Fixing quantile probs argument (closes #387).

Author: lima1

DESCRIPTION

@@ -2,8 +2,8 @@ Package: PureCN
 Type: Package
 Title: Copy number calling and SNV classification using
     targeted short read sequencing
-Version: 2.15.0
-Date: 2025-03-28
+Version: 2.15.1
+Date: 2025-04-17
 Authors@R: c(person("Markus", "Riester",
                     role = c("aut", "cre"),
                     email = "markus.riester@novartis.com",
@@ -64,6 +64,7 @@ Enhances:
     genomicsdb (>= 0.0.3)
 VignetteBuilder: knitr
 License: Artistic-2.0
+BugReports: https://github.com/lima1/PureCN/issues
 URL: https://github.com/lima1/PureCN
 biocViews: CopyNumberVariation, Software, Sequencing,
     VariantAnnotation, VariantDetection, Coverage, ImmunoOncology

R/correctCoverageBias.R

@@ -123,11 +123,11 @@ output.qc.file = NULL) {
                     as.data.frame(normalized)[, ids])
 
     tumCov <- tumCov[which(tumCov$average.coverage <
-        quantile(tumCov$average.coverage, 0.999, na.rm = TRUE)), ]
+        quantile(tumCov$average.coverage, probs = 0.999, na.rm = TRUE)), ]
 
     if (sum(!tumCov$on.target)) {
         tumCov <- tumCov[which(tumCov$on.target | tumCov$average.coverage <
-            quantile(tumCov$average.coverage[!tumCov$on.target], 0.99, na.rm = TRUE)), ]
+            quantile(tumCov$average.coverage[!tumCov$on.target], probs = 0.99, na.rm = TRUE)), ]
     }
     tumCov$on.target <- factor(ifelse(tumCov$on.target, "on-target", "off-target"),
         levels = c("on-target", "off-target"))
@@ -227,10 +227,10 @@ output.qc.file = NULL) {
         if (!any(tumor$valid)) next
         tumor$ideal <- TRUE
         routlier <- 0.01
-        range <- quantile(tumor$average.coverage[tumor$valid], prob =
+        range <- quantile(tumor$average.coverage[tumor$valid], probs =
             c(0, 1 - routlier), na.rm = TRUE)
         doutlier <- 0.001
-        domain <- quantile(tumor$gc_bias[tumor$valid], prob = c(doutlier, 1 - doutlier),
+        domain <- quantile(tumor$gc_bias[tumor$valid], probs = c(doutlier, 1 - doutlier),
             na.rm = TRUE)
 
         tumor$ideal[!tumor$valid |
@@ -243,7 +243,7 @@ output.qc.file = NULL) {
         if (!any(tumor$ideal)) next
 
         if (!on.target) {
-            widthR <- quantile(width(tumor[tumor$ideal]), prob = 0.1)
+            widthR <- quantile(width(tumor[tumor$ideal]), probs = 0.1)
             tumor$ideal[width(tumor) < widthR] <- FALSE
         }
         rough <- loess(tumor$average.coverage[tumor$ideal] ~ tumor$gc_bias[tumor$ideal],

R/filterIntervals.R

@@ -275,7 +275,7 @@ normalDB.min.coverage, normalDB.max.missing) {
 
 
 .filterIntervalsLowHighGC <- function(intervalsUsed, tumor, filter.lowhigh.gc) {
-    qq <- quantile(tumor$gc_bias, p = c(filter.lowhigh.gc,
+    qq <- quantile(tumor$gc_bias, probs = c(filter.lowhigh.gc,
         1 - filter.lowhigh.gc), na.rm = TRUE)
 
     nBefore <- sum(intervalsUsed)

R/plotAbs.R

@@ -199,7 +199,7 @@ ss) {
     containsOfftarget <- sum(on.target)!=length(on.target)
     if (!containsOfftarget) return(NULL)
     myylim <- quantile(subset(log.ratio,
-        !is.infinite(log.ratio)), p=c(0.0001, 1-0.0001),na.rm=TRUE)
+        !is.infinite(log.ratio)), probs = c(0.0001, 1 - 0.0001), na.rm = TRUE)
     plot(log.ratio, col=ifelse(on.target, "black", "red"),
         pch=".",cex=3, ylim=myylim, ylab="log2 ratio")
     legend("bottomleft", legend=c("On-Target", "Off-Target"), ncol=2, fill=c("black", "red"))
@@ -240,7 +240,7 @@ ss) {
     minLogRatio <- max(min(seg$seg.mean), peak.ideal.means[1]*2)
     logRatio <- do.call(c, lapply(seq_len(nrow(seg)), function(i)
                         rep(seg$seg.mean[i], seg$num.mark[i])))
-    minLogRatio <- min(minLogRatio, quantile(logRatio, p=0.01))
+    minLogRatio <- min(minLogRatio, quantile(logRatio, probs = 0.01))
     logRatio <- logRatio[logRatio >= minLogRatio]
 
