Results for class Matrix

Found 65769 results in 3254 files, showing top 50 files (show more).

ExploreModelMatrix:R/ExploreModelMatrix.R: [ ]
177: class = "fa fa-question-circle",
658: class = "fa fa-question-circle",
55: ExploreModelMatrix <- function(sampleData = NULL, designFormula = NULL) {
199: class = "fa fa-question-circle",
224: class = "fa fa-question-circle",
252: class = "fa fa-question-circle",
283: class = "fa fa-question-circle",
306: class = "fa fa-question-circle",
333: class = "fa fa-question-circle",
355: class = "fa fa-question-circle",
23: #' app <- ExploreModelMatrix(
90: title = paste0("Design matrix visualization (ExploreModelMatrix v",
91: utils::packageVersion("ExploreModelMatrix"), ")"),
612: designMatrix = NULL)
1109: package = "ExploreModelMatrix"),
1131: package = "ExploreModelMatrix"),

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

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

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

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

geNetClassifier:R/functions.public.R: [ ]
389: class <- names(x)[largest]
71: classes <- factor(apply(prob, 2, function(x) {assignment.conditions(x, minProb, minDiff)}))
97: classes<-unique(c(rownames(mxcf),colnames(mxcf)))
133: byClass <- matrix(nrow=nclasses, ncol=4)
176: externalValidation.probMatrix<- function(queryResult, realLabels, numDecimals=2)
209: probMatrix <- matrix(0,nrow=length(levels(realLabels)), ncol=length(predClasses))
215: classAssignments <- globalQueryResult$class[names(realLabels)[which(realLabels==label)]] #Prob for the class samples, even if the prediction was wrong
249: classes <- rownames(globalQueryResult$probabilities)
289: highestProbClass...(9 bytes skipped)...mes(globalQueryResult$probabilities)[apply(globalQueryResult$probabilities[,which(globalQueryResult$class == "NotAssigned"), drop=FALSE], 2, function(x) which(order(x, decreasing=TRUE)==1))] #Clase con la ...(18 bytes skipped)...
293: nextClass...(17 bytes skipped)...mes(globalQueryResult$probabilities)[apply(globalQueryResult$probabilities[,which(globalQueryResult$class == "NotAssigned"), drop=FALSE], 2, function(x) which(order(x, decreasing=TRUE)==2))]
608: exprMatrix <- eset
695: classes <- levels(sampleLabels)
718: classLabels <- stats::setNames(paste("C", sapply(classes,function(x) which(classes==x)), sep=""), classes)
795: if(!is.null(colnames(genes))) { geneClass <- unique(colnames(genes)[which(genes == genesVector[i],arr.ind=TRUE)[,2]])
815: classMean <- mean(matriz[genesVector[i], (prevLim+1):j])
931: classificationGenesRanking <- classificationGenes
1084: geneClass<-NULL
1156: tempDpMatrix <- discrPwDF
1307: classificationGenesInfo <- genesDetails(classificationGenes)[nwClasses]
1382: classGeneLabels <- as.vector(genesInfoList[[cl]][,"GeneName"])[availableNames]
1395: classGenes <- getNodes(genesNetwork[[cl]])
1412: classificationGenesNetwork <- NULL
1413: classificationGenesID <- NULL
1489: if(length(genesNetwork[[nw]]@nodes)>0) { classGraph <- igraph::graph.data.frame(as.data.frame(genesNetwork[[nw]]@edges[,ntwColnames,drop=FALSE]), ...(68 bytes skipped)...
31: queryGeNetClassifier <- function(classifier, eset, minProbAssignCoeff=1, minDiffAssignCoeff=0.8, verbose=TRUE)
42: numClasses <- length(classifier$levels)
99: nclasses <- length(classes)
105: missingClasses <- classes[which(!classes %in% colnames(mxcf))]
207: predClasses <- c(levels(realLabels), rownames(globalQueryResult$probabilities)[which(!rownames(globalQueryRes...(46 bytes skipped)...
250: numClasses <- length(classes)
337: if(is.null(totalNumberOfClasses)) {numClasses <- length(levels(realLabels))
509: numClasses <- length(gClasses(genesRanking))
697: numClasses <- length(classes)
991: longClassNames <- any(nchar(classNames)>6)
1000: numClasses <- ifelse(is.matrix(classificationGenes), length(classNames), length(classifier$levels))
1269: nwClasses <- names(genesNetwork)
1473: numClasses<-length(genesNetwork)
43: ...(52 bytes skipped)...obAssignCoeff' should be a coefficient to modify the probability required to assign the sample to a class.")
44: ...(72 bytes skipped)...d be a coefficient to modify the required difference between probabilites to assign the sample to a class.")
67: ...(86 bytes skipped)...etSelection)) esetSelection <- t(cbind(NULL,esetSelection)) #To avoid error when there is only 1gen/class
72: ret <- list(call=match.call(), class= classes, probabilities=prob)
77: ...(6 bytes skipped)...ulates stats from the confussion matrix. i.e. sensitivity and specificity of the predictor for each class, global accuracy and call rate (rate of assigned samples)
79: # 100% Sensitivity = Recognizes all positives for the class
80: # 100% Specificity = Recognizes all negatives for the class
82: #Renombrado de class.accuracy
93: warning("The confussion matrix should have the real class in rows and the assigned class in cols. The matrix provided didn't seem to be in the right order so it was transposed:")
174: # Returns the matrix with the average probabilities of assigning a sample to each class (only of assigned samples)
186: if (length(realLabels) == length( names(queryResult$class)))
188: names(realLabels) <- names(queryResult$class)
201: if(sum(!names(globalQueryResult$class) %in% names(realLabels)) >0) stop("There are samples for which the real label was not provided.")
203: if((length(globalQueryResult$class)!=dim(globalQueryResult$probabilities)[2]) \|\| (sum(!names(globalQueryResult$class) %in% colnames(globalQueryResult$probabilities))>0 )) {stop("The samples in $class and in $probabilities do not match.")}
228: # Gives basic stats of the probabilities with wich the samples were assigned to the class
246: if(length(globalQueryResult$class)!=dim(globalQueryResult$probabilities)[2]) {}#El numero de samples no encaja
248: numSamples <- length(globalQueryResult$class)
262: if (globalQueryResult$class[i] == classes[c])
270: else if (c==1 && (globalQueryResult$class[i] == "NotAssigned")) notAssigned <- notAssigned+1
284: # Info about NotAssigned samples (most likely class & probs)
288: highestProb <- apply(globalQueryResult$probabilities[,which(globalQueryResult$class == "NotAssigned"), drop=FALSE], 2, function(x) round(max(x), numDecimals)) #--> mayor probabilid...(13 bytes skipped)...
291: nextProbIndex <- cbind(apply(globalQueryResult$probabilities[,which(globalQueryResult$class...(10 bytes skipped)...signed"), drop=FALSE], 2, function(x) which(order(x, decreasing=TRUE)==2)), which(globalQueryResult$class == "NotAssigned"))
302: ...(19 bytes skipped)...("The query contains ", samplesQueried=numSamples, " samples. ",sum(stats[,1])," were assigned to a class resulting on a call rate of ", callRate,"%. \n", sep=""))
331: if (length(realLabels) == length(names(queryResult$class)))
333: names(realLabels) <- names(queryResult$class)
342: ...(72 bytes skipped)...d be a coefficient to modify the required difference between probabilites to assign the sample to a class.")
363: plot(c(minX,1), c(0,1), type="n", xlab="Probability of the most likely class", ylab="Difference with next class", frame=FALSE, main="Thresholds to assign query samples")
380: ...(0 bytes skipped)... graphics::legend("bottomright", "(x,y)", legend=c("Correct", "Incorrect"), title = "Most likely class", text.width = strwidth("1,000,000"), xjust = 1, yjust = 1, lty = 0, pch=16, col=c(correctColor, i...(24 bytes skipped)...
393: class <- "NA"
395: return(c(biggestProb=x[largest], nextProb=nextProb, assignedClass=class))
466: if(class(sampleLabels) != "factor") {
480: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(27 bytes skipped)...
599: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(27 bytes skipped)...
638: if(class(sampleLabels) != "factor") { warning("The argument 'sampleLabels' had to be converted into a factor...(4 bytes skipped)...
671: # Default class colors for boxplot
719: warning(paste("Some class names are longer than 10 characters. The following labels will be used in plots:\n",paste(classLabels, names(classLabels), sep=": ", collapse="\n"), sep=""))
746: geneTitles <- matrix(ncol=3, dimnames=list(genesVector,c("class","label","labelShort")),nrow=length(genesVector))
749: # Class
752: # Gene class, from genes table
753: geneTitles[g,"class"] <- unique(colnames(genes)[which(genes == g,arr.ind=TRUE)[,2]])
754: if(nchar(geneTitles[g,"class"])>10) geneTitles[g,"class"] <- paste(geneTitles[g,"class"], " (C",which(classes==geneTitles[g,"class"]), ")", sep="")
757: geneTitles[g,"class"]<-""
799: title(paste(geneTitles[genesVector[i],"class"], geneTitles[genesVector[i],"label"], sep="\n" ))
811: if (any(nchar(classes)>10) ) {graphics::text(prevLim+((j-prevLim)/2), ylim[2]-(ylim[2]0.04), labels=classLabels[sampleLabels[j]]) # Class* title
812: ...(52 bytes skipped)...evLim+((j-prevLim)/2)+0.5, y=ylim[2]-(ylim[2]0.04), labels=paste(sampleLabels[j],sep=""), pos=3) # Class* title
845: title(paste(geneTitles[gen,"class"], geneTitles[gen,"label"], sep="\n" ))
926: if(any(class(classificationGenes) == "GenesRanking"))
988: warning(paste("The number of classes provided don't match the classifier's. The default class names will be used instead.",sep=""), immediate. = TRUE) }
1002: if(!is.matrix(classificationGenes)) { #if(verbose) warning("The 'classification genes' are not sorted by colums and classes, the gene class will not be shown .")
1010: classificationGenes <- classificationGenes[,apply(classificationGenes, 2, function(x) !all(is.na(x)))] # Is there any class without genes?
1259: warning(paste("Plotting up to ", max(numGenes(genesRanking)), " genes of each class.", sep=""))
1272: if(!class(genesNetwork) == "GenesNetwork") stop("genesNetwork should be either a list or a GenesNetwork.")
1273: if((sum(c("class1", "class2") %in% colnames(genesNetwork@edges)) == 2 ) && nrow(genesNetwork@edges)>0)
1275: nwClasses <- unique(as.vector(genesNetwork@edges[,c("class1", "class2")]))
1291: if(any(class(classificationGenes) == "GenesRanking") && all(numGenes(classificationGenes) == 0)) classificationGenes <- NULL
1298: if(class(genesRanking) != "GenesRanking") stop("genesRanking should be an object of type GenesRanking.")
1305: if(class(classificationGenes)[1] != "GenesRanking") stop("classificationGenes should be an object of type GenesRanking (the classificationGenes object returned by the classifier).")
1345: if(any(! names(genesNetwork) %in% names(genesInfo))) { stop("The class names in genesInfo and genesNetwork do not match.")
1481: # For each class...
4: # clasificador (in file: classifier.main.r)
7: # externalValidation.probMatrix
24: # classifier:
30: # WARNING!: The arrays should have been normalized with the samples used for the classifier training.
36: if(is(classifier, "GeNetClassifierReturn")){
37: if("classifier" %in% names(classifier)) { classifier <- classifier@classifier$SVMclassifier
38: }else stop("'classifier' doesn't contain a trained classifier.")
40: if(!is(classifier, "svm")) classifier <- classifier$SVMclassifier
41: if(!is(classifier, "svm")) stop("The first argument should be the classifier returned by geNetClassifier.")
45: if(minProbAssignCoeff<0 \|\| ((numClasses != 2) &&(minProbAssignCoeff>(numClasses/2)))) stop("'minProbAssignCoeff' should be between 0 and half of the number of classes.")
46: if(minDiffAssignCoeff<0 \|\| minDiffAssignCoeff>numClasses) stop("'minDiffAssignCoeff' should be between 0 and the number of classes.")
47: genes <- colnames(classifier$SV)
59: rand <- 1/length(classifier$levels)
60: if(length(classifier$levels)>2) { minProb <- 2rand minProbAssignCoeff
