diff --git a/R/plotter.R b/R/plotter.R index 624df4f..3ce1e5e 100644 --- a/R/plotter.R +++ b/R/plotter.R @@ -34,8 +34,6 @@ if (sex.chrom == "X"){ exclude.chr = c() } -plot.constitutionals <- T - gain.cut <- log2(1 + as.numeric(beta) / 2) del.cut <- log2(1 - as.numeric(beta) / 2) @@ -105,8 +103,8 @@ for (chr in chrs){ h.whis.per.chr = c(h.whis.per.chr, whis[2]) } -chr.wide.upper.limit <- max(0.8, max(h.whis.per.chr)) * 1.25 -chr.wide.lower.limit <- min(-0.8, min(l.whis.per.chr)) * 1.25 +chr.wide.upper.limit <- max(0.65, max(h.whis.per.chr)) * 1.25 +chr.wide.lower.limit <- min(-0.95, min(l.whis.per.chr)) * 1.25 # plot chromosome wide plot @@ -162,11 +160,19 @@ plot(ratio, main = "", axes=F, axis(1, at=mid.chr, labels=labels, tick = F, cex.lab = 3) axis(2, tick = T, cex.lab = 2, col = black, las = 1, tcl=0.5) -genome.len <- chr.end.pos[chrs[length(chrs)] + 1] -segments(0 - genome.len * 0.025, 0, genome.len + genome.len * 0.025, 0, col=orange, lwd = 1, lty = 3) -if (plot.constitutionals){ - segments(0 - genome.len * 0.025, -1, genome.len + genome.len * 0.025, -1, col=red, lwd = 1, lty = 3) - segments(0 - genome.len * 0.025, 0.58, genome.len + genome.len * 0.025, 0.58, col=blue, lwd = 1, lty = 3) +plot.constitutionals <- function(ploidy, start, end){ + segments(start, log2(1/ploidy), end, log2(1/ploidy), col=red, lwd = 2, lty = 3) + segments(start, log2(2/ploidy), end, log2(2/ploidy), col=orange, lwd = 2, lty = 3) + segments(start, log2(3/ploidy), end, log2(3/ploidy), col=blue, lwd = 2, lty = 3) +} + +genome.len <- chr.end.pos[length(chr.end.pos)] +autosome.len <- chr.end.pos[23] +if ((sex.chrom == "X")){ + plot.constitutionals(2, -genome.len * 0.025, genome.len * 1.025) +} else { + plot.constitutionals(2, -genome.len * 0.025, autosome.len) + plot.constitutionals(1, autosome.len, genome.len * 1.025) } for (x in chr.end.pos){ @@ -175,17 +181,15 @@ for (x in chr.end.pos){ par(xpd=TRUE) # Legends -if (plot.constitutionals){ - legend(x=chr.end.pos[length(chr.end.pos)] * 0.2, - y = chr.wide.upper.limit + (abs(chr.wide.upper.limit) + abs(chr.wide.lower.limit)) * 0.15, - legend = c("Constitutional triploid", "Constitutional diploid", "Constitutional monoploid"), - text.col = c(blue, orange, red), cex = 1.3, bty="n", text.font = 1.8, lty = c(3,3,3), - col = c(blue, orange, red)) -} +legend(x=chr.end.pos[length(chr.end.pos)] * 0.2, + y = chr.wide.upper.limit + (abs(chr.wide.upper.limit) + abs(chr.wide.lower.limit)) * 0.15, + legend = c("Constitutional triploid", "Constitutional diploid", "Constitutional monoploid"), + text.col = c(blue, orange, red), cex = 1.3, bty="n", text.font = 1.8, lty = c(3,3,3), lwd = 1.5, + col = c(blue, orange, red)) legend(x=0, y = chr.wide.upper.limit + (abs(chr.wide.upper.limit) + abs(chr.wide.lower.limit)) * 0.15, - legend = c("Gain", "Loss (or monoploid)", paste0("Number of reads: ", nreads)), text.col = c(blue, red, black), + legend = c("Gain", "Loss", paste0("Number of reads: ", nreads)), text.col = c(blue, red, black), cex = 1.3, bty="n", text.font = 1.8, pch = c(16,16), col = c(blue, red, "white")) par(xpd=FALSE) @@ -216,11 +220,7 @@ axis(2, tick = T, cex.lab = 2, col = black, las = 1, tcl=0.5) par(mar = c(4,4,4,0), mgp=c(1,0.5,2)) axis(1, at=1:22, labels=labels[1:22], tick = F, cex.lab = 3) -segments(0, 0, length(chrs[1:22]) + 1, 0, col=orange, lwd = 2, lty = 3) -if (plot.constitutionals){ - segments(0, -1, length(chrs[1:22]) + 1, -1, col=red, lwd = 2, lty = 3) - segments(0, 0.58, length(chrs[1:22]) + 1, 0.58, col=blue, lwd = 2, lty = 3) -} +plot.constitutionals(2, 0, 23) par(mar = c(4,4,4,0), mgp=c(2.2,-0.5,2)) @@ -230,12 +230,13 @@ axis(2, tick = T, cex.lab = 2, col = black, las = 1, tcl=0.5) par(mar = c(4,4,4,0), mgp=c(1,0.5,2)) axis(1, at=1:(length(chrs) - 22), labels=labels[23:length(chrs)], tick = F, cex.lab = 3) -segments(0.6, 