(nr_class-1)/2;i++)
2330: 			model->probA = Malloc(double,nr_class*(nr_class-1)/2);
2331: 			model->probB = Malloc(double,nr_class*(nr_class-1)/2);
2332: 			for(i=0;i<nr_class*(nr_class-1)/2;i++)
2345: 		int *nz_count = Malloc(int,nr_class);
2346: 		model->nSV = Malloc(int,nr_class);
2347: 		for(i=0;i<nr_class;i++)
2377: 		int *nz_start = Malloc(int,nr_class);
2379: 		for(i=1;i<nr_class;i++)
2382: 		model->sv_coef = Malloc(double *,nr_class-1);
2383: 		for(i=0;i<nr_class-1;i++)
2387: 		for(i=0;i<nr_class;i++)
2388: 			for(int j=i+1;j<nr_class;j++)
2420: 		for(i=0;i<nr_class*(nr_class-1)/2;i++)
2436: 	int nr_class;
2445: 	// Each class to l folds -> some folds may have zero elements
2452: 		svm_group_classes(prob,&nr_class,&label,&start,&count,perm);
2460: 		for (c=0; c<nr_class; c++) 
2469: 			for (c=0; c<nr_class;c++)
2475: 		for (c=0; c<nr_class;c++)
2539: 			double *prob_estimates=Malloc(double,svm_get_nr_class(submodel));
2572: 	return model->nr_class;
2578: 		for(int i=0;i<model->nr_class;i++)
2609: 	if(model->param.svm_type == ONE_CLASS ||
2625: 		if(model->param.svm_type == ONE_CLASS)
2632: 		int nr_class = model->nr_class;
2643: 		int *start = Malloc(int,nr_class);
2645: 		for(i=1;i<nr_class;i++)
2648: 		int *vote = Malloc(int,nr_class);
2649: 		for(i=0;i<nr_class;i++)
2653: 		for(i=0;i<nr_class;i++)
2654: 			for(int j=i+1;j<nr_class;j++)
2680: 		for(i=1;i<nr_class;i++)
2693: 	int nr_class = model->nr_class;
2695: 	if(model->param.svm_type == ONE_CLASS ||
2700: 		dec_values = Malloc(double, nr_class*(nr_class-1)/2);
2713: 		int nr_class = model->nr_class;
2714: 		double *dec_values = Malloc(double, nr_class*(nr_class-1)/2);
2718: 		double **pairwise_prob=Malloc(double *,nr_class);
2719: 		for(i=0;i<nr_class;i++)
2720: 			pairwise_prob[i]=Malloc(double,nr_class);
2722: 		for(i=0;i<nr_class;i++)
2723: 			for(int j=i+1;j<nr_class;j++)
2729: 		multiclass_probability(nr_class,pairwise_prob,prob_estimates);
2732: 		for(i=1;i<nr_class;i++)
2735: 		for(i=0;i<nr_class;i++)
2747: 	"c_svc","nu_svc","one_class","epsilon_svr","nu_svr",NULL
2777: 	int nr_class = model->nr_class;
2779: 	fprintf(fp, "nr_class %d\n", nr_class);
2784: 		for(int i=0;i<nr_class*(nr_class-1)/2;i++)
2792: 		for(int i=0;i<nr_class;i++)
2800: 		for(int i=0;i<nr_class*(nr_class-1)/2;i++)
2807: 		for(int i=0;i<nr_class*(nr_class-1)/2;i++)
2815: 		for(int i=0;i<nr_class;i++)
2830: 		for(int j=0;j<nr_class-1;j++)
2943: 		else if(strcmp(cmd,"nr_class")==0)
2944: 			FSCANF(fp,"%d",&model->nr_class);
2949: 			int n = model->nr_class * (model->nr_class-1)/2;
2956: 			int n = model->nr_class;
2963: 			int n = model->nr_class * (model->nr_class-1)/2;
2970: 			int n = model->nr_class * (model->nr_class-1)/2;
2977: 			int n = model->nr_class;
3076: 	int m = model->nr_class - 1;
3172: 		for(int i=0;i<model_ptr->nr_class-1;i++)
3224: 	   svm_type != ONE_CLASS &&
3260: 	   svm_type == ONE_CLASS ||
3278: 	   svm_type == ONE_CLASS)
3279: 		return "one-class SVM probability output not supported yet";
3287: 		int max_nr_class = 16;
3288: 		int nr_class = 0;
3289: 		int *label = Malloc(int,max_nr_class);
3290: 		int *count = Malloc(int,max_nr_class);
3297: 			for(j=0;j<nr_class;j++)
3303: 			if(j == nr_class)
3305: 				if(nr_class == max_nr_class)
3307: 					max_nr_class *= 2;
3308: 					label = (int *)realloc(label,max_nr_class*sizeof(int));
3309: 					count = (int *)realloc(count,max_nr_class*sizeof(int));
3311: 				label[nr_class] = this_label;
3312: 				count[nr_class] = 1;
3313: 				++nr_class;
3317: 		for(i=0;i<nr_class;i++)
3320: 			for(int j=i+1;j<nr_class;j++)
471: 	void Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
481: 	const QMatrix *Q;
569: void Solver::Solve(int l, const QMatrix& Q, const double *p_, const schar *y_,
1070: // Solver for nu-svm classification and regression
1078: 	void Solve(int l, const QMatrix& Q, const double *p, const schar *y,
1893: // Method 2 from the multiclass_prob paper by Wu, Lin, and Weng
1951: 		info("Exceeds max_iter in multiclass_prob\n");
2222: 		// classification
2390: 				// classifier (i,j): coefficients with
GSgalgoR:R/galgo.R: [ ]
390:         class_results <- mapply(cluster_classify, test_a, centroids,
392:         cluster_class <- unlist(class_results)
129:             numInClass <- table(y)
44: #' Galgo Object class
53: galgo.Obj <- setClass( # Set the name for the class