63: ...(147 bytes skipped)..., "(default)",""),".\n Minimum difference between the probabilities of first and second most likely classes = ", round(minDiff,2),ifelse(minDiffAssignCoeff==1, "(default)",""), sep="")) ; utils::flush.co...(8 bytes skipped)...
68: prob <- t(attributes(stats::predict(classifier, esetSelection, probability=TRUE ))$probabilities)
69: if(is.null(names(prob))) colnames(prob)<-rownames(esetTdf) # if 2 classes... not labeled. Needed for mxcf...
83: externalValidation.stats <- function(confussionMatrix, numDecimals=2) #Confussion matrix
85: mxcf <- confussionMatrix
98: if(any(classes=="NotAssigned")) classes <- classes[-which(classes=="NotAssigned")]
103: if (any(!classes %in% colnames(mxcf)))
111: if (any(!classes %in% rownames(mxcf)))
113: missingClasses <- classes[which(!classes %in% rownames(mxcf))]
120: #Just in case they are not in order (Diagonal=hits). Will use only the real classes.
121: mxcf<- mxcf[,c(classes,"NotAssigned")] ...(22 bytes skipped)...
122: mxcf<- mxcf[c(classes),]
134: rownames(byClass)<- classes
135: colnames(byClass)<-c("Sensitivity","Specificity", "MCC", "CallRate")
159: byClass[i,1] <- round(100*(truePositives[i]/(truePositives[i]+falseNegatives[i])) ,numDecimals)
161: byClass[i,2] <- round(100*(trueNegatives[i]/(trueNegatives[i]+falsePositives[i])) ,numDecimals)
163: byClass[i,3] <- round(100( ((truePositives[i]trueNegatives[i])-(falsePositives[i]*falseNegatives[i])) / ...(175 bytes skipped)...
165: if(i <= dim(mxcf)[1]) byClass[i,4] <- round(100*( (sum(mxcf[i,])- mxcf[i,dim(mxcf)[2]])/sum(mxcf[i,])) ,numDecimals)
171: return( list(byClass=byClass, global=global, confMatrix=mxcf) )
210: rownames(probMatrix)<- levels(realLabels)
211: colnames(probMatrix)<- predClasses
216: assignedSamples <- names(classAssignments)[ which( classAssignments!= "NotAssigned")]
219: if(length(assignedSamples)>1) probMatrix...(8 bytes skipped)... <- apply(globalQueryResult$probabilities[,assignedSamples], 1, function(x) {mean(x)})[colnames(probMatrix)]
220: else probMatrix[label,] <- globalQueryResult$probabilities[,assignedSamples][colnames(probMatrix)]
224: ret<- round(probMatrix,numDecimals)
253: rownames(stats)<-c(classes)
295: notAssignedSamples <- cbind(highestProbClass=highestProbClass, as.data.frame(highestProb), nextProb=nextProb, nextClass=nextClass)
339: if(!is.numeric(totalNumberOfClasses) \|\| (totalNumberOfClasses<length(levels(realLabels)))) stop ("totalNumberOfClasses should be the number of classes for which the classifier was originaly trained.")
343: if(minProbAssignCoeff<0 \|\| ((numClasses != 2) &&(minProbAssignCoeff>(numClasses/2)))) stop("'minProbAssignCoeff' should be between 0 and half of the number of classes.")
344: if(minDiffAssignCoeff<0 \|\| minDiffAssignCoeff>numClasses) stop("'minDiffAssignCoeff' should be between 0 and the number of classes.")
397: rownames(prob) <- c("biggestProb", "nextProb", "assignedClass")
400: correct <- which(prob["assignedClass",] == prob["realLabels",])
401: incorrect <- which(prob["assignedClass",] != prob["realLabels",])
467: #warning("The argument 'classification sampleLabels' had to be converted into a factor.", immediate. = TRUE)
533: numClasses <- ncol(postProb) # If there are only 2 classes, postProb only has 1 column
575: ...(55 bytes skipped)...leName=NULL, geneLabels=NULL, type="lines", sampleLabels=NULL, sampleColors=NULL, labelsOrder=NULL, classColors=NULL, sameScale=TRUE, showSampleNames=FALSE, showMean= FALSE, identify=TRUE, verbose=TRUE)
610: if(is(exprMatrix, "ExpressionSet")) exprMatrix <- exprs(exprMatrix) else if (!is.matrix(exprMatrix)) stop("The first argument should be an expression matrix or an ExpressionSet.")
615: genes <- rownames(exprMatrix)
618: if(is(genes, "GeNetClassifierReturn") && "classificationGenes" %in% names(genes)) {
619: genes <- genes@classificationGenes
620: warning("Plotting expression profiles of the classification genes. To plot other genes, set i.e. genes=...@genesRanking")
629: if(sum(!genes[which(genes!="NA")] %in% rownames(exprMatrix))!=0) stop ("The expression matrix doesn't contain all the genes.")
632: if(!is.null(geneLabels)) geneLabels<-extractGeneLabels(geneLabels, rownames(exprMatrix[genesVector,]))
635: numSamples <- dim(exprMatrix)[2]
643: if(sum(!names(sampleLabels) %in% colnames(exprMatrix))>0 ) stop("The names of the labels do not match the samples.")
646: names(sampleLabels)<-colnames(exprMatrix)
659: if(!is.null(sampleColors) && !is.null(classColors)) stop("Provide either 'sampleColors' or 'classColors'")
662: if(is.null(classColors))
672: if(is.null(classColors))
674: if(!is.null(sampleLabels)) classColors <- rev(hcl(h=seq(0,360, length.out=length(levels(sampleLabels))+1))[1:length(levels(sampleLab...(7 bytes skipped)...
675: if(is.null(sampleLabels)) classColors <- "white"
680: if(is.null(sampleLabels)) stop("Cannot use 'classColors' if 'sampleLabels' is not provided.")
681: if(length(levels(sampleLabels)) != length(classColors))stop("Length of 'classColors' should match the number of classes in the samples.")
683: if(any(type%in%"lines")) sampleColors <- classColors[sampleLabels]
696: if(!is.null(labelsOrder)) classes <- labelsOrder
702: indexes <- c(indexes, which(sampleLabels==classes[i]))
704: matriz <- exprMatrix[genesVector, indexes, drop=FALSE]
709: classes <- colnames(genes)
710: numClasses <- length(classes)
711: matriz <- exprMatrix[genesVector,, drop=FALSE]
716: if(any(nchar(classes)>10))
796: } else { geneClass<-"" } #classes[which(genes == genesVector[i], arr.ind=TRUE)[2]]
810: ...(21 bytes skipped)... if(!is.na((sampleLabels[j] != sampleLabels[j+1]) ) ) abline(v=j+0.5, col="black") # Separate classes
816: graphics::lines(c(prevLim+1, j), c(classMean, classMean) , col="grey")
834: names(esetXclases) <- classLabels[names(esetXclases)] #paste("C", sapply(names(esetXclases),function(x) which(classes==x)), sep="")
844: ...(12 bytes skipped)... boxplot(Expression~sampleLabel, esetExprSamplesMelted, ylim=ylim, ylab="Expression values", col=classColors, las=2, outpch=16, outcex=0.5)
879: # discriminant.power.plot(classifier, classificationGenes, classNames=c("ALL","AML","CLL","CML","NoLeu"), fileName="test.pdf",correctedAlpha=TRUE)
880: # discriminant.power.plot(classifier, classificationGenes, fileName="test.pdf")
881: # Classification genes: Genes por columnas (nombrecolumna= clase)
882: # discriminant.power.plot(classifier, colnames(classif$SV), fileName="test.pdf")
888: # classifier: puede ser un svm o el objeto devuelto por la funcion principal
889: # classificationGenes: puede ser un c(), una matriz o un GenesRanking
891: plotDiscriminantPower <- function(classifier, classificationGenes=NULL , geneLabels=NULL, classNames=NULL, plotDP = TRUE, fileName= NULL, returnTable=FALSE, verbose=TRUE)
904: # Classifier
905: if(is(classifier, "GeNetClassifierReturn")){
906: if("classificationGenes" %in% names(classifier))
908: if(is.null(classificationGenes))
910: classificationGenes <- classifier@classificationGenes
912: if(is.null(geneLabels) && is.character(classificationGenes))
914: if(length(classifier@classificationGenes@geneLabels) > 0 && any(!is.na(classifier@classificationGenes@geneLabels[classificationGenes])))
915: geneLabels <- classifier@classificationGenes@geneLabels[classificationGenes]
919: if("classifier" %in% names(classifier)) {classifier <- classifier@classifier$SVMclassifier
920: }else stop("'classifier' doesn't contain a trained classifier.")
922: if(is.list(classifier) && ("SVMclassifier" %in% names(classifier))) classifier <- classifier$SVMclassifier
923: if(!is(classifier,"svm")) stop("The first argument should be a svm classifier or the object returned by geNetClassifier.")
925: # ClassificationGenes (GenesRanking)
928: if(sum(numGenes(classificationGenes)))
930: if(is.null(geneLabels) && (length(classificationGenes@geneLabels) > 0 && any(!is.na(classificationGenes@geneLabels)))) geneLabels <- classificationGenes@geneLabels
932: classificationGenes <- getRanking(classificationGenes, showGeneLabels=FALSE, showGeneID=TRUE)$geneID
934: classificationGenes <- NULL
935: classificationGenesRanking<-NULL
938: classificationGenesRanking<-NULL
941: # If classificationGenes is not provided/valid, use the classifier's SV
942: missingGenes <- !as.vector(classificationGenes[!is.na(classificationGenes)]) %in% colnames(classifier$SV)
945: missingGenes <- as.vector(classificationGenes[!is.na(classificationGenes)])[which(missingGenes)]
951: missingGenes <- missingGenes[which(!missingGenes %in% colnames(classifier$SV))]
953: classificationGenes[which(classificationGenes %in% missingGenes)] <- NA
955: if(all(is.na(classificationGenes))) stop("The given 'classificationGenes' are not used by the classifier. Their Discriminant Power cannot be calculated.")
956: if(length(missingGenes)>0) warning(paste("The following classificationGenes are not used by the classifier. Their Discriminant Power cannot be calculated: ", missingGenes, sep=""))
958: if(is.null(classificationGenes)) classificationGenes <- colnames(classifier$SV)
981: ...(35 bytes skipped)...ull(names(geneLabels))) stop("names(geneLabels) can't be empty. It should contain the names used in classification genes.")
982: #if(!is.null(geneLabels) && sum(!names(geneLabels) %in% classificationGenes[which(classificationGenes!="NA")] )>0) warning("Some geneLabels will not be used.")
983: if(!is.null(geneLabels) && sum(!classificationGenes[which(class...(9 bytes skipped)...Genes!="NA")] %in% names(geneLabels))>0) warning("geneLabels doesn't contain the symbol for all the classification genes.")
985: # classNames
986: if(is.null(classNames) \|\| (length(classifier$levels) != length(classNames))){
987: if (!is.null(classNames) && length(classifier$levels) != length(classNames)) {
989: classNames <-classifier$levels
994: for( i in 1:length(classNames)) #Add "C1:..."
996: if (nchar(classNames[i])>10 ) classNames[i] <- paste(substr(classNames[i] ,1,10), "...",sep="")
997: classNames[i] <- paste("C", i, ": ", classNames[i], sep="")
1005: if(length(classifier$levels) == 2) {
1006: if (dim(classificationGenes)[2] != 1) stop("The classes of the classifier and the classification genes provided don't match.")
1008: if(sum(!colnames(classificationGenes) %in% classifier$levels)>0) stop("The classes of the classifier provided and the classification genes don't match.")
1013: nGenes <- length(classificationGenes[which(!is.na(classificationGenes))]) #sum(numGenes(classifier$classificationGenes))
1014: if(nGenes>dim(classifier$SV)[2]){ warning(paste("The given number of genes is bigger than the classifier's.",sep=""), immediate. = TRUE)}
1021: if(is.matrix(classificationGenes)) # If it contains the genes by classes (columns)
1023: for(cl in 1:dim(classificationGenes)[2])
1025: discrPwList <- c(discrPwList, discrPwList=list(sapply(as.character(classificationGenes[which(classificationGenes[,cl]!="NA"),cl]), function(x) SV.dif(classifier, x, correctedAlpha=correctedAlpha))))
1026: names(discrPwList)[cl] <- colnames(classificationGenes)[cl]
1030: classificationGenes <- classificationGenes[which(!is.na(classificationGenes))]
1031: discrPwList <- list(sapply(as.character(classificationGenes), function(x) SV.dif(classifier, x, correctedAlpha=correctedAlpha)))
1032: classificationGenes <- as.matrix(classificationGenes)
1042: numRows <- numGenesPlot/dim(classificationGenes)[2]
1043: while ((numRows < dim(classificationGenes)[1]) && (length(classificationGenes[which(!is.na(classificationGenes[1:numRows,, drop=FALSE]))]) < numGenesPlot))
1048: if(length(classificationGenes[which(!is.na(classificationGenes[1:numRows,, drop=FALSE]))]) <= numGenesPlot)