0, length(chrs[23:length(chrs)]) + 1, 0, col=orange, lwd = 2, lty = 3) -if (plot.constitutionals){ - segments(0.6, -1, length(chrs[23:length(chrs)]) + 1, -1, col=red, lwd = 2, lty = 3) - segments(0.6, 0.58, length(chrs[23:length(chrs)]) + 1, 0.58, col=blue, lwd = 2, lty = 3) +if ((sex.chrom == "X")){ + plot.constitutionals(2, 0.6, length(chrs[23:length(chrs)]) + 1) +} else { + plot.constitutionals(1, 0.6, length(chrs[23:length(chrs)]) + 1) } + box("outer", lwd = 4) # write image @@ -258,8 +259,8 @@ for (c in chrs){ mean = mean(box.list[[c]], na.rm = T) whis = boxplot(box.list[[c]], plot = F)$stats[c(1,5),] - upper.limit <- 0.8 + whis[2] - lower.limit <- -0.8 + whis[1] + upper.limit <- 0.6 + whis[2] + lower.limit <- -1.05 + whis[1] par(mar = c(4,4,4,0), mgp=c(2.2,-0.2,2)) @@ -297,10 +298,14 @@ for (c in chrs){ axis(1, at=x.labels.at, labels=x.labels, tick = F, cex.axis=0.8) axis(2, tick = T, cex.lab = 2, col = black, las = 1, tcl=0.5) - segments(chr.end.pos[c] - bins.per.chr[c] * 0.02, 0, chr.end.pos[c+1] + bins.per.chr[c] * 0.02, 0, col=orange, lwd = 1, lty = 3) - if (plot.constitutionals){ - segments(chr.end.pos[c] - bins.per.chr[c] * 0.02, -1, chr.end.pos[c+1] + bins.per.chr[c] * 0.02, -1, col=red, lwd = 1, lty = 3) - segments(chr.end.pos[c] - bins.per.chr[c] * 0.02, 0.58, chr.end.pos[c+1] + bins.per.chr[c] * 0.02, 0.58, col=blue, lwd = 1, lty = 3) + if ((sex.chrom == "X")){ + plot.constitutionals(2, chr.end.pos[c] - bins.per.chr[c] * 0.02, chr.end.pos[c+1] + bins.per.chr[c] * 0.02) + } else { + if (c == 23 | c == 24){ + plot.constitutionals(1, chr.end.pos[c] - bins.per.chr[c] * 0.02, chr.end.pos[c+1] + bins.per.chr[c] * 0.02) + } else { + plot.constitutionals(2, chr.end.pos[c] - bins.per.chr[c] * 0.02, chr.end.pos[c+1] + bins.per.chr[c] * 0.02) + } } for (x in chr.end.pos){ diff --git a/README.md b/README.md index 88d502e..549c7a9 100755 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ There are three main stages for using wisecondorX: normalize the X and/or Y chromosome. - When the female reference is given to the [`predict`](#stage-3-predict-cnas) function, chromosome X will be analysed; when on the other hand the male reference is used, chromosomes X & Y are analysed. This regardless of the gender of the test case, - although I would **not** advice to use a female reference and a male test case, or vice versa — this because numerous Y reads + although I would **never** advice to use a female reference and a male test case, or vice versa — this because numerous Y reads wrongly map the X chromosome. Using a matching reference, the latter is accounted for. - For NIPT, exclusively a female reference should be created. This implies that for NIPT, wisecondorX is not able to analyse the Y chromosome. Furthermore, obtaining consistent shifts in the X chromosome is only possible when the reference @@ -80,8 +80,8 @@ There are three main stages for using wisecondorX: `-alpha x` | P-value cut-off for calling a CBS breakpoint (default: x=1e-4) `-beta x` | Number between 0 and 0.5, defines the trade-off between sensitivity and specificity for aberration calling. If e.g. beta=0.1, ratios between 0.95 and 1.05 (1.05 - 0.95 = 0.1) are seen as non-aberrant (default: x=0.05) `-blacklist x` | Blacklist that masks additional regions in output, requires header-less .bed file. This is particularly useful when the reference set is a too small to recognize some obvious regions (such as centromeres; example at `./blacklist/centromere.hg38.txt`) (default: x=None) -`-bed` | Outputs tab-delimited .bed files (healthy male example at `./example.bed`), containing all necessary information **(\*)** -`-plot` | Outputs custom .png plots (healthy male example at `./example.plot`), directly interpretable **(\*)** +`-bed` | Outputs tab-delimited .bed files (trisomy 21 NIPT example