118:         if (class(y)[1] == "Surv") {y <- y[, "time"]}
393:         cluster_class <- cluster_class[order(as.vector(unlist(flds)))]
394:         fit_silhouette <- mean(cluster::silhouette(cluster_class,
396:         fit_differences <- surv_fitness(surv_obj, cluster_class, period)
684:     # Matrix with random TRUE false with uniform distribution,
2: #' robust transcriptomic classifiers associated with patient outcome across
13: #' classification of tumors is a difficult task and the results obtained
131:             for (i in seq_len(length(numInClass))) {
132:                 min_reps <- numInClass[i] %/% k
134:                     spares <- numInClass[i] %% k
139:                     foldVector[which(y == names(numInClass)[i])] <-
143:                     foldVector[which(y == names(numInClass)[i])] <-
144:                         sample(seq_len(k), size = numInClass[i]
227: #' @param clustclass a numeric vector with the group label for each patient
254: #' surv_fitness(OS, clustclass = clinical$grade, period = 3650)
255: surv_fitness <- function(OS, clustclass, period) {
259:                 RMST(survival::survfit(OS ~ clustclass), rmean = period)
CiteFuse:R/QCfunctions.R: [ ]
570:     doubletClassify_between_class <- ifelse(!hto_cellHash_mix_label %in%
833:     doubletClassify_within_class <- ifelse(doubletClassify_within_label ==
825:     doubletClassify_within_label <- apply(batch_doublets_mat, 1, function(res) {
10: #' \code{dgCMatrix} class)
30: #' @importFrom Matrix rowSums
90:                 rowsums <- Matrix::rowSums(exprs)
164: #' @importFrom Matrix readMM sparseMatrix
260:         exprs_mat <- Matrix::sparseMatrix(i = indices + 1,
272:         exprs_mat <- Matrix::readMM(mat_path)
341: #' @importFrom Matrix rowMeans
457:     ref <- Matrix::rowMeans(logSet)
489: #' @importFrom Matrix rowSums
519:     hto_cellHash_log <- hto_cellHash_log[Matrix::rowSums(hto_cellHash_log) >
577:     sce$doubletClassify_between_class <- doubletClassify_between_class
782:     sce$nUMI <- Matrix::colSums(SingleCellExperiment::counts(sce))
838:     sce$doubletClassify_within_class <- doubletClassify_within_class
48:              "dgCMatrix" %in% methods::is(exprsMat),
54:         if (any(!unlist(lapply(exprsMat, function(x) "dgCMatrix" %in% is(x))) &
62:             "dgCMatrix" %in% methods::is(exprsMat))) {
79:         if (!"dgCMatrix" %in% methods::is(exprs)) {
80:             exprs <- methods::as(exprs, "dgCMatrix")
89:             if ("dgCMatrix" %in% methods::is(exprsMat)) {
273:         exprs_mat <- methods::as(exprs_mat, "dgCMatrix")
576:     sce$doubletClassify_between_label <- hto_cellHash_mix_label
578:     doublet_res <- list(doubletClassify_between_threshold = hto_threshold,
579:                         doubletClassify_between_resultsMat = hto_cellHash_pass)
676:     if (!"doubletClassify_between_label" %in% colnames(colData(sce))) {
677:         warning("Haven't performed doubletClassify() yet!")
692:         hto_cellHash_pass <- metadata(sce)[["doubletClassify_between_resultsMat"]]
697:         hto_threshold <- metadata(sce)[["doubletClassify_between_threshold"]]
799:     if (!"doubletClassify_between_label" %in% colnames(colData(sce))) {
800:         stop("Haven't performed doubletClassify_between() yet!")
803:     hto_threshold <- metadata(sce)[["doubletClassify_between_threshold"]]
808:     hto_cellHash_mix_label <- sce$doubletClassify_between_label
837:     sce$doubletClassify_within_label <- doubletClassify_within_label
841:                                   list(doubletClassify_within_resultsMat =
rCGH:R/AllHelperFunctions.R: [ ]
347: .readSNP6Matrix <- function(filePath, startAt, useProbes, verbose){
111: .readAgilentMatrix <- function(filePath, verbose){
419: .readCytoScanMatrix <- function(filePath, startAt, useProbes, verbose){
342:     cnSet <- .readSNP6Matrix(filePath, startAt, useProbes, verbose)
414:     cnSet <- .readCytoScanMatrix(filePath, startAt, useProbes, verbose)
628:     K <- model$classification
666:     K <- model$classification
SpeCond:R/fct_SpeCond_2.R: [ ]
387:   M_col_class=M_col
311:   classification=fit_p$classification
331:   classification=rep(10,length(expression_values))
589: createParameterMatrix <- function(param.detection=NULL,beta.1=NULL,beta.2=NULL,lambda.1=NULL,lambda.2=NULL,per.1=NULL,per...(93 bytes skipped)...