1050: classificationGenes <- classificationGenes[1:numRows,, drop=FALSE]
1052: classificationGenes <- classificationGenes[1:(numRows-1),, drop=FALSE]
1068: mycols <- colorRampPalette(c("blue","white"))(max(classifier$nSV+2))
1070: for(c in 1:dim(classificationGenes)[2]) #numClasses
1072: for(g in 1:dim(classificationGenes)[1])
1075: gene <- classificationGenes[g,c]
1087: geneClass <- names(gene)
1088: if(nchar(geneClass)>70) geneClass<- substr(geneClass,1,70)
1101: tit<- paste(geneClass,"\n", geneName, "\n", sep="")
1104: ...(31 bytes skipped)...(tit, abs(round(discrPwList[[c]][,gene]$discriminantPower,2)), " (", discrPwList[[c]][,gene]$discrPwClass, ")", sep="")
1125: ...(0 bytes skipped)... barplot(pos,add=TRUE, col=mycols, width=0.9, space=0.1, names.arg=rep("",length(classNames)))
1126: ...(0 bytes skipped)... barplot(neg,add=TRUE, col=mycols, width=0.9, space=0.1, names.arg=rep("",length(classNames)))
1128: if(!correctedAlpha) graphics::text(seq(1, length(classNames), by=1)-0.5, par("usr")[3] - 0.2, labels = classNames, srt = 90, pos = 4, xpd = TRUE)
1129: if(correctedAlpha) graphics::text(seq(1, length(classNames), by=1)-0.5, par("usr")[3], labels = classNames, srt = 90, pos = 4, xpd = TRUE)
1148: discrPwDF<- rbind(discrPwDF, cbind(t(discrPwList[[cl]][c("discriminantPower","discrPwClass"),]), originalClass=rep(names(discrPwList)[cl],dim(discrPwList[[cl]])[2])))
1153: discrPwDF[,"discrPwClass"] <- as.character(discrPwDF[,"discrPwClass"])
1157: tempDpMatrix[,"discriminantPower"] <- abs (tempDpMatrix[,"discriminantPower"] )
1159: for(cl in classifier$levels)
1161: clGenes <- which(tempDpMatrix[,"discrPwClass"]==cl)
1162: discrPwDF <- rbind(discrPwDF, tempDpMatrix[clGenes[order(as.numeric(tempDpMatrix[clGenes,"discriminantPower"]),decreasing=TRUE)],])
1166: if(is.null(classificationGenesRanking))
1171: gDetails<-genesDetails(classificationGenesRanking)
1177: genesDetailsDF <- cbind(discrPwClass=rep(NA,dim(discrPwDF)[1]), discriminantPower=rep(NA,dim(discrPwDF)[1]), genesDetailsDF[rownames(dis...(39 bytes skipped)...
1178: genesDetailsDF[,"discrPwClass"] <- as.character(discrPwDF[,"discrPwClass"])
1187: # plotType="dynamic" (each can be modified), plotType="static" (1 image divided into classes), plotType="pdf"
1189: # genesInfo: Data.frame containing info about the genes. Can be replaced by classificationGenes or genesRanking (recommended).
1190: # If classificationGenes + genesRanking:
1191: # classificationGenes: Tiene q ser un genesRanking
1193: plotNetwork <- function(genesNetwork, class...(96 bytes skipped)...tType="dynamic", fileName=NULL, plotAllNodesNetwork=TRUE, plotOnlyConnectedNodesNetwork=FALSE, plotClassifcationGenesNetwork=FALSE, labelSize=0.5, vertexSize=NULL, width=NULL, height=NULL, verbose=TRUE)
1254: if(is.null(classificationGenes) && ("classificationGenes" %in% names(genesNetwork))) classificationGenes <- genesNetwork@classificationGenes
1257: nGenes <- max( 100, numGenes(genesNetwork@classificationGenes))
1277: }else nwClasses <- "geneClass"
1289: # Check classificationGenes and Genes ranking format and EXTRACT its genes INFO.
1290: if(is.matrix(classificationGenes) && nrow(classificationGenes)==0) classificationGenes <- NULL
1292: if(plotClassifcationGenesNetwork && is.null(classificationGenes)) warning("The classifcation genes network can only be plotted if the classification genes are provided.")
1293: if((!is.null(class...(30 bytes skipped)...nesRanking)) && !is.null(genesInfo)) stop("Please, provide either 'genesInfo' OR a genesRanking and classificationGenes.")
1294: if(!is.null(genesRanking) \|\| !is.null(classificationGenes))
1303: if(!is.null(classificationGenes))
1308: clGenes <- lapply(classificationGenesInfo, rownames)
1310: if(showWarning) warning("Not all the classificationGenes are available in the genesNetwork. They will be represented, but there may be missing...(29 bytes skipped)...
1314: genesInfo <- classificationGenesInfo
1317: missingColumnsInGlobal <- colnames(classificationGenesInfo[[1]])[which(!colnames(classificationGenesInfo[[1]]) %in% colnames(genesInfo[[1]]))]
1328: ...(30 bytes skipped)... if((is.factor(temp[,tempCol]))) levels(temp[,tempCol]) <- unique(c(levels(temp[,tempCol]), levels(classificationGenesInfo[[cl]][,tempCol])))
1331: temp[rownames(classificationGenesInfo[[cl]]), ] <- classificationGenesInfo[[cl]][,colnames(temp)]
1364: ...(143 bytes skipped)...lot, but there may be missing relationships.") # showWarning: The warning was already shown for the classification genes.
1383: names(classGeneLabels) <- rownames(genesInfoList[[cl]])[availableNames]
1384: geneLabels <- c(geneLabels, classGeneLabels)
1396: missingGenes <- classGenes[which(!classGenes %in% rownames(genesInfoList[[cl]]))]
1408: # - Add classification nodes Network
1411: # Extract CLASSIFICATIONgenesNetwork if available/needed & add to list
1414: if(!is.null(classificationGenes) && plotClassifcationGenesNetwork)
1416: classificationGenesID <- getRanking(classificationGenes, showGeneLabels=FALSE, showGeneID=TRUE)$geneID[, nwClasses, drop=FALSE]
1417: classificationGenesNetwork <- getSubNetwork(genesNetwork, classificationGenesID)
1418: names(classificationGenesNetwork) <- paste(names(classificationGenesNetwork), " - Classification Genes",sep="")
1420: clToAdd <- which(sapply(genesNetwork, function(x){length(getNodes(x))}) - sapply(classificationGenesNetwork, function(x){length(getNodes(x))}) != 0)
1423: warning("Only the classification genes network was provided. Only 'AllNodesNetwork' will be plotted.")
1431: genesNetwork <- c(genesNetwork[1:pos], classificationGenesNetwork[clToAdd[i]], genesNetwork[-(1:pos)])
1433: genesInfoList <- c(genesInfoList[1:pos], list(genesInfoList[[i]][classificationGenesID[,clToAdd[i]][!is.na(classificationGenesID[,i])],] ), genesInfoList[-(1:pos)])
1434: names(genesInfoList)[pos+1] <- names(classificationGenesNetwork[clToAdd[i]])
1440: # (Needs to be added after classific. in order to add it right after the "full" network)
1474: if( numClasses>25 ) stop("Too many classes to draw in a single plot. Use 'pdf' instead.")
1485: classGenes <- unique(c(genesNetwork[[nw]]@edges[,"gene1"],genesNetwork[[nw]]@edges[,"gene2"]))
1487: if((is.null(genesInfoList) \|\| nrow(genesInfoList[[nw]])==0 ) \|\| any(!classGenes %in% rownames(genesInfoList[[nw]])))
1490: } else classGraph <- igraph::graph.data.frame(as.data.frame(genesNetwork[[nw]]@edges[,ntwColnames,drop=FALSE]), ...(15 bytes skipped)...
1493: classGraph <- igraph::graph.data.frame(as.data.frame(genesNetwork[[nw]]@edges[,ntwColnames,drop=FALSE]), ...(101 bytes skipped)...
1495: if (igraph::vcount(classGraph) != 0)
1501: graphLayout <- igraph::layout.fruchterman.reingold(classGraph) # .grid is faster, but the result looks far worse.
1509: vertexLabels <- igraph::get.vertex.attribute(classGraph,"name")
1515: if(!is.null(igraph::get.vertex.attribute(classGraph,"exprsMeanDiff")))
1517: exprsDiff <- as.numeric(igraph::get.vertex.attribute(classGraph,"exprsMeanDiff"))
1539: if(!is.null(igraph::get.vertex.attribute(classGraph,"discriminantPower")))
1541: discPower<-round(as.numeric(igraph::get.vertex.attribute(classGraph,"discriminantPower")))
1552: # Shape: Classification gene
1555: if(!is.null(igraph::get.vertex.attribute(classGraph,"discriminantPower")))
1560: if(!is.null(classificationGenes)) # alguna comprobacion mas?
1562: vertexShape[which(igraph::get.vertex.attribute(classGraph,"name")%in% as.vector(getRanking(classificationGenes, showGeneID=TRUE)$geneID))] <- "square"
1567: relColors <- ifelse( igraph::get.edge.attribute(classGraph,"relation")==levels(factor(igraph::get.edge.attribute(classGraph,"relation")))[1], relColors[1],relColors[2])
1571: if(igraph::ecount(classGraph) > 0)
1573: igraph::tkplot(classGraph, layout=graphLayout, vertex.label=vertexLabels, vertex.label.family="sans", vertex.color=vert...(178 bytes skipped)...
1583: plot(classGraph, layout=graphLayout, vertex.label=vertexLabels, vertex.label.family="sans", vertex.label.cex=...(219 bytes skipped)...
1586: graphList <- c(graphList, graph=list(classGraph))
1607: text(0,0.6,"Node shape: Chosen/Not chosen for classification", pos=4, font=2)
5: # queryGeNetClassifier
21: # queryGeNetClassifier:
106: mxcf <- cbind(mxcf, matrix(ncol=length(missingClasses), nrow=dim(mxcf)[1], data=0))
107: colnames(mxcf)[which(colnames(mxcf)=="")]<-missingClasses
114: mxcf <- rbind(mxcf, matrix(nrow=length(missingClasses), ncol=dim(mxcf)[2], data=0))
115: rownames(mxcf)[which(rownames(mxcf)=="")]<-missingClasses
129: #nclasses <- dim(mxcf)[1]
131: numNA <- sum(mxcf[,nclasses+1])
137: falseNegatives <- array(0,dim=nclasses)
138: falsePositives <- array(0,dim=nclasses)
139: trueNegatives <- array(0,dim=nclasses)
140: truePositives <- array(0,dim=nclasses)
144: for(j in 1:nclasses) #dim(mxcf)[2]) Para incluir NA
156: for (i in 1:nclasses) #We need another loop in order to have the whole trueNegatives ready
175: # Can receive the result from executing queryGeNetClassifier, or a list of several: queryResult<-c(assignment1, assignment2)
229: # Can receive the result from executing queryGeNetClassifier, or a list of several: queryResult<-c(prediction1, prediction2)
252: stats <- cbind(c(rep(0,numClasses)), c(rep(1,numClasses)), c(rep(0,numClasses)),c(rep(NA,numClasses)),c(rep(NA,numClasses)))
257: for (c in 1:numClasses)
321: ...(12 bytes skipped)...nts <- function(queryResult, realLabels, minProbAssignCoeff=1, minDiffAssignCoeff=0.8, totalNumberOfClasses=NULL, pointSize=0.8, identify=FALSE)
340: numClasses <- totalNumberOfClasses
357: rand <- 1/numClasses
358: if(numClasses>2) { minProb <- 2rand minProbAssignCoeff
364: if(numClasses>2)
377: if(numClasses>2) graphics::text(0.3, 0.95, labels="Not Assigned", col="#606362", cex=0.8)
513: colnames(meanExprDiff) <- gClasses(genesRanking)
526: if (length(gClasses(genesRanking)) > 2){ ord <- genesRanking@ord[1:numGenesPlot,]
535: if((numClasses>3 && numClasses<10) && ("RColorBrewer" %in% rownames(utils::installed.packages())))
537: cols <- RColorBrewer::brewer.pal(numClasses,"Set1")
538: }else cols <- grDevices::rainbow(numClasses)
547: for(i in 1:numClasses)
555: legend("bottomleft", paste( gClasses(genesRanking)," (",lp," genes)",sep=""), lty=1, col=cols, pch=pchs)
700: for(i in 1:numClasses) #Por si no estan agrupados
731: if( numClasses == 0 \|\| !is.matrix(genes) ) {
992: if(longClassNames)
1102: if(longClassNames){ tit<- paste(tit,"DP: ", sep="")
1105: barplot(rep(0,numClasses),add=FALSE, ylim=lims, main=tit, col=mycols, width=0.9, space=0.1, cex.main=1)
1217: if(!is.logical(plotClassifcationGenesNetwork)) stop("plotClassifcationGenesNetwork should be either TRUE or FALSE.")
1248: if(!(returniGraphs \|\| plotAllNodesNetwork \|\| plotOnlyConnectedNodesNetwork \|\| plotClassifcationGenesNetwork)) stop("No network plots have been requested.")
1249: if(!(plotAllNodesNetwork \|\| plotOnlyConnectedNodesNetwork \|\| plotClassifcationGenesNetwork)) warning("No network plots have been requested, only the iGraph will be return...(13 bytes skipped)...
1252: if(is(genesNetwork, "GeNetClassifierReturn"))
1264: }else stop("'genesNetwork' is the return of geNetClassifier, but doesn't contain a genesNetwork.")
1276: # if (nwClasses[1] == nwClasses[2]) nwClasses <- nwClasses[1]
1280: names(genesNetwork) <- nwClasses[1]
1320: for( cl in nwClasses)
1460: genesNetwork <- genesNetwork[-which(names(genesNetwork) %in% nwClasses)]
1475: cols <- ceiling(sqrt(numClasses))
1476: rows <- ifelse(sqrt(numClasses)<round(sqrt(numClasses)), ceiling(sqrt(numClasses)),round(sqrt(numClasses)))