at `./example.bed`), containing all necessary information **(\*)** +`-plot` | Outputs custom .png plots (trisomy 21 NIPT example at `./example.plot`), directly interpretable **(\*)** **(\*)** At least one of these output formats should be selected diff --git a/example.bed/ID_1_aberrations.bed b/example.bed/ID_1_aberrations.bed deleted file mode 100644 index cf57e4f..0000000 --- a/example.bed/ID_1_aberrations.bed +++ /dev/null @@ -1,3 +0,0 @@ -chr start end ratio type -X 300001 156000000 -0.998 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-0.0383405331554 +X 154700000 154800000 X:154700001-154800000 0.0233192952303 +X 154800000 154900000 X:154800001-154900000 0.0614400490327 +X 154900000 155000000 X:154900001-155000000 -0.0908042434869 +X 155000000 155100000 X:155000001-155100000 0.0031048394534 +X 155100000 155200000 X:155100001-155200000 0.0500452001909 +X 155200000 155300000 X:155200001-155300000 0.118888861133 +X 155300000 155400000 X:155300001-155400000 -0.0766469436345 +X 155400000 155500000 X:155400001-155500000 0.0548990618125 +X 155500000 155600000 X:155500001-155600000 -0.0909756666637 +X 155600000 155700000 X:155600001-155700000 0.0767152621206 +X 155700000 155800000 X:155700001-155800000 0.0959480976147 +X 155800000 155900000 X:155800001-155900000 -0.0333410939307 +X 155900000 156000000 X:155900001-156000000 0.0298598893832 +X 156000000 156100000 X:156000001-156100000 NaN diff --git a/example.bed/ID_segments.bed b/example.bed/ID_segments.bed new file mode 100644 index 0000000..64e44cd --- /dev/null +++ b/example.bed/ID_segments.bed @@ -0,0 +1,24 @@ +chr start end ratio +1 1500001 248900000 -0.0011 +2 1 241800000 -0.0036 +3 1 198100000 -0.0021 +4 100001 190000000 -0.0051 +5 100001 181300000 -0.0009 +6 100001 170600000 0.002 +7 300001 159300000 -0.001 +8 200001 145100000 -0.0029 +9 400001 138100000 -0.0029 +10 1 133400000 -0.004 +11 200001 135100000 0.0003 +12 100001 133200000 0.0012 +13 18500001 114300000 -0.0064 +14 18200001 106900000 -0.0001 +15 19800001 101900000 0.0001 +16 100001 90100000 -0.0036 +17 500001 82900000 0.0007 +18 100001 80300000 -0.0096 +19 200001 58600000 0.0123 +20 100001 64300000 -0.0047 +21 8200001 46700000 0.1037 +22 11200001 50800000 -0.0103 +X 1700001 156000000 -0.0066 diff --git a/example.bed/ID_statistics.txt b/example.bed/ID_statistics.txt new file mode 100644 index 0000000..77f17c2 --- /dev/null +++ b/example.bed/ID_statistics.txt @@ -0,0 +1,26 @@ +chr ratio.mean ratio.median zscore.mean zscore.median +1 -0.00114312634001 0.00188624494016 0.050249478493 0.0837803050784 +2 -0.00364460099022 -0.00279423273723 0.0100482822043 0.00376508523415 +3 -0.00213035049277 -0.00239334926369 0.0358141629064 0.0103449634051 +4 -0.00507167847621 -0.00321883539017 -0.01293423965 -0.00325053147493 +5 -0.000937904951329 -0.000247364882766 0.0548240709891 0.0459646142417 +6 0.00199718285359 0.00423380737716 0.106139844286 0.123349686302 +7 -0.000967108282834 -0.00141477287777 0.0543840769139 0.0272449551047 +8 -0.00285340574585 -0.000854472123527 0.023235984367 0.0367223786249 +9 -0.0029482166278 -0.00258851541524 0.020011622676 0.00712724508525 +10 -0.00403775006407 -0.00382352283322 0.00375504132292 -0.0130543537627 +11 0.000272491061894 0.0041801245504 0.075440495545 0.118735035891 +12 0.00120490103263 0.00350517616301 0.0903147823954 0.108750651157 +13 -0.00638409255242 -0.00743016886552 -0.0319462746074 -0.0740449870406 +14 -0.000106759405938 0.00146924739817 0.0694941954146 0.0751193412643 +15 9.88711415468e-05 0.00130686111246 0.0759445354096 0.0740022693106 +16 -0.00359749401839 -0.00495054955024 0.0100213824365 -0.0320036081632 +17 0.000679786764808 0.00230019127219 0.0819534555574 0.0901793241695 +18 -0.00958845523227 -0.00860972192097 -0.0910030115464 -0.092206664057 +19 0.0122807094891 0.0118274130538 0.273612297482 