511:   M_class_col=t(sapply(1:length(specificResult$fit), function(i) changeColorClassification(specificResult$fit[[i]]$classification)))
385: changeColorClassification <- function(M_col){
930: getMatrixFromExpressionSet <- function(expSet,condition.factor=NULL,condition.method=c("mean","median","max")...(2 bytes skipped)...
388:   M_col_class[M_col==1] <- 4
389:   M_col_class[M_col==2] <- 3
390:   M_col_class[M_col==3] <- "orange"
391:   M_col_class[M_col==4] <- "purple"
392:   M_col_class[M_col==5] <- "cyan"
393:   M_col_class[M_col==10] <- "black"
394:   return(M_col_class)
530:     l=sapply(1:nrow(expressionMatrix), function(i) createSingleGeneHtmlPage(index.html.link,prefix.file,i,row.names(expressionMatrix)[i],expressionMatrix...(43 bytes skipped)...cResult$fit[[i]],specificResult$L.specific.result$specific[i],specificResult$fit[[i]]$NorMixParam,M_class_col[i,],specificResult$L.specific.result$M.specific.all[i,],specificResult$L.specific.result$L.pv[[...(98 bytes skipped)...
552: ...(25 bytes skipped)...e.html.ids), function(i) createSingleGeneHtmlPage(index.html.link,prefix.file,i,row.names(expressionMatrix)[gene.html.ids[i]],expressionMatrix...(103 bytes skipped)...t$L.specific.result$specific[gene.html.ids[i]],specificResult$fit[[gene.html.ids[i]]]$NorMixParam,M_class_col[gene.html.ids[i],],specificResult$L.specific.result$M.specific.all[gene.html.ids[i],],specificR...(188 bytes skipped)...
945:         stop("condition must be of class factor")
223:         diff_median=rbind(diff_median,c(i,j,abs(median(expression_values[which(fit2_p$classification==i)])-median(expression_values[which(fit2_p$classification==j)]))))
323:   L_p=list(G_initial,G,NorMixParam,classification)
324:   names(L_p)=c("G_initial","G","NorMixParam","classification")
332:   names(classification)=colnames
337:     classification=fit_p$classification
359:       classification[-specific_outlier_step1]=1 
364:     classification[names(mclust2$classification)]=mclust2$classification
371:   L_p=list(G_initial,G,NorMixParam,classification,specific_outlier_step1)
372:   names(L_p)=c("G_initial","G","NorMixParam","classification","specific_outlier_step1")
470: getGeneHtmlPage <- function(expressionMatrix,specificResult,name.index.html="index.html",prefix.file=NULL, outdir="Single_result_pages",gene.htm...(30 bytes skipped)...
482:       stop("Error you need to enter a prefix.file value or to use a specificResult attribute of classe sp_list containing a prefix.file value (object inside the SpeCond() result value or the result of ...(35 bytes skipped)...
491:   if(nrow(expressionMatrix)<10 && !is.null(gene.html.ids)){
492:     gene.html.ids=c(1:nrow(expressionMatrix))
497:     p_identic_row_names=row.names(expressionMatrix)[specificResult$identic.row.ids]
499:       gene.html=row.names(expressionMatrix)
502:       gene.html=row.names(expressionMatrix)[gene.html.ids]
507:     expressionMatrix=expressionMatrix[-(specificResult$identic.row.ids),]
508:     gene.html.ids=which(rownames(expressionMatrix) %in% gene.html)
516:     n_page=sapply(1:nrow(expressionMatrix), function(i) paste(i,"- ID ",row.names(expressionMatrix)[i],sep=""))
517:     n_link=sapply(1:nrow(expressionMatrix), function(i) paste(prefix.file,"_",row.names(expressionMatrix)[i],".html",sep=""))
535:       gene.html.ids=which(rownames(expressionMatrix)%in% gene.html)
538:     n_page=sapply(1:length(gene.html.ids), function(i) paste(i,"- ID ",row.names(expressionMatrix)[gene.html.ids[i]],sep=""))
539:     n_link=sapply(1:length(gene.html.ids), function(i) paste(prefix.file,"_",row.names(expressionMatrix)[gene.html.ids[i]],".html",sep=""))
558:   geneLink=cbind(rownames(expressionMatrix)[gene.html.ids],n_link)
565: getIdenticRow <- function(expressionMatrix){
567:   M_identic=t(sapply(1:nrow(expressionMatrix), function(i) expressionMatrix[i,]==expressionMatrix[i,1]))
569:   identic_ids=which(M_identic_sum==ncol(expressionMatrix))