     # estimate genome fractions
@@ -377,10 +377,10 @@ ss) {
             ylim = c(
                    min(
                         log2(.calcExpectedRatio(-0.1, purity, ploidy)),
-                        quantile(logRatio$log.ratio, p = 0.0001)),
+                        quantile(logRatio$log.ratio, probs = 0.0001)),
                    max(
                         max(segment.log.ratio*1.1),
-                        quantile(logRatio$log.ratio, p = 1 - 0.0001))
+                        quantile(logRatio$log.ratio, probs = 1 - 0.0001))
                    ),
             ...)
         points(x, r$log.ratio[idx], col=col.snps, pch=mypch)
@@ -418,7 +418,7 @@ ss) {
             round(res$results[[id]]$log.likelihood, digits = 2))
 
         myylim <- quantile(subset(r$log.ratio,
-            !is.infinite(r$log.ratio)), p=c(0.001, 1-0.001), na.rm=TRUE)
+            !is.infinite(r$log.ratio)), probs = c(0.001, 1 - 0.001), na.rm = TRUE)
         myylim[1] <- floor(myylim[1])
         myylim[2] <- ceiling(myylim[2])
 
@@ -491,7 +491,7 @@ ss) {
 
     seg <- res$results[[id]]$seg
     mylogratio.xlim <- quantile(subset(r$log.ratio,
-            !is.infinite(r$log.ratio)), p = c(0.001, 1 - 0.001), na.rm = TRUE)
+            !is.infinite(r$log.ratio)), probs = c(0.001, 1 - 0.001), na.rm = TRUE)
 
     peak.ideal.means <- .calcIdealPeaks(seg, purity, ploidy)
     scatter.labels <- paste0(r$ML.C, "m", r$ML.M)[!r$ML.SOMATIC]
@@ -608,15 +608,15 @@ ss) {
     xc <- .matrixTotalPloidyToTumorPloidy(res$candidates$all)
     xc[is.infinite(xc)] <- min(xc[!is.infinite(xc)], na.rm = TRUE)
     xc[is.na(xc)] <- min(xc[!is.infinite(xc)], na.rm = TRUE)
-    xc[xc < quantile(xc, p = 0.2)] <- quantile(xc, p = 0.2)
+    xc[xc < quantile(xc, p = 0.2)] <- quantile(xc, probs = 0.2)
 
     mycol.image <- colorRampPalette(rev(brewer.pal(n = 7,
         name = "RdYlBu")))(100)
     image(as.numeric(colnames(xc)), as.numeric(rownames(xc)),
         t(xc) - max(xc), col = mycol.image, xlab = "Purity",
         ylab = "Ploidy",main = main,...)
     .legend.col(col = mycol.image, lev = min(xc):max(xc),
-        ylim = quantile(as.numeric(rownames(xc)), p = c(0,1)))
+        ylim = quantile(as.numeric(rownames(xc)), probs = c(0,1)))
 
     if (show.contour) contour(as.numeric(colnames(xc)),
         as.numeric(rownames(xc)), t(xc), add = TRUE)

R/segmentationCBS.R

@@ -303,7 +303,7 @@ segmentationCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
     }
     # a crude way of estimating purity - in heigher purity samples, we
     # can undo a little bit more aggressively
-    q <- quantile(log.ratio, p = c(d, 1 - d))
+    q <- quantile(log.ratio, probs = c(d, 1 - d))
     q.diff <- abs(q[1] - q[2])
     if (q.diff < 0.5) return(0.5 * sd.min.ratio)
     if (q.diff < 1) return(0.75 * sd.min.ratio)
@@ -315,7 +315,7 @@ segmentationCBS <- function(normal, tumor, log.ratio, seg, plot.cnv,
     seg <- seg[seg$num.mark >= 1, ]
     log.ratio <- unlist(lapply(seq_len(nrow(seg)), function(i)
         rep(seg$seg.mean[i], seg$num.mark[i])))
-    q <- quantile(log.ratio, p = c(d, 1 - d))
+    q <- quantile(log.ratio, probs = c(d, 1 - d))
     q.diff <- abs(q[1] - q[2])
     if (q.diff < 0.5) return(0.1)
     if (q.diff < 1) return(0.15)