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

GRaNIE:R/core.R: [ ]
4799: d = tibble::add_row(d, r_positive = r_pos, class = classCur, peak_gene.p.raw.class = pclassCur, n = 0)
633: GC_class = cut(.data$`G\|C`, breaks = seq(0,1,0.1), include.lowest = TRUE, ordered_result = TRUE))
650: peak.GC.class = .data$GC_class)
4794: for (classCur in networkType_details){
4847: classAll = c(as.character(d2$class), d3$r_positive),
2373: GC_class.cur = GC_classes_foreground.df$GC_class[i]
4876: peak_gene.p.raw.class.bin = as.numeric(.data$peak_gene.p.raw.class)) %>%
635: GC_classes.df = GC_content.df %>%
1135: TFBS_bindingMatrix.df = tibble::as_tibble(res.l)
1638: AR_classification_wrapper<- function (GRN, significanceThreshold_Wilcoxon = 0.05,
2066: GC_classes_foreground.df = peaksForeground %>%
2074: GC_classes_background.df = peaksBackground %>%
2122: GC_classes_all.df = dplyr::full_join(GC_classes_foreground.df, GC_classes_background.df, suffix = c(".fg",".bg"), by = "GC_class") %>%
4887: .classFreq_label <- function(tbl_freq) {
4795: for (pclassCur in levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class)) {
3: #' Create and initialize a \code{\linkS4class{GRN}} object.
24: #' @return Empty \code{\linkS4class{GRN}} object
119: #' Add data to a \code{\linkS4class{GRN}} object.
121: #' This function adds both RNA and peak data to a \code{\linkS4class{GRN}} object, along with data normalization.
148: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function (e.g., slots \code{GRN@data$peaks} and \code{GRN@d...(9 bytes skipped)...
636: dplyr::group_by(.data$GC_class) %>%
639: tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
643: #ggplot2::ggplot(GC_content.df, ggplot2::aes(GC.class)) + geom_histogram(stat = "count") + ggplot2::theme_bw()
645: #ggplot2::ggplot(GC_classes.df , ggplot2::aes(GC.class, n_rel)) + geom_bar(stat = "identity") + ggplot2::theme_bw()
660: #' Filter RNA-seq and/or peak data from a \code{\linkS4class{GRN}} object
663: ...(130 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)...
680: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function.
941: #' Add TFBS to a \code{\linkS4class{GRN}} object
950: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function (\code{GRN@annotation$TFs} in pa...(9 bytes skipped)...
1070: #' Overlap peaks and TFBS for a \code{\linkS4class{GRN}} object
1075: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function (\code{GRN@data$TFs$TF_peak_overlap} in particular...(1 bytes skipped)...
1341: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function
1509: #' @return An updated \code{\linkS4class{GRN}} object, with added data from this function.
1622: #' Run the activator-repressor classification for the TFs for a \code{\linkS4class{GRN}} object
1632: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
1760: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_density"), "_", connectionTypeCur, suffixFile, ".pdf")
1770: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_medianClass"), "_", connectionTypeCur, suffixFile, ".pdf")
1783: fileCur = paste0(outputFolder, .getOutputFileName("plot_class_densityClass"), "_", connectionTypeCur, suffixFile, ".pdf")
1846: #' Add TF-peak connections to a \code{\linkS4class{GRN}} object
1864: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
2067: dplyr::group_by(.data$GC_class) %>%
2070: tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
2075: dplyr::group_by(.data$GC_class) %>%
2078: tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
2137: peaksBackgroundGCCur = peaksBackground %>% dplyr::filter(.data$GC_class == GC_classes_foreground.df$GC_class[i])
2378: dplyr::filter(.data$GC_class == GC_class.cur) %>%
2380: #futile.logger::flog.info(paste0(" GC.class ", GC.class.cur, ": Required: ", requiredNoPeaks, ", available: ", availableNoPeaks))
2385: #futile.logger::flog.info(paste0(" Mimicking distribution FAILED (GC class ", GC.class.cur, " could not be mimicked"))
2408: #' Add peak-gene connections to a \code{\linkS4class{GRN}} object
2425: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
2996: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
3603: #' Add TF-gene correlations to a \code{\linkS4class{GRN}} object. The information is currently stored in \code{GRN@connections$TF_genes.filtered}. Note...(36 bytes skipped)...
3611: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
3862: ...(4 bytes skipped)...enerate a summary for the number of connections for different filtering criteria for a \code{\linkS4class{GRN}} object.
3879: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function.
4178: #' @return An small example \code{\linkS4class{GRN}} object
4234: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object
4236: #' Get counts for the various data defined in a \code{\linkS4class{GRN}} object.
4237: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4249: ...(37 bytes skipped)... the type as indicated by the function parameters. This function does NOT return a \code{\linkS4class{GRN}} object.
4331: #' Extract connections or links from a \code{\linkS4class{GRN}} object as da data frame.
4334: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4348: ...(5 bytes skipped)...eturn A data frame with the requested connections. This function does NOT return a \code{\linkS4class{GRN}} object.
4552: "plot_class_density" = "TF_classification_densityPlots",
4553: "plot_class_medianClass" = "TF_classification_stringencyThresholds",
4554: "plot_class_densityClass" = "TF_classification_summaryHeatmap",
4614: ...(0 bytes skipped)...#' Retrieve parameters for previously used function calls and general parameters for a \code{\linkS4class{GRN}} object.
4616: ...(10 bytes skipped)...{Note: This function, as all \code{get} functions from this package, does NOT return a \code{\linkS4class{GRN}} object.}
4622: #' @return The requested parameters. This function does NOT return a \code{\linkS4class{GRN}} object.
4678: #' @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})
4758: #' @return An updated \code{\linkS4class{GRN}} object, with the output directory being adjusted accordingly
4788: dplyr::group_by(.data$r_positive, class, .data$peak_gene.p.raw.class) %>%
4797: row = which(d$r_positive == r_pos & d$class == classCur & as.character(d$peak_gene.p.raw.class) == as.character(pclassCur))
4805: # Restore the "ordered" factor for class
4806: d$peak_gene.p.raw.class = factor(d$peak_gene.p.raw.class, ordered = TRUE, levels = levels(peakGeneCorrelations.all.cur$peak_gene.p.raw.class))
4811: dplyr::group_by(.data$r_positive, .data$class) %>%
4817: dplyr::group_by(class, .data$peak_gene.p.raw.class)%>%
4826: normFactor_real = dplyr::filter(dsum, class == !! (networkType_details[1])) %>% dplyr::pull(.data$sum_n) %>% sum() /
4827: dplyr::filter(dsum, class == !! (networkType_details[2])) %>% dplyr::pull(.data$sum_n) %>% sum()
4831: dplyr::group_by(.data$peak_gene.p.raw.class, .data$r_positive) %>%
4832: dplyr::summarise(n_real = .data$n[class == !! (names(colors_vec)[1]) ],
4833: n_permuted = .data$n[class == !! (names(colors_vec)[2]) ]) %>%
4841: stopifnot(identical(levels(d2$peak_gene.p.raw.class), levels(d3$peak_gene.p.raw.class)))
4844: d_merged <- tibble::tibble(peak_gene.p.raw.class = c(as.character(d2$peak_gene.p.raw.class),
4845: as.character(d3$peak_gene.p.raw.class)),
4850: peak_gene.p.raw.class = factor(.data$peak_gene.p.raw.class, levels = levels(d2$peak_gene.p.raw.class)))
4852: d4 = tibble::tibble(peak_gene.p.raw.class = unique(d$peak_gene.p.raw.class),
4859: row_d2 = which(d2$class == networkType_details[1] & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4863: row_d2 = which(d2$class == paste0("random_",range) & d2$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4867: row_d3 = which(d3$r_positive == TRUE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4869: row_d3 = which(d3$r_positive == FALSE & d3$peak_gene.p.raw.class == d4$peak_gene.p.raw.class[i])
4877: dplyr::arrange(.data$peak_gene.p.raw.class.bin)
4879: d4_melt = reshape2::melt(d4, id = c("peak_gene.p.raw.class.bin", "peak_gene.p.raw.class")) %>%
4893: ...(36 bytes skipped)...ources of biological and technical variation for features (TFs, peaks, and genes) in a \code{\linkS4class{GRN}} object
4913: #' @return An updated \code{\linkS4class{GRN}} object, with additional information added from this function to \code{GRN@stats$varianceParti...(111 bytes skipped)...
4971: coltypes = meta %>% dplyr::summarise_all(class)
83: # Stringencies for AR classification
89: # Colors for the different classifications
167: checkmate::assertClass(GRN, "GRN")
311: if (isIntegerMatrix(counts_peaks[, GRN@config$sharedSamples])) {
312: GRN@data$peaks$counts_orig = DESeq2::DESeqDataSetFromMatrix(counts_peaks[, GRN@config$sharedSamples],
320: if (isIntegerMatrix(counts_rna[, GRN@config$sharedSamples])) {
321: GRN@data$RNA$counts_orig = DESeq2::DESeqDataSetFromMatrix(counts_rna[, GRN@config$sharedSamples],
653: GRN@stats$peaks$GC = GC_classes.df
697: checkmate::assertClass(GRN, "GRN")
957: checkmate::assertClass(GRN, "GRN")
1085: checkmate::assertClass(GRN, "GRN")
1137: if (!all(colnames(TFBS_bindingMatrix.df) %in% GRN@config$allTF)) {
1149: GRN@data$TFs$TF_peak_overlap = TFBS_bindingMatrix.df %>%
1155: GRN@data$TFs$TF_peak_overlap = .asSparseMatrix(as.matrix(GRN@data$TFs$TF_peak_overlap),
1346: checkmate::assertClass(GRN, "GRN")
1428: if (checkmate::testClass(data, "DESeqDataSet")) {
1512: checkmate::assertClass(GRN, "GRN")
1620: ######## AR classification ########
1625: ...(47 bytes skipped)...c[0,1]. Default 0.05. Significance threshold for Wilcoxon test that is run in the end for the final classification. See the Vignette and diffTF paper for details.
1636: #' # GRN = AR_classification_wrapper(GRN, outputFolder = ".", forceRerun = FALSE)
1646: checkmate::assertClass(GRN, "GRN")
1660: GRN@data$TFs$classification$TF.translation.orig = GRN@annotation$TFs %>%
1684: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]])) {