665: fitPrior <- function(expressionMatrix,param.detection=NULL,lambda=1,beta=6, evaluation.lambda.beta=FALSE){
669:   identic_row_ids=getIdenticRow(expressionMatrix)
671:     expressionMatrix=expressionMatrix[-(identic_row_ids),]
672:     print(dim(expressionMatrix))
683:     fit_beta_0=lapply(1:nrow(expressionMatrix), function(i) Mclust( expressionMatrix[i,], G = 1:3,modelNames = "V" ))
686:       fit=lapply(1:nrow(expressionMatrix), function(i) Mclust( expressionMatrix[i,], G = 1:3,modelNames = "V" ))
691:         fit=lapply(1:nrow(expressionMatrix), function(i) Mclust( expressionMatrix[i,], G = 1:3, prior=priorControl(shrinkage=0,scale=getScaleMAD(beta,expressionMatrix[i,],G=1:3,1)),modelNames = "V" ))
703: ...(12 bytes skipped)...glikelihood=t(sapply(1:length(fit), function(i) getLoglikelihoodFromBIC(fit[[i]]$BIC,ncol(expressionMatrix))))
704: ...(21 bytes skipped)...=lapply(1:nrow(M_loglikelihood), function(i) getLambdaBIC(lambda,M_loglikelihood[i,],ncol(expressionMatrix)))
707:           fitlambda=lapply(1:nrow(expressionMatrix), function(i) Mclust(expressionMatrix[i,],G=L_G_lambdaBIC[[i]]$G,modelNames="V"))
710:           fitlambda=lapply(1:nrow(expressionMatrix), function(i) Mclust(expressionMatrix[i,],G=L_G_lambdaBIC[[i]]$G,prior=priorControl(shrinkage=0,scale=getScaleMAD(beta,expressionMatrix[i,],G=L_G_lambdaBIC[[i]]$G,1)),modelNames="V"))
715:     fit2=lapply(1:nrow(expressionMatrix), function(i) mclust_step2(rownames(expressionMatrix)[i],expressionMatrix[i,],G_initial=fit[[i]]$G,fitlambda[[i]]))
716:     names(fit2)=row.names(expressionMatrix)
747: fitNoPriorWithExclusion <- function(expressionMatrix,specificOutlierStep1=FALSE,param.detection=NULL,lambda=1,beta=0){
751:   identic_row_ids=getIdenticRow(expressionMatrix)
753:     expressionMatrix=expressionMatrix[-(identic_row_ids),]
764:       fit=lapply(1:nrow(expressionMatrix), function(i) Mclust( expressionMatrix[i,], G = 1:3,modelNames = "V" ))
769: ##         fit=lapply(1:nrow(expressionMatrix), function(i) Mclust( expressionMatrix[i,], G = 1:3, prior=priorControl(shrinkage=0,scale=getScaleMAD(beta,expressionMatrix[i,],G=1:3,1)),modelNames = "V" ))
772:   fit2=lapply(1:nrow(expressionMatrix), function(i)
773:     if(length(which(names(specificOutlierStep1)==rownames(expressionMatrix)[i]))>0){
774:       callMclustInStep2(rownames(expressionMatrix)[i],colnames(expressionMatrix),expressionMatrix...(27 bytes skipped)...it[[i]],beta_value=beta,specificOutlierStep1[[which(names(specificOutlierStep1)==rownames(expressionMatrix)[i])]])
777:       callMclustInStep2(rownames(expressionMatrix)[i],colnames(expressionMatrix),expressionMatrix[i,],G_initial=fit[[i]]$G,fit[[i]],beta_value=beta, NULL)
780:   names(fit2)=row.names(expressionMatrix)
786: getSpecificOutliersStep1 <- function(expressionMatrix,fit1=NULL,param.detection=NULL, multitest.correction.method="BY", prefix.file=NULL, print.hist.pv=F...(6 bytes skipped)...
808:     resultSpecific=getSpecific(expressionMatrix,fit=fit1,param_d,specificOutlierStep1=NULL,multitest.correction.method="BY",prefix.file=paste(prefi...(44 bytes skipped)...
809:     L_specific_Step1=lapply(1:nrow(expressionMatrix...(15 bytes skipped)...if(length(which(names(resultSpecific$L.specific.result$L.condition.specific.id)==rownames(expressionMatrix...(4 bytes skipped)...))>0) {as.vector(unlist(resultSpecific$L.specific.result$L.condition.specific.id[rownames(expressionMatrix)[i]]))}
812:     names(L_specific_Step1)=rownames(expressionMatrix)
818: getSpecificResult<- function(expressionMatrix,fit2=NULL,param.detection=NULL,specificOutlierStep1=NULL,multitest.correction.method="BY",prefix.fi...(29 bytes skipped)...
836:     resultSpecific=getSpecific(expressionMatrix,fit=fit2,param.detection=param_d,specificOutlierStep1=specificOutlierStep1,multitest.correction.met...(61 bytes skipped)...
842: getSpecific <- function(expressionMatrix,fit,param.detection,specificOutlierStep1=NULL,multitest.correction.method="BY",prefix.file=NULL,pri...(39 bytes skipped)...
854:   identic_row_ids=getIdenticRow(expressionMatrix)
856:     expressionMatrix=expressionMatrix[-(identic_row_ids),]
859:   L_diff_median=lapply(1:nrow(expressionMatrix), function(i) getDifferenceMedian(expressionMatrix[i,],fit[[i]]))
862:   L_null_min_lk_values_rsd=lapply(1:nrow(expressionMatrix), function(p) if(length(which(names(specificOutlierStep1)==rownames(expressionMatrix)[p]))>0){
863:     getNullLoglikelihoodRsdMd(expressionMatrix...(76 bytes skipped)...ective_threshold,length(specificOutlierStep1[[which(names(specificOutlierStep1)==rownames(expressionMatrix)[p])]]))
866:     getNullLoglikelihoodRsdMd(expressionMatrix[p,],fit[[p]]$NorMixParam,L_diff_median[[p]],min_loglike,min_rsd,percent_selective_threshold,0)
871:   names(L_null)=rownames(expressionMatrix)
873:   names(L_min_lk_values)=rownames(expressionMatrix)
875:   names(L_rsd)=rownames(expressionMatrix)  
877:   M_null_mean_pv=t(sapply(1:nrow(expressionMatrix), function(i) getPValueMean(expressionMatrix[i,],fit[[i]]$NorMixParam,L_null[[i]])))
879:   M_signe=t(sapply(1:nrow(expressionMatrix), function(i) expressionMatrix[i,]<M_null_mean[i]))
898:   ## row.names(M_pv)=row.names(expressionMatrix)
899:   ## colnames(M_pv)=colnames(expressionMatrix)
904:   M_pv=matrix(M_pv1,ncol=ncol(expressionMatrix),nrow=nrow(expressionMatrix),byrow = FALSE)
905:   row.names(M_pv)=row.names(expressionMatrix)
906:   colnames(M_pv)=colnames(expressionMatrix)
940:       print("The expressionMatrix argument that you entered has been coverted to a matrix using the exprs() function of the Biobase p...(8 bytes skipped)...
962: SpeCond<- function(expressionMatrix,param.detection=NULL,multitest.correction.method="BY",prefix.file="A",print.hist.pv=FALSE,fit1=NULL...(93 bytes skipped)...
964:    if(is(expressionMatrix,"ExpressionSet"))
966: ##       expressionMatrix=exprs(expressionMatrix)
967:       expressionMatrix=getMatrixFromExpressionSet(expressionMatrix,condition.factor,condition.method)
968:       print("The expressionMatrix argument that you entered has been coverted to a matrix using the getMatrixFromExpressionSet() function")
971:      if(!is(expressionMatrix,"matrix")){
972:        stop("The expressionMatrix argument used must be a matrix or an ExpressionSet object")
976:   if(ncol(expressionMatrix)<8){
987:     L_b_fit=fitPrior(expressionMatrix,lambda=param.detection[1,"lambda"],beta=param.detection[1,"beta"],evaluation.lambda.beta=FALSE)
991:     specificOutlierStep1=getSpecificOutliersStep1(expressionMatrix,fit=fit1,param.detection=param.detection,multitest.correction.method="BY",prefix.file=prefix.file,p...(19 bytes skipped)...
1000:     fit2=fitNoPriorWithExclusion(expressionMatrix,specificOutlierStep1=specificOutlierStep1,lambda=param.detection[2,"lambda"],beta=param.detection[2...(9 bytes skipped)...
1003:   specificResult=getSpecificResult(expressionMatrix,fit=fit2,specificOutlierStep1=specificOutlierStep1,param.detection=param.detection,multitest.correc...(92 bytes skipped)...
OpenStats:R/Auxi.R: [ ]
109: Matrix2List <- function(x, ...) {
2636:   XclassValue <- tapply(controls[, depVariable], cutsC, function(x) {
2640:   MclassValue <- lapply(
2646:   CclassValue <- lapply(XclassValue, length)
4487: FormatPvalueClassificationTag <- function(x, decimals = 4) {
71: is0 = function(obj = NULL, class2 = NULL) {
74:       is.null(class2) ||
75:       length(class2) < 1)
78:   for (cl2 in class2) {
467:     class(x) %in% c("matrix", "integer", "double", "numeric")) {
639:     return(Matrix2List(out, sep = sep))
652:         "Percentage change"   = Matrix2List(out / ran * 100, sep = sep)
2401:   if (class(x) %in% c("PhenList", "OpenStatsList")) {
2417:   if (class(data) %in% c("PhenList", "OpenStatsList")) {
3031:     if (any(class(x) %in% "glm")) {
3057:       "Confidence" =  Matrix2List(x = fixedEffInters),
3139:     "Formula" = printformula(if (class(obj) %in% "lme") {
3774:     class(aod) <- c("Anova", "data.frame")
4057:     class(aod) <- c("anova", "data.frame")
2641:     XclassValue,
2649:   tbl <- rbind(unlist(CclassValue), unlist(MclassValue))
msPurity:vignettes/msPurity-spectral-database-vignette.html: [ ]
554: <text x='1005' y='228' text-anchor='end' class='colType'>#</text>  <a xlink:href='#Default.metab_compound.compound_class'><text id='Default.metab_compound.compound_class' x='883' y='247'>compound_class</text><title>compound_class
715: <text x='333' y='420' text-anchor='end' class='colType'>t</text>  <a xlink:href='#Default.fileinfo.class'><text id='Default.fileinfo.class' x='275' y='439'>class</text><title>class
85: code[class] {
184: ...(6648 bytes skipped)...ight:-15px;margin-left:-15px}.no-gutters{margin-right:0;margin-left:0}.no-gutters>.col,.no-gutters>[class...(17168 bytes skipped)...flex;-ms-flex-wrap:wrap;flex-wrap:wrap;margin-right:-5px;margin-left:-5px}.form-row>.col,.form-row>[class*=col-]{padding-right:5px;padding-left:5px}.form-check{position:relative;display:block;padding-left:...(116552 bytes skipped)...