1685: if (is.null(GRN@data$TFs$classification[[permIndex]])) {
1686: GRN@data$TFs$classification[[permIndex]] = list()
1688: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
1691: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground) \|
1692: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background) \|
1693: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground) \|
1694: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background) \|
1695: is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor) \|
1699: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]] = list()
1722: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = res.l[["median_foreground"]]...(0 bytes skipped)...
1723: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = res.l[["median_background"]]...(0 bytes skipped)...
1724: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = res.l[["foreground"]]
1725: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = res.l[["background"]]
1727: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor = TF_peak_cor
1730: # Final classification: Calculate thresholds by calculating the quantiles of the background and compare the real ...(24 bytes skipped)...
1731: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l) \| forceRerun) {
1732: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l =
1733: .calculate_classificationThresholds(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
1737: if (is.null(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification) \| forceRerun) {
1739: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification =
1741: output.global.TFs = GRN@data$TFs$classification$TF.translation.orig %>% dplyr::mutate(TF = .data$TF.name),
1742: median.cor.tfs = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground,
1743: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
1745: t.cor.sel.matrix = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground),
1746: t.cor.sel.matrix.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
1751: # PLOTS FOR THE RNA-SEQ CLASSIFICATION
1762: .plot_density(.asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground),
1763: .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background),
1773: median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground),
1774: median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background),
1776: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
1786: TF_peak_cor = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor
1788: .plot_heatmapAR(TF.peakMatrix.df = peak_TF_overlapCur.df,
1793: median.cor.tfs = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground),
1794: median.cor.tfs.non = .asMatrixFromSparse(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background),
1795: act.rep.thres.l = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l,
1796: finalClassification = GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF.classification,
1806: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_foreground = NULL
1807: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_cor_median_background = NULL
1808: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground = NULL
1809: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background = NULL
1810: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$act.rep.thres.l = NULL
1814: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground =
1815: .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_foreground), convertNA_to_zero = TRUE)
1816: GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background =
1817: .asSparseMatrix(as.matrix(GRN@data$TFs$classification[[permIndex]] [[connectionTypeCur]]$TF_peak_cor_background), convertNA_to_zero = TRUE)
1881: checkmate::assertClass(GRN, "GRN")
2090: minPerc = .findMaxBackgroundSize(GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage = threshold_percentage)
2135: for (i in seq_len(nrow(GC_classes_foreground.df))) {
2143: #Select the minimum, which for low % classes is smaller than the required number to mimic the foreground 100%
2145: nPeaksCur = GC_classes_all.df$n.bg.needed[i]
2147: nPeaksCur = min(GC_classes_all.df$n.bg.needed[i], nrow(peaksBackgroundGCCur))
2150: if (GC_classes_all.df$n.bg.needed[i] > nrow(peaksBackgroundGCCur)) {
2165: plots_GC.l[[TFCur]] = .generateTF_GC_diagnosticPlots(TFCur, GC_classes_foreground.df, GC_classes_background.df, GC_classes_all.df, peaksForeground, peaksBackground, peaksBackgroundGC)
2353: .findMaxBackgroundSize <- function (GC_classes_foreground.df, GC_classes_background.df, peaksBackground, threshold_percentage = 0.05) {
2370: for (i in seq_len(nrow(GC_classes_foreground.df))) {
2372: n_rel = GC_classes_foreground.df$n_rel[i]
2377: availableNoPeaks = GC_classes_background.df %>%
2389: if (i == nrow(GC_classes_foreground.df)) {
2395: } # end of for (i in 1:nrow(GC_classes_foreground.df)) {
2441: checkmate::assertClass(GRN, "GRN")
3031: checkmate::assertClass(GRN, "GRN")
3620: checkmate::assertClass(GRN, "GRN")
3899: checkmate::assertClass(GRN, "GRN")
4253: checkmate::assertClass(GRN, "GRN")
4273: if (checkmate::testClass(countsOrig, "DESeqDataSet")) {
4291: if (checkmate::testClass(countsOrig, "DESeqDataSet")) {
4363: checkmate::assertClass(GRN, "GRN")
4629: checkmate::assertClass(GRN, "GRN")
4675: #' Optional convenience function to delete intermediate data from the function \code{\link{AR_classification_wrapper}} and summary statistics that may occupy a lot of space
4685: checkmate::assertClass(GRN, "GRN")
4693: GRN@data$TFs$classification[[permIndex]]$TF_cor_median_foreground = NULL
4694: GRN@data$TFs$classification[[permIndex]]$TF_cor_median_background = NULL
4695: GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_foreground = NULL
4696: GRN@data$TFs$classification[[permIndex]]$TF_peak_cor_background = NULL
4697: GRN@data$TFs$classification[[permIndex]]$act.rep.thres.l = NULL
4718: checkmate::assertClass(GRN, "GRN")
4766: checkmate::assertClass(GRN, "GRN")
4792: # Some classes might be missing, add them here with explicit zeros
4849: dplyr::mutate(classAll = factor(.data$classAll, levels=c(paste0("real_",range), paste0("random_",range), "TRUE", "FALSE")),
4930: checkmate::assertClass(GRN, "GRN")
84: par.l$internal$allClassificationThresholds = c(0.1, 0.05, 0.01, 0.001)
1315: peak_TF_overlapCur.df = .asMatrixFromSparse(GRN@data$TFs$TF_peak_overlap, convertZero_to_NA = FALSE) %>%
1740: .finalizeClassificationAndAppend(

Pi:R/xMLcaret.r: [ ]
88: class <- as.factor(gs_targets[!is.na(ind)])
87: df_predictor_class <- as.data.frame(df_predictor[ind[!is.na(ind)],])
102: ...(4 bytes skipped)...Control <- caret::trainControl(method=c("repeatedcv","cv","oob")[1], number=nfold, repeats=nrepeat, classProbs=TRUE, summaryFunction=caret::twoClassSummary, allowParallel=FALSE)
3: ...(348 bytes skipped)... in rows and predictors in columns, with their predictive scores inside it. It returns an object of class 'sTarget'.
10: ...(71 bytes skipped)...idataion. Per fold creates balanced splits of the data preserving the overall distribution for each class (GSP and GSN), therefore generating balanced cross-vallidation train sets and testing sets. By defa...(44 bytes skipped)...
18: #' an object of class "sTarget", a list with following components:
20: #' \item{\code{model}: an object of class "train" as a best model}
29: #' \item{\code{evidence}: an object of the class "eTarget", a list with following components "evidence" and "metag"}
85: ## predictors + class
89: levels(class) <- c("GSN","GSP")
90: df_predictor_class$class <- class
94: ...(40 bytes skipped)...ds (%d in GSP, %d in GSN) are used for supervised integration of %d predictors/features (%s).", sum(class=="GSP"), sum(class=="GSN"), ncol(df_predictor), as.character(now)), appendLF=TRUE)
119: fit_gbm <- caret::train(class ~ .,
120: data = df_predictor_class,
158: fit_svm <- caret::train(class ~ .,
159: data = df_predictor_class,
195: fit_rda <- caret::train(class ~ .,
196: data = df_predictor_class,
231: fit_knn <- caret::train(class ~ .,
232: data = df_predictor_class,
267: fit_pls <- caret::train(class ~ .,
268: data = df_predictor_class,
305: suppressMessages(fit_nnet <- caret::train(class ~ .,
306: data = df_predictor_class,
346: fit_rf <- caret::train(class ~ .,
347: data = df_predictor_class,
384: class = c("numeric", 'numeric'),
446: fit_myrf <- caret::train(class ~ .,
447: data = df_predictor_class,
486: fit_crf <- caret::train(class ~ .,
487: data = df_predictor_class,
524: fit_glmnet <- caret::train(class ~ .,
525: data = df_predictor_class,
556: fit_glm <- caret::train(class ~ .,
557: data = df_predictor_class,
589: fit_bglm <- caret::train(class ~ .,
590: data = df_predictor_class,
627: fit_blr <- caret::train(class ~ .,
628: data = df_predictor_class,
669: fit_xgbl <- caret::train(class ~ .,
670: data = df_predictor_class,
712: fit_xgbt <- caret::train(class ~ .,
713: data = df_predictor_class,
792: ...(54 bytes skipped)...atrix of %d rows/genes X %d columns/repeatsfolds, aggregated via '%s' (%s) ...", nrow(df_predictor_class), nfoldnrepeat, aggregateBy, as.character(now)), appendLF=TRUE)
917: class(sTarget) <- "sTarget"
34: #' @seealso \code{\link{xPierMatrix}}, \code{\link{xPredictROCR}}, \code{\link{xPredictCompare}}, \code{\link{xSparseMatrix}}, \code{\link{xSymbol2GeneID}}
57: df_predictor <- xPierMatrix(list_pNode, displayBy="score", combineBy="union", aggregateBy="none", RData.location=RData.location...(12 bytes skipped)...
61: eTarget <- xPierMatrix(list_pNode, displayBy="evidence", combineBy="union", aggregateBy="none", verbose=FALSE, RData.locat...(30 bytes skipped)...
103: fitControl_withoutParameters <- caret::trainControl(method="none", classProbs=TRUE, allowParallel=FALSE)
381: type = "Classification",
394: fit = function(x, y, wts, param, lev, last, classProbs, ...) {
432: levels = function(x) x$classes,
818: df_full <- as.matrix(xSparseMatrix(df_full, verbose=FALSE))

ISAnalytics:R/internal-functions.R: [ ]
98: rlang::warn(warn, class = "missing_crit_tags")
215: ), class = "missing_req_col_err")
243: ), class = "missing_req_col_err")
427: rlang::inform(warn_empty_iss, class = "warn_empty_iss")
535: class = "warn_empty_iss"
597: rlang::abort(error_msg(tags_names), class = "missing_tags_err")
705: rlang::abort(single_err, class = "tag_dupl_err")
770: rlang::abort(compact_msg, class = "tag_type_err")
1011: rlang::abort(add_types_err, class = "add_types_err")
1193: class = "xls_file"
1369: class = "na_concat"
1469: class = "filter_warn"
1850: class = "missing_path_col"
2806: rlang::inform(missing_msg, class = "auto_mode_miss")
3030: class = "coll_matrix_issues"
3095: rlang::abort(not_date_err, class = "not_date_coll_err")
4092: rlang::inform(warn, class = "rec_unsupp_ext")
4599: rlang::abort(err_msg, class = "genomic_file_char")
4688: rlang::warn(warn_miss, class = "warn_miss_genes")
5048: class = "missing_cols_key"
5096: rlang::inform(flag_msg, class = "flag_logic_long")
5413: rlang::abort(format_err, class = "outlier_format_err")
5017: KnownGeneClass = ifelse(
892: colClasses = col_types,
601: rlang::abort(error_msg(missing_tags), class = "missing_tags_err")
723: rlang::inform(single_warn, class = "tag_dupl_warn")
746: err <- c(paste("Wrong col class for tag '", sub_df$tag[1], "'"),
783: # @param x A data.frame object (or any extending class)
802: # @param x A data.frame object (or any extending class)
854: #---- USED IN : import_single_Vispa2Matrix ----
1008: "?import_single_Vispa2Matrix"
1025: class = "unsup_comp_format"
1040: class = "im_single_miss_mand_vars"
1152: class = "ism_import_summary"
2830: rlang::inform(dupl_msg, class = "auto_mode_dupl")
5066: class = "missing_cols_pool"
5118: class = "flag_logic_short"
5136: rlang::abort(unknown_logi_op_err, class = "unsupp_logi_op")
5416: rlang::abort(format_err, class = "outlier_format_err")
767: "Wrong column classes for some tags",
925: col_types <- .mandatory_IS_types("classic")
929: .annotation_IS_types("classic")
1020: ### If not, switch to classic for reading
1021: mode <- "classic"

CancerSubtypes:R/internal.R: [ ]
427: class <- unique(x)
169: affinityMatrix <- function(Diff,K=20,sigma=0.5) {
406: classx <- unique(x)
407: classy <- unique(y)
105: .distanceWeighted2<-function(X,weight) ##X is the expression Matrix(Row is sample, column is feature)
155: #' This is the affinity Matrix function extracted from SNFtool package.
429: nc <- length(class)
433: prob[i] <- sum(x == class[i])/nx
12: #' #W1 = affinityMatrix(Dist1, 20, 0.5)
163: #' W1 = affinityMatrix(Dist1, 20, 0.5)
196: ###You can do various applications on this graph, such as clustering(subtyping), classification, prediction.
409: ncx <- length(classx)
410: ncy <- length(classy)
415: probxy[i, j] <- sum((x == classx[i]) & (y == classy[j])) / nx

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.")

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

BioNERO:R/gcn_inference.R: [ ]
450: Matrix = modtraitcor, yLabels = yLabels, xLabels = xLabels,
428: textMatrix <- paste(signif(modtraitcor, 2), modtraitsymbol, sep = "")
776: # Create a data frame containing annotations and the annotation class
786: # Add column containing the annotation class
429: dim(textMatrix) <- dim(modtraitcor)
433: textMatrix <- t(textMatrix)
452: colors = cols, textMatrix = textMatrix, setStdMargins = FALSE,

psichomics:R/analysis.R: [ ]
888: warn <- tags$div(class="alert alert-warning", role="alert",
969: error <- tagList(h4("t-test"), tags$div(class="alert alert-danger",
118: #' @param match Matrix: match between samples and subjects
254: class(survTime) <- c("data.frame", "survTime")
407: class(res) <- c("survTerms", class(res))
556: if ("simpleError" %in% class(survTerms)) {
686: if ("simpleError" %in% class(survTerms)) return(NA)
975: warn <- tags$div(class="alert alert-warning", role="alert",
1220: type <- sapply(cols, function(i) class(df[[i]]))
2064: #' @return A list with class \code{"htest"} containing the following components:
2103: class(rval) <- "htest"
2710: type <- sapply(cols, function(i) class(stats[[i]]))
3178: div(class="col-sm-6 col-md-4",
3179: div(class="thumbnail", style="background:#eee;",
3180: div(class="caption", uiOutput(ns(id)))))
3193: class="btn-info btn-md btn-block", class="visible-lg visible-md"),
3196: class="btn-info btn-xs btn-block", class="visible-sm visible-xs"))
3214: actionButton(ns("analyse"), "Perform analyses", class="btn-primary"),
3224: div(class="row", card("ttest"), card("levene")),
3226: div(class="row", card("wilcox"), card("kruskal"), card("fligner")))
3307: event, class=NULL, showPath=FALSE, showText=FALSE,
3315: event, class=NULL, showPath=FALSE, showText=FALSE,

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]

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: [ ]
4012: shinyjs::addClass(id = "cv_button1", class = "btn-block")
4910: classes <- names(colData(hmTemp$sce))
6010: classCol <- colData(vals$counts)[[input$deC1Class]]
6011: classChoices <- sort(as.vector(unique(classCol)))
98: updateSelectInput(session, "deC1Class",
240: label = "Select Input Matrix:",
244: label = "Select Input Matrix:",
250: label = "Select Input Matrix:",
2643: shinyjs::show(selector = ".dimRedPCAICA_plotTabset_class")
4013: shinyjs::addClass(id = "cv_button2", class = "btn-block")
4014: shinyjs::addClass(id = "cv_button3", class = "btn-block")
4434: pltVars$class <- "factor"
4436: pltVars$class <- "numeric"
4489: conditionClass = pltVars$class, defaultTheme = as.logical(pltVars$defTheme))
4505: ...(28 bytes skipped)... legendSize = input$adjustlegendsize,legendTitleSize = input$adjustlegendtitlesize,conditionClass = pltVars$class)
4971: ...(7 bytes skipped)... p("Since more than 12 unique values detected, discrete colors will be assigned for this class")
4994: 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)...
5027: p("No effective category found for the class.")
5353: selectInput("batchCheckCorrName", "Corrected Matrix",
6009: !input$deC1Class == "None"){
6022: !input$deC1Class == "None"){
6023: classCol <- colData(vals$counts)[[input$deC1Class]]
6036: g1Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G1
6050: g2Idx <- colData(vals$counts)[[input$deC1Class]] %in% input$deC1G2
6232: class = input$deC1Class,
3350: conditionClass = "factor",
3399: conditionClass = "factor",
4911: selectInput('hmCellAnn', 'Add cell annotation', classes,
4918: classes <- names(rowData(hmTemp$sce))
4919: selectInput('hmGeneAnn', 'Add feature annotation', classes,
5436: conditionClass = "character",
5444: conditionClass = "character",
6013: choices = classChoices, multiple = TRUE)
6024: classChoices <- sort(as.vector(unique(classCol)))
6026: choices = classChoices, multiple = TRUE)
6233: classGroup1 = input$deC1G1,
6234: classGroup2 = input$deC1G2,
7219: vals$effectSizes <- calcEffectSizes(countMatrix = expData(vals$counts, input$snapshotAssay), condition = colData(vals$counts)[, input$selectSnapsho...(12 bytes skipped)...

ChromSCape:inst/server.R: [ ]
2227: buttonType = "default", class = NULL) %>%
3118: MSIG.classes <- reactive({
52: req(input$options.bpparam_class, input$options.nb_workers)
53: BPPARAM = BiocParallel::bpparam(input$options.bpparam_class)
455: you have to input a single Dense Matrix."),
654: title = shiny::HTML(paste0("<span style='color: white'><h4> <i class='far fa-caret-square-down'",
666: title = shiny::HTML(paste0("<span style='color: white'><h4> <i class='far fa-caret-square-down'",
993: df = data.frame(coverage = sort(unname(Matrix::colSums(init$datamatrix))))
1027: df = data.frame(coverage = sort(unname(Matrix::rowSums(init$datamatrix)),decreasing = TRUE))
2516: # return( as.character(icon("check-circle", class = "large_icon")))}
2518: # return( as.character(icon("times-circle", class = "large_icon")))
3025: DT::datatable(diff, options = list(dom='tpi'), class = "display",
3196: output$enr_class_sel <- renderUI({
3199: inputId = "enr_class_sel", inline = TRUE,
3207: ...(4 bytes skipped)... if(!is.null(scExp_cf()@metadata$enr) && !is.null(input$GSA_group_sel) && !is.null(input$enr_class_sel) &&
3213: enr_class_sel = input$enr_class_sel) %>%
3218: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3232: enr_class_sel = input$enr_class_sel) %>%
3237: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3252: enr_class_sel = input$enr_class_sel) %>%
3257: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3297: most_diff[,"qval"] = Matrix::rowMeans(as.matrix(most_diff[,-1]))
3507: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3522: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
3537: options = list(pageLength = 10, dom = 'tpi'), class = 'display', rownames = FALSE)
187: if(input$data_choice_box == "DenseMatrix"){
195: else if (input$data_choice_box == "SparseMatrix"){
232: Example of folder structure (for scBED, but applies in a similar manner for SparseMatrix, Fragment File & scBAM) : <br>
264: if(input$data_choice_box != "DenseMatrix"){
283: if(input$data_choice_box != "DenseMatrix"){
284: if(input$data_choice_box == "SparseMatrix"){
336: if(input$data_choice_box %in% c("DenseMatrix", "SparseMatrix"))
448: if(type_file == "DenseMatrix" & !is.null(input$datafile_matrix)){
481: if(type_file == "SparseMatrix") {
496: if(type_file == "SparseMatrix"){
3174: GeneSetClasses = MSIG.classes(), progress = progress))
3197: req(MSIG.classes())
3200: label = "Select classes to display:",
3201: selected = MSIG.classes(), choiceNames = MSIG.classes(),
3202: choiceValues = MSIG.classes())
3351: req(pathways(), annotation_id(), MSIG.classes())
3360: database <- load_MSIGdb(annotation_id(), MSIG.classes())