190: <p><body class='bg-light'></p>
192: <div class='container'>
196: <a href='https://www.dbschema.com' class='text-secondary small'>&nbsp;&nbsp;&nbsp;by DbSchema</a><br><br><br>
197: <div class='svg-container'>
201: ...(25 bytes skipped)... ( var i in el ){ var elem = document.getElementById(el[i]); if ( elem != null ) elem.setAttribute('class','highlight');  }  }
202: ...(26 bytes skipped)... ( var i in el ){ var elem = document.getElementById(el[i]); if ( elem != null ) elem.setAttribute('class','scene'); }  } 
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424: c_peak_X_s_peak_meta ref c_peaks ( cid )' class='relName' style='fill:#748EA7'>cid</text>
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432: c_peak_X_s_peak_meta ref s_peak_meta ( pid )' class='relName' style='fill:#748EA7'>pid</text>
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440: s_peak_meta ref fileinfo ( fileid )' class='relName' style='fill:#748EA7'>fileid</text>
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446: <path transform='translate(8,0)' marker-start='url(#foot0p)' marker-end='url(#arrow1)'  class='dotted '  d='M 608,208L 560,208' ></path>
448: s_peak_meta ref source ( souceid -&gt; id )' class='relName' style='fill:#748EA7'>souceid</text>
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454: <path transform='translate(8,0)' marker-start='url(#foot0p)' marker-end='url(#arrow1)'  class='dotted '  d='M 768,96L 848,96' ></path>
456: s_peak_meta ref metab_compound ( inchikey_id )' class='relName' style='fill:#748EA7'>inchikey_id</text>
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464: s_peaks ref s_peak_meta ( pid )' class='relName' style='fill:#748EA7'>pid</text>
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472: c_peak_X_c_peak_group ref c_peak_groups ( grpid )' class='relName' style='fill:#748EA7'>grpid</text>
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480: c_peak_X_c_peak_group ref c_peaks ( cid )' class='relName' style='fill:#748EA7'>cid</text>
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261:       <feColorMatrix result='matrixOut' in='offOut' type='matrix' 
tidySummarizedExperiment:R/utilities.R: [ ]
155: add_class <- function(var, name) {
172: drop_class <- function(var, name) {
71:         dplyr::summarise_all(class) %>%
72:         tidyr::gather(variable, class) %>%
73:         pull(class) %>%
144: #' Add class to abject
156:     if (!name %in% class(var)) class(var) <- prepend(class(var), name)
161: #' Remove class to abject
167: #' @param name A character name of the class
173:     class(var) <- class(var)[!class(var) %in% name]
628:       if(is(.x, "dgCMatrix") | is(.x, "DelayedArray")) {
signifinder:R/SignatureFunction.R: [ ]
1332:     sign_class <- unique(sign_df[,2:3])
11: #' Alternatively, an object of type \linkS4class{SummarizedExperiment},
24: #' @return If dataset is a \linkS4class{SummarizedExperiment} object, then
27: #' \linkS4class{SummarizedExperiment} object is created in which scores are
50:         EL <- sign_df[grep("Epithelial-like", sign_df$class), ]
51:         ML <- sign_df[grep("Mesenchymal-like", sign_df$class), ]
76:             Sign_E <- sign_df$SYMBOL[sign_df$class == "E"]
77:             Sign_M <- sign_df$SYMBOL[sign_df$class == "M"]
433:                 sign_df$NAME[sign_df$class == "MHC"]))], na.rm = TRUE)
436:                 sign_df$NAME[sign_df$class == "CP"]))], na.rm = TRUE)
439:                 sign_df$NAME[sign_df$class == "EC"]))], na.rm = TRUE)
442:                 sign_df$NAME[sign_df$class == "SC"]))], na.rm = TRUE)
634:         row.names(datasetm_n), sign_df$SYMBOL[sign_df$class == "Housekeeping"])
636:         row.names(datasetm_n), sign_df$SYMBOL[sign_df$class == "TInflam"])
729:         CIN_Carter$SYMBOL[CIN_Carter$class == "CIN25"],
891:     sign_list <- split(sign_df$SYMBOL, sign_df$class)
942:     sign_list <- split(sign_df$SYMBOL, sign_df$class)
989:     sign_up <- sign_df[grep("up", sign_df$class), ]
990:     sign_down <- sign_df[grep("down", sign_df$class), ]
1069:             sign_df[sign_df$class == "OS", ], datasetm_n,
1072:             sign_df[sign_df$class == "DFS", ], datasetm_n,
1088: #' Extracellular Matrix Signature
1113:     sign_up <- sign_df[grep("ECMup", sign_df$class), ]
1114:     sign_down <- sign_df[grep("ECMdown", sign_df$class), ]
1194:     sign_list <- split(sign_df$SYMBOL, sign_df$class)
1282:         colnames(datasetm_n), sign_df[sign_df$class == "high", ]$SYMBOL)
1284:         colnames(datasetm_n), sign_df[sign_df$class == "low", ]$SYMBOL)
1321:     sign_list <- split(sign_df$SYMBOL, sign_df$class)
1333:     sign_class <- sign_class[sign_class$class %in% row.names(gsva_matrix), ]
1334:     columnNA <- .managena(datasetm = gsva_matrix, genes = sign_class$class)
1336:         gsva_matrix[sign_class$class, ] * sign_class$coeff, na.rm = TRUE)
44:     datasetm <- .getMatrix(dataset)
140:     datasetm <- .getMatrix(dataset)
190:     datasetm <- .getMatrix(dataset)
231:     datasetm <- .getMatrix(dataset)
257:     datasetm <- .getMatrix(dataset)
290:     datasetm <- .getMatrix(dataset)
350:     datasetm <- .getMatrix(dataset)
402:     datasetm <- .getMatrix(dataset)
474:     datasetm <- .getMatrix(dataset)
500:     datasetm <- .getMatrix(dataset)
531:     datasetm <- .getMatrix(dataset)
572:     datasetm <- .getMatrix(dataset)
598:     datasetm <- .getMatrix(dataset)
630:     datasetm <- .getMatrix(dataset)
665:     datasetm <- .getMatrix(dataset)
695:     datasetm <- .getMatrix(dataset)
722:     datasetm <- .getMatrix(dataset)
743: #' Cell-cycle Signature classifier
760:     datasetm <- .getMatrix(dataset)
807:     datasetm <- .getMatrix(dataset)
843:     datasetm <- .getMatrix(dataset)
883:     datasetm <- .getMatrix(dataset)
934:     datasetm <- .getMatrix(dataset)
987:     datasetm <- .getMatrix(dataset)
1029:     datasetm <- .getMatrix(dataset)
1062:     datasetm <- .getMatrix(dataset)
1111:     datasetm <- .getMatrix(dataset)
1157:     datasetm <- .getMatrix(dataset)
1196:     datasetm <- .getMatrix(dataset)
1238:     datasetm <- .getMatrix(dataset)
1269:     datasetm <- .getMatrix(dataset)
1313:     datasetm <- .getMatrix(dataset)
gaggle:R/gaggle.R: [ ]
720: 				j.class <- .jcall(.jcall(value, "Ljava/lang/Class;", "getClass"), "Ljava/lang/String;", "getName")
126: getMatrix <- function ()
379: getJavaClassName <- function(jobj, quiet=FALSE) {
374: 	return (class(obj)=="jobjRef" && isS4(obj) && slot(obj, "jclass")=="org/systemsbiology/gaggle/core/datatypes/Tuple" && !is.jnull(obj))
378: # get the Java class name of an rJava object
380: 	if (class(jobj)=='jobjRef') {
384: 		return(.jcall(.jcall(jobj, 'Ljava/lang/Class;', 'getClass'), 'S', 'getName'))
434:   else if (class (x) == "graphNEL") {
438:   else if (class (x) == "environment") {
721: 				# cat("j.class = ", j.class, "\n")
722: 				if (j.class == "org.systemsbiology.gaggle.core.datatypes.Tuple") {
756: 	else if (class(value)=='list') {
759: 	else if (class(value)=='jobjRef') {
21:   # Before starting VM, set the system classpath to a blank string
23:   # if the classpath has spaces in it (which it often does on
26:   Sys.unsetenv("CLASSPATH")
405:       .jcall (goose, "V", "createAndBroadcastMatrix", rownames (x), colnames (x),
128:   rowCount <- .jcall (goose, "I", "getMatrixRowCount")
129:   columnCount <- .jcall (goose, "I", "getMatrixColumnCount")
130:   matrixRowNames <- .jcall (goose, "[S", "getMatrixRowNames")
131:   matrixColumnNames <- .jcall (goose, "[S", "getMatrixColumnNames")
137:   data <- .jcall (goose, "[D", "getAllMatrixData");
388: 			cat("Error in getJavaClassName: Not a java object?\n")
719: 				# Note that the current method will fail to detect subclasses of Tuple
TCGAbiolinks:R/analyze.R: [ ]
327:     tabSurv_Matrix <- plyr::adply(.data =1:length(rownames(dataNormal)),.margins = 1,.fun = function(i){
333:         tabSurv_Matrix <- data.frame("mRNA" = mRNAselected)
12: #' @return object of class hclust if method selected is 'hclust'.
16: #' (consensus class assignments). ConsensusClusterPlus also produces images.