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

Prostar:inst/ProstarApp/global.R: [ ]
497: class = "progress",
567: class = "progress-group",
722: textEl <- tags$p(class = "navbar-text", text)
734: form <- tags$form(class = "navbar-form", inputs)
296: classID <- getOption("shiny.table.id", "example")
711: actionBtnClass <- "btn-primary"
713: PrevNextBtnClass <- "btn-info"
714: optionsBtnClass <- "info"
295: getOption("shiny.table.class", "table table-striped table-bordered")
306: paste("class=\"", classNames, "\" id=\"", classID, "\"",
446: div(class = "busy-indicator", p(text), img(src = img)),
498: class = if (!is.null(size)) paste0("progress-", size),
499: class = if (vertical) "vertical",
500: class = if (active) "active",
502: class = "progress-bar",
503: class = paste0("progress-bar-", color),
504: class = if (striped) "progress-bar-striped",
513: class = if (!label) "sr-only",
568: tags$span(class = "progress-text", text),
569: tags$span(class = "progress-number", sprintf("%d / %d", value, max)),
154: "Whole matrix" = "WholeMatrix",
161: gFiltersListAnaDiff[["Whole matrix"]] <- "WholeMatrix"
294: classNames <-
347: "corrMatrix" = "corrMatrix",
372: corrMatrix = "corrMatrix.png",
449: if ($('html').hasClass('shiny-busy')) {
451: if ($('html').hasClass('shiny-busy')) {
744: "intensity", "pca", "varDist", "corrMatrix",
393: GOClassificationImg1 = "GOClassification_img1.png",
394: GOClassificationImg2 = "GOClassification_img2.png",
395: GOClassificationImg3 = "GOClassification_img3.png",

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

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

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

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

Pi:R/xMLrandomforest.r: [ ]
89: class <- as.factor(gs_targets[!is.na(ind)])
88: df_predictor_class <- as.data.frame(df_predictor[ind[!is.na(ind)],])
3: ...(283 bytes skipped)... in rows and predictors in columns, with their predictive scores inside it. It returns an object of class 'sTarget'.
9: ...(71 bytes skipped)...idataion. Per fold creates balanced splits of the data preserving the overall distribution for each class (GSP and GSN), therefore generating balanced cross-vallidation train sets and testing sets. By defa...(44 bytes skipped)...
20: #' an object of class "sTarget", a list with following components:
32: #' \item{\code{evidence}: an object of the class "eTarget", a list with following components "evidence" and "metag"}
86: ## predictors + class
90: df_predictor_class$class <- class
94: ...(40 bytes skipped)...ds (%d in GSP, %d in GSN) are used for supervised integration of %d predictors/features (%s).", sum(class==1), sum(class==0), ncol(df_predictor), as.character(now)), appendLF=TRUE)
104: message(sprintf("2. GS matrix of %d rows/genes X %d columns (predictors+class) are used as train set (%s) ...", nrow(df_predictor_class), ncol(df_predictor_class), as.character(now)), appendLF=TRUE)
106: message(sprintf("2. GS matrix of %d rows/genes X %d columns (predictors+class...(102 bytes skipped)...e remaining '1/%d' as test set. These spits are repeated over %d times (%s) ...", nrow(df_predictor_class), ncol(df_predictor_class), nfold, nfold-1, nfold, nfold, nrepeat, as.character(now)), appendLF=TRUE)
112: # preserve the overall class distribution
116: index_sets <- caret::createMultiFolds(y=df_predictor_class$class, k=nfold, times=nrepeat)
121: res_ls <- caret::createFolds(y=df_predictor_class$class, k=nfold, list=TRUE, returnTrain=TRUE)
143: trainset <- df_predictor_class[index_sets[[i]],]
146: message(sprintf("\tFold %d: %d GSP + %d GSN", i, table(trainset$class)[2], table(trainset$class)[1]), appendLF=TRUE)
159: #suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry, ...))
160: suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry))
166: trainset <- df_predictor_class[trainindex,]
169: message(sprintf("\tRepeatFold %d: %d GSP + %d GSN", i, table(trainset$class)[2], table(trainset$class)[1]), appendLF=TRUE)
183: #suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry, ...))
184: suppressMessages(rf.model <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry))
194: ...(51 bytes skipped)...eature importance matrix of %d rows/predictors X %d columns/repeatsfolds (%s).", ncol(df_predictor_class)-1, nfoldnrepeat, as.character(now)), appendLF=TRUE)
229: trainset <- df_predictor_class
240: suppressMessages(rf.model.overall <- randomForest::randomForest(class ~ ., data=trainset, importance=TRUE, ntree=ntree, mtry=mtry))
241: rf.model.overall.importance <- randomForest::importance(rf.model.overall, type=NULL, class=NULL, scale=TRUE)[,3:4]
249: ...(38 bytes skipped)...OC matrix of %d rows (Supervised + predictors) X %d columns/repeatsfolds (%s).", ncol(df_predictor_class), nfoldnrepeat, as.character(now)), appendLF=TRUE)
259: testset <- df_predictor_class[-trainindex,]
351: ...(54 bytes skipped)...atrix of %d rows/genes X %d columns/repeatsfolds, aggregated via '%s' (%s) ...", nrow(df_predictor_class), nfoldnrepeat, fold.aggregateBy, as.character(now)), appendLF=TRUE)
473: class(sTarget) <- "sTarget"
37: #' @seealso \code{\link{xPierMatrix}}, \code{\link{xSparseMatrix}}, \code{\link{xPredictROCR}}, \code{\link{xPredictCompare}}, \code{\link{xSymbol2GeneID}}
58: df_predictor <- xPierMatrix(list_pNode, displayBy="score", combineBy="union", aggregateBy="none", RData.location=RData.location...(12 bytes skipped)...
62: eTarget <- xPierMatrix(list_pNode, displayBy="evidence", combineBy="union", aggregateBy="none", verbose=FALSE, RData.locat...(30 bytes skipped)...
206: df_res <- as.matrix(xSparseMatrix(df_res, verbose=FALSE))
294: df_res <- as.matrix(xSparseMatrix(df_res[,-4], verbose=FALSE))
317: df_res <- as.matrix(xSparseMatrix(df_res[,-3], verbose=FALSE))
362: df_full <- as.matrix(xSparseMatrix(df_full, verbose=FALSE))

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

GRaNIE:R/plot.R: [ ]
1014: dplyr::mutate(class = paste0("real_",range))) %>%
2431: dplyr::mutate(Class = dplyr::if_else(.data$isTF, "TF", "gene"))
3184: dplyr::mutate(class = droplevels(factor(class, levels = c("TF", "peak (TF end)", "peak (gene end)", "gene")))) %>% # in case it was the TF-gene d...(50 bytes skipped)...
1026: class_levels = c(paste0("real_",range), paste0("random_",range))
1053: peak_gene.p.raw.class = cut(.data$peak_gene.p_raw, breaks = seq(0,1,0.05), include.lowest = TRUE, ordered_result = TRUE),...(0 bytes skipped)...
1054: peak_gene.r.class = cut(.data$peak_gene.r, breaks = seq(-1,1,0.05), include.lowest = TRUE, ordered_result = TRUE)) %>...(1 bytes skipped)...
1072: colors_class = c("black", "black")
1076: r_pos_class = c("black", "darkgray")
1079: dist_class = c("dark red", "#fc9c9c")
1083: freq_class = paste0(gsub(names(freqs), pattern = "(.+)(_.*)", replacement = "\\1"), " (n=", .prettyNum(freqs) ...(6 bytes skipped)...
1089: xlabels_peakGene_r.class = levels(peakGeneCorrelations.all$peak_gene.r.class)
1098: xlabels_peakGene_praw.class = levels(peakGeneCorrelations.all$peak_gene.p.raw.class)
1181: customLabel_class = .customLabeler(table(peakGeneCorrelations.all[indexCur,]$class))
1051: peak_gene.distance_class_abs = forcats::fct_explicit_na(addNA(cut(abs(.data$peak_gene.distance),
1094: xlabels_peakGene_r.class2 = levels(peakGeneCorrelations.all$peak_gene.r.class)
1456: dplyr::mutate(peak_gene.distance.class250k = factor(dplyr::if_else(.data$peak_gene.distance <= 250000, "<=250k", ">250k"))) %>%
1493: distance_class_abund = table(peakGeneCorrelations.all[indexCur,]$peak_gene.distance_class_abs)
592: GC_classes_background_GC.df = peaksBackgroundGC %>%
600: GC_classes_all2.df = rbind(GC_classes_foreground.df, GC_classes_background.df, GC_classes_background_GC.df) %>%
1045: nClasses_distance = 10
8: #' Produce a PCA plot of the data from a \code{\linkS4class{GRN}} object
21: #' @return An updated \code{\linkS4class{GRN}} object.
368: # data: Matrix of your data, with column names. May or may not be vsd transformed, log2 transformed etc
487: #' Plot diagnostic plots for TF-peak connections for a \code{\linkS4class{GRN}} object
502: #' @return An updated \code{\linkS4class{GRN}} object.
593: dplyr::group_by(.data$GC_class) %>%
596: tidyr::complete(.data$GC_class, fill = list(n = 0)) %>%
602: dplyr::left_join(GC_classes_all.df, by = "GC_class") %>%
616: g1 = ggplot2::ggplot(GC_classes_all2.df , ggplot2::aes(.data$GC_class, .data$n_rel, group = .data$type, fill = .data$type, label = .data$n.bg.needed.relFreq)) +
624: ggplot2::xlab("GC class from peaks") +
629: g2 = ggplot2::ggplot(GC_classes_all2.df , ggplot2::aes(.data$GC_class, log10(.data$n+1), group = .data$type, fill = .data$type)) +
635: ggplot2::xlab("GC class from peaks") +
879: #' Plot diagnostic plots for peak-gene connections for a \code{\linkS4class{GRN}} object
893: #' @return An updated \code{\linkS4class{GRN}} object.
1016: dplyr::mutate(class = paste0("random_",range)))
1031: dplyr::mutate(class = factor(paste0("real_",range), levels = class_levels)),
1033: dplyr::mutate(class = factor(paste0("random_",range), levels = class_levels))) %>%
1049: peak_gene.distance_class =
1059: levels(peakGeneCorrelations.all$peak_gene.distance_class_abs)[1] =
1060: gsub("(-\\d+)", "0", levels(peakGeneCorrelations.all$peak_gene.distance_class_abs)[1], perl = TRUE)
1065: dplyr::mutate(peak_gene.p_raw.robust.class =
1073: names(colors_class)= unique(peakGeneCorrelations.all$class)
1074: colors_class[which(grepl("random", names(colors_class)))] = "darkgray"
1077: names(r_pos_class) =c("TRUE", "FALSE")
1080: names(dist_class) = class_levels
1082: freqs= table(peakGeneCorrelations.all$class)
1085: freq_class = gsub(freq_class, pattern = "random", replacement = "permuted")
1086: names(freq_class) <- names(freqs)
1090: nCur = length(xlabels_peakGene_r.class)
1091: xlabels_peakGene_r.class[setdiff(seq_len(nCur), c(1, floor(nCur/2), nCur))] <- ""
1095: nCur = length(xlabels_peakGene_r.class2)
1096: xlabels_peakGene_r.class2[setdiff(seq_len(nCur), c(1, floor(nCur/4), floor(nCur/2), floor(nCur/4*3), nCur))] <- ""
1099: nCur = length(xlabels_peakGene_praw.class)
1100: xlabels_peakGene_praw.class[setdiff(seq_len(nCur), c(1, floor(nCur/2), nCur))] <- ""
1170: indexCurReal = intersect(indexCur, which(peakGeneCorrelations.all$class == names(dist_class)[1]))
1180: # Produce the labels for the class-specific subtitles
1195: ggplot2::facet_wrap(~ .data$class, labeller = ggplot2::labeller(class=freq_class) ) +
1197: ggplot2::scale_color_manual(labels = names(r_pos_class), values = r_pos_class) +
1207: xlabels = levels(tbl.l$d_merged$peak_gene.p.raw.class)
1210: gB3 = ggplot2::ggplot(tbl.l$d_merged, ggplot2::aes(.data$peak_gene.p.raw.class, .data$ratio, fill = .data$classAll)) +
1214: ggplot2::scale_fill_manual("Class", values = c(dist_class, r_pos_class),
1217: ggplot2::scale_x_discrete(labels = xlabels_peakGene_praw.class) +
1231: sum_real = table(peakGeneCorrelations.all[indexCur,]$class)[names(dist_class)[1]]
1232: sum_rnd = table(peakGeneCorrelations.all[indexCur,]$class)[names(dist_class)[2]]
1234: dplyr::group_by(class) %>%
1235: dplyr::count(.data$peak_gene.r.class) %>%
1236: dplyr::mutate(nnorm = dplyr::case_when(class == !! (names(dist_class)[1]) ~ .data$n / (sum_real / sum_rnd),
1241: gD = ggplot2::ggplot(binData.r, ggplot2::aes(.data$peak_gene.r.class, .data$nnorm, group = .data$class, fill = .data$class)) +
1243: ggplot2::geom_line(ggplot2::aes(.data$peak_gene.r.class, .data$nnorm, group = .data$class, color= .data$class), stat = "identity") +
1244: ggplot2::scale_fill_manual("Group", labels = names(dist_class), values = dist_class) +
1245: ggplot2::scale_color_manual("Group", labels = names(dist_class), values = dist_class) +
1246: ggplot2::scale_x_discrete(labels = xlabels_peakGene_r.class2, drop = FALSE) +
1293: allVars = c("peak.GC.class", "peak.width", "peak.mean","peak.median",
1307: dplyr::select(.data$peak_gene.p_raw, tidyselect::all_of(varCur), .data$class, .data$r_positive, .data$peak_gene.p.raw.class, .data$peak_gene.distance)
1310: dplyr::select(.data$peak_gene.p_raw, .data$class, .data$gene.CV, .data$peak.CV, .data$r_positive, .data$peak_gene.p.raw.class, .data$peak_gene.distance)
1316: if (varCur %in% c("peak.annotation","peak.GC.class")) {
1322: newColName = paste0(varCur, ".class")
1391: # Class in the ggplot2::facet_wrap has been removed, as this is only for real data here
1402: dplyr::group_by(class, .data[[newColName]], .data$peak_gene.p.raw.class, .data$r_positive) %>%
1405: tidyr::complete(class, .data[[newColName]], .data$peak_gene.p.raw.class, .data$r_positive, fill = list(n = 0)) %>% # SOme cases might be missing
1406: dplyr::group_by(class, .data[[newColName]], .data$peak_gene.p.raw.class) %>% # dont group by r_positive because we want to calculate the ratio within each group
1410: dplyr::filter(.data$r_positive, class == names(dist_class)[1])# Keep only one r_positive row per grouping as we operate via the ratio and this data is duplic...(129 bytes skipped)...
1416: gB3 = ggplot2::ggplot(freq, ggplot2::aes(.data$peak_gene.p.raw.class, .data$ratio_pos_raw, fill = .data[[newColName]])) +
1420: ggplot2::scale_x_discrete(labels = xlabels_peakGene_praw.class) +
1426: ggplot2::facet_wrap(~ factor(class), nrow = 2, scales = "free_y", strip.position = "left", labeller = ggplot2::labeller(class=freq_class))
1470: ggplot2::facet_wrap(~ peak_gene.distance.class250k + .data[[newColName]], nrow = nrows_plot, scales = "free_y") +
1473: ggplot2::scale_fill_manual("Class for r", values = mycolors, labels = r_positive_label, drop = FALSE ) +
1494: indexFilt = which(peakGeneCorrelations.all$peak_gene.distance_class_abs %in%
1495: names(distance_class_abund)[which(distance_class_abund > 50)])
1501: ...(37 bytes skipped)...eCorrelations.all[indexFilt,], ggplot2::aes(.data$peak_gene.p_raw, color = .data$peak_gene.distance_class_abs)) + ggplot2::geom_density() +
1504: ggplot2::scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$peak_gene.distance_class_abs))) +
1514: ...(46 bytes skipped)...ations.all[indexFilt,], ggplot2::aes(.data$peak_gene.p_raw.robust, color = .data$peak_gene.distance_class_abs)) + ggplot2::geom_density() +
1517: ggplot2::scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$peak_gene.distance_class_abs))) +
1537: ggplot2::facet_wrap(~ peak_gene.distance_class_abs, ncol = 2, labeller = .customLabeler(table(peakGeneCorrelations.all$peak_gene.distance_class_abs))) +
1538: ggplot2::scale_color_manual(labels = .classFreq_label(table(peakGeneCorrelations.all[indexFilt,]$r_positive)), values = r_pos_class) +
1553: ...(30 bytes skipped)...akGeneCorrelations.all[indexCur,], ggplot2::aes(.data$peak_gene.r, color = .data$peak_gene.distance_class_abs)) + ggplot2::geom_density() +
1556: ggplot2::scale_color_viridis_d(labels = .classFreq_label(table(peakGeneCorrelations.all[indexCur,]$peak_gene.distance_class_abs))) +
1594: #' Plot various network connectivity summaries for a \code{\linkS4class{GRN}} object
1605: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
1937: #' Plot general structure and connectivity statistics for a filtered \code{\linkS4class{GRN}} object
1948: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
1992: totalVerteces = data.frame(Class = c("TF", "Peak", "Gene"),
2007: gVertexDist = ggplot2::ggplot(totalVerteces, ggplot2::aes(x="", y=.data$Count, fill=.data$Class)) + ggplot2::geom_bar(stat="identity") +
2166: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
2331: #' Plot general structure & connectivity statistics for each community in a filtered \code{\linkS4class{GRN}}
2347: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
2437: # Class: TF or gene
2438: ...(11 bytes skipped)...mmunityVertices = ggplot2::ggplot(communityVertices, ggplot2::aes(x = .data$community, fill = .data$Class)) +
2535: #' Plot community-based enrichment results for a filtered \code{\linkS4class{GRN}} object
2548: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
2894: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
3183: .id = "class") %>%
3185: dplyr::arrange(class, dplyr::desc(.data$Degree))
3190: gDegrees = ggplot2::ggplot(degrees.table, ggplot2::aes(x=.data$Degree, fill = class)) +
3196: ggplot2::facet_wrap(~class, labeller = ggplot2::labeller(class=.facetLabel(degrees.table)), scales = "free", ncol = 2) +
3201: dplyr::filter(class =="gene") %>%
3206: dplyr::filter(class =="TF") %>%
3313: dplyr::group_by(class) %>%
3315: dplyr::mutate(label = paste0(class, "\n(mean: ", round(mean, 1),
3321: names(labels) <- summary$class
3331: ...(36 bytes skipped)...tered eGRN in a very flexible manner and requires a filtered set of connections in the \code{\linkS4class{GRN}} object as generated by \code{\link{filterGRNAndConnectGenes}}.
3355: #' @return The same \code{\linkS4class{GRN}} object, without modifications.
33: checkmate::assertClass(GRN, "GRN")
62: transformation = dplyr::if_else(checkmate::testClass(GRN@data$RNA$counts_orig, "DESeqDataSet"), "vst", "log2")
118: transformation = dplyr::if_else(checkmate::testClass(GRN@data$peaks$counts_orig, "DESeqDataSet"), "vst", "log2")
175: checkmate::assertClass(counts, "DESeqDataSet")
184: checkmate::assertMatrix(counts)
189: checkmate::assertMatrix(counts)
518: checkmate::assertClass(GRN, "GRN")
590: .generateTF_GC_diagnosticPlots <- function(TFCur, GC_classes_foreground.df, GC_classes_background.df, GC_classes_all.df, peaksForeground, peaksBackground, peaksBackgroundGC) {
607: GC_classes_all2.df$n.bg.needed.relFreq[GC_classes_all2.df$type != "background_GC" \| GC_classes_all2.df$n == 0] = ""
912: checkmate::assertClass(GRN, "GRN")
1394: ggplot2::geom_density(ggplot2::aes(color = .data$classNew), color = "black", linetype = "dotted", alpha = 1) +
1471: ggplot2::xlab(xlabel) + ggplot2::ylab(paste0("Abundance for classes with n>=", nGroupsMin)) + ggplot2::theme_bw() +
1490: # Here, we focus on distance and exclude distance classes with too few points and create a new subset of the data
1492: # Filter distance classes with too few points
1618: checkmate::assertClass(GRN, "GRN")
1962: checkmate::assertClass(GRN, "GRN")
2183: checkmate::assertClass(GRN, "GRN")
2363: checkmate::assertClass(GRN, "GRN")
2567: checkmate::assertClass(GRN, "GRN")
2915: checkmate::assertClass(GRN, "GRN")
3365: checkmate::assertClass(GRN, "GRN")
1050: forcats::fct_explicit_na(addNA(cut(.data$peak_gene.distance, breaks = nClasses_distance, include.lowest = TRUE)), "random"),
1052: breaks = nClasses_distance, include.lowest = TRUE, ordered_result = TRUE)), "random"),

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

ISAnalytics:R/utility-functions.R: [ ]
39: purrr::walk(desc_msg, ~ rlang::inform(.x, class = "tag_inspect"))
494: rlang::inform(warn_msg, class = "missing_quant_specs")
637: rlang::inform(err_msg, class = "skip_col_transform")
736: rlang::inform(.nas_introduced_msg(), class = "comp_nas")
964: rlang::inform(msg, class = "launch_af_empty")
1173: 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")
1306: rlang::inform("Matrix suffixes specs successfully changed")

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

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

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

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

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

metaseqR2:R/main.R: [ ]
2768: invisible(knitr::knit_meta(class=NULL,clean=TRUE))
1580: tempMatrix <- round(counts(normGenes,normalized=TRUE))
1609: classes <- asClassVector(sampleList)
1577: switch(class(normGenes)[1],
1608: # Dribble for taking a mtx out of SeqCountSet class
1659: switch(class(normGenes)[1],
1896: switch(class(normGenesExpr)[1],
1579: #tempMatrix <- round(DESeq::counts(normGenes,normalized=TRUE))
1583: tempMatrix <- round(DESeq2::counts(normGenes,normalized=TRUE))
1589: tempMatrix <- round(t(t(normGenes$counts)/scl)*mean(scl))
1592: tempMatrix <- normGenes
1595: tempMatrix <- as.matrix(normGenes)
1598: tempMatrix <- as.matrix(round(sweep(normGenes$counts,2,
1602: tempMatrix <- as.matrix(round(normGenes$pseudo.counts))
1605: tempMatrix <- as.matrix(round(excounts(normGenes)))
1610: theDesign <- data.frame(condition=classes,
1616: #tempMatrix <- as.matrix(round(DESeq::counts(cds,
1618: tempMatrix <- as.matrix(round(counts(cds,normalized=TRUE)))
1624: geneFilterOut <- filterGenes(tempMatrix,geneData,geneFilters,
1652: geneCountsDead <- tempMatrix[theDead,]
1658: theDeadInd <- match(theDead,rownames(tempMatrix))
1709: classes <- asClassVector(sampleList)
1710: theDesign <- data.frame(condition=classes,
1934: classes <- asClassVector(sampleList)
1935: theDesign <- data.frame(condition=classes,

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

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

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

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

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

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

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

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

HDF5Array:R/H5SparseMatrixSeed-class.R: [ ]
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",

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

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)

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

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

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