56: #' @param object gene expression of class RangedSummarizedExperiment from TCGAprepare
389:             tabSurv_Matrix[1, "Cancer Deaths"] <- deads_complete
390:             tabSurv_Matrix[1, "Cancer Deaths with Top"] <- deads_top
391:             tabSurv_Matrix[1, "Cancer Deaths with Down"] <- deads_down
392:             tabSurv_Matrix[1, "Mean Normal"] <- mean(as.numeric(mRNAselected_values_normal))
397:             tabSurv_Matrix[1, "Mean Tumor Top"] <- mean(as.numeric(dataCancer_onlyTop_sample_mRNASelected))
398:             tabSurv_Matrix[1, "Mean Tumor Down"] <- mean(as.numeric(dataCancer_onlyDown_sample_mRNASelected))
420:             tabSurv_Matrix[1, "pvalue"] <- tabSurv_pvalue
444:         tabSurv_Matrix
447:     tabSurv_Matrix[tabSurv_Matrix == "-Inf"] <- 0
449:     tabSurvKM <- tabSurv_Matrix
723: #'     It is possible to do a two-class analysis.
746: #' @param Cond1type a string containing the class label of the samples in mat1
748: #' @param Cond2type a string containing the class label of the samples in mat2
1259: #' @param typeCond1 a string containing the class label of the samples
1261: #' @param typeCond2 a string containing the class label of the samples
1617:     fit <- limma::lmFit(AffySet, design) ## fit is an object of class MArrayLM.
15: #' consensusMatrix (numerical matrix), consensusTree (hclust), consensusClass
1331: #'identify classes of genes or proteins that are #'over-represented using
spatzie:R/find_ep_coenrichment.R: [ ]
247:     jaspar_matrix_class <- "PFM"
255:                                         matrixClass = jaspar_matrix_class)
158:     stop("'int_raw_data' data type unsupported: ", class(int_raw_data))
221:     promoter_left <- S4Vectors::elementMetadata(anchor1)[, "node.class"] == "promoter"
222:     promoter_right <- S4Vectors::elementMetadata(anchor2)[, "node.class"] == "promoter"
249:     jaspar_matrix_class <- "PWMProb"
251:     jaspar_matrix_class <- "PWM"
118: #' @importFrom TFBSTools readJASPARMatrix
254:   motifs <- TFBSTools::readJASPARMatrix(motifs_file,
genefu:R/subtype.cluster.R: [ ]
200: 	class.tr <- mclust::map(emclust.tr$z, warn=FALSE)
149: 	uclass <- sort(unique(rr3$classification))
156: 	nclass <-  uclass[order(mm, decreasing=TRUE)[1]]
194: 	myclass <- mclust::unmap(rr3$classification)
233: 		mclust::mclust2Dplot(data=dd[ , c("ESR1", "ERBB2"), drop=FALSE], what="classification", classification=class.tr, parameters=mclust.tr$parameters, colors=c("darkred", "darkgreen", "darkblue"), xlim=myxlim, yli...(9 bytes skipped)...
16: #' @param module.ESR1	Matrix containing the ESR1-related gene(s) in
23: #' @param data	Matrix of gene expressions with samples in rows and probes
25: #' @param annot	Matrix of annotations with at least one column named
30: #' @param mapping	**DEPRECATED** Matrix with columns "EntrezGene.ID" and
66: #' - module.scores: Matrix containing ESR1, ERBB2 and AURKA module scores.
201: 	names(class.tr) <- dimnames(dd)[[1]]
207: 	sbt[names(class.tr)] <- sbtn[class.tr]
129: 		## necessary if we want to validate the classifier using a different dataset
148: 	#redefine classification to be coherent with subtypes
150: 	uclass <- uclass[!is.na(uclass)]
151: 	if(length(uclass) != 3) { stop("less than 3 subtypes are identified!") }
153: 	for(i in 1:length(uclass)) {
154: 		mm <- c(mm, median(dd[rr3$classification == uclass[i],"ERBB2"], na.rm=TRUE) )
158: 	for(i in 1:length(uclass[-nclass])) {
159: 		mm <- c(mm, median(dd[rr3$classification == uclass[-nclass][i],"ESR1"], na.rm=TRUE))
161: 	nclass <- c(uclass[-nclass][order(mm, decreasing=TRUE)[2]], nclass, uclass[-nclass][order(mm, decreasing=TRUE)[1]])
162: 	#nclass contains the new order
163: 	rr3$z <- rr3$z[ ,nclass, drop=FALSE]
164: 	ncl <- rr3$classification
165: 	for(i in 1:length(uclass)) {
166: 		ncl[rr3$classification == nclass[i]] <- i
168: 	rr3$classification <- ncl
169: 	rr3$parameters$pro <- rr3$parameters$pro[nclass]
170: 	rr3$parameters$mean <- rr3$parameters$mean[ , nclass, drop=FALSE]
171: 	rr3$parameters$variance$sigma <- rr3$parameters$variance$sigma[ , , nclass, drop=FALSE]
195: 	dimnames(myclass)[[1]] <- dimnames(dd)[[1]]
196: 	mclust.tr <- mclust::mstep(modelName=model.name, data=dd[ , c("ESR1", "ERBB2"), drop=FALSE], z=myclass)
197: 	dimnames(mclust.tr$z) <- dimnames(myclass)
199: 	dimnames(emclust.tr$z) <- dimnames(myclass)
234: ...(6 bytes skipped)...nd(x="topleft", col=c("darkred", "darkgreen", "darkblue"), legend=sbtn, pch=mclust::mclust.options("classPlotSymbols")[1:length(uclass)], bty="n")