0.251102539315 +20 -0.00465048671036 -0.00134865893338 -0.0028542356715 0.0284663778861 +21 0.103666380347 0.101954616614 1.88982003046 1.8363835519 +22 -0.0102809168332 -0.0139214449745 -0.0953439026552 -0.176573195734 +X -0.00664702119287 -0.00538855623359 -0.0400257300099 -0.0391997073146 +Standard deviation of mean chromosomal ratios: 0.0220354915498 +Median of all within-segment ratio variances: 0.00377949476555 diff --git a/example.plot/chr1.png b/example.plot/chr1.png index d8257de..237e7ad 100644 Binary files a/example.plot/chr1.png and b/example.plot/chr1.png differ diff --git a/example.plot/chr10.png b/example.plot/chr10.png index 4e06535..e037c85 100644 Binary files a/example.plot/chr10.png and 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+1,3 @@ -# Copyright (C) 2016 VU University Medical Center Amsterdam -# Author: Roy Straver (github.com/rstraver) -# -# This file is part of WISECONDOR -# WISECONDOR is distributed under the following license: -# Attribution-NonCommercial-ShareAlike, CC BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) -# This license is governed by Dutch law and this license is subject to the exclusive jurisdiction of the courts of the Netherlands. - import numpy as np class TriArr: diff --git a/python/wisetools.py b/python/wisetools.py index 3888486..03a48c5 100755 --- a/python/wisetools.py +++ b/python/wisetools.py @@ -1,11 +1,3 @@ -# Copyright (C) 2016 VU University Medical Center Amsterdam -# Author: Roy Straver (github.com/rstraver) -# -# This file is part of WISECONDOR -# WISECONDOR is distributed under the following license: -# Attribution-NonCommercial-ShareAlike, CC BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) -# This license is governed by Dutch law and this license is subject to the exclusive jurisdiction of the courts of the Netherlands. - import time import bisect from sklearn.decomposition import PCA @@ -354,12 +346,6 @@ def getPart(partnum, outof, bincount): endBin = int(bincount / float(outof) * (partnum + 1)) return startBin, endBin -def normalizeRefMale(sample): - sexChrs = ["23", "24"] - for sexChr in sexChrs: - sample[sexChr] = sample[sexChr] * 2 - return sample - def getReference(correctedData, chromosomeBins, chromosomeBinSums, gender, selectRefAmount=100, part=1, splitParts=1): timeStartSelection = time.time() bigIndexes = [] diff --git a/wisecondorX.py b/wisecondorX.py index 70e3edb..13bd76a 100755 --- a/wisecondorX.py +++ b/wisecondorX.py @@ -1,12 +1,3 @@ -#!/usr/bin/env python -# Copyright (C) 2016 VU University Medical Center Amsterdam -# Author: Roy Straver (github.com/rstraver) -# -# This file is part of WISECONDOR -# WISECONDOR is distributed under the following license: -# Attribution-NonCommercial-ShareAlike, CC BY-NC-SA (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) -# This license is governed by Dutch law and this license is subject to the exclusive jurisdiction of the courts of the Netherlands. - import argparse import sys from scipy.stats import norm @@ -75,8 +66,6 @@ def toolNewrefPrep(args): print 'Loading:', infile, npzdata = np.load(infile) sample = npzdata['sample'].item() - if args.gender == "M": - sample = normalizeRefMale(sample) print ' \tbinsize:', int(npzdata['arguments'].item()['binsize']) samples.append(scaleSample(sample, npzdata['arguments'].item()['binsize'], args.binsize)) nreads.append(sum([sum(sample[x]) for x in sample.keys()])) @@ -229,7 +218,6 @@ def toolTest(args): num_tests = sum(masked_sizes) z_threshold = norm.ppf(1 - 1. / (num_tests * 0.1)) - print 'Per bin z-score threshold for first testing cycles:', z_threshold testCopy = np.copy(testData) @@ -248,13 +236,21 @@ def toolTest(args): resultsZ = [] resultsR = [] inflatedZ = inflateArrayMulti(cleanedZ, mask_list) - inflatedR = inflateArrayMulti(np.log2(cleanedR) - np.median(np.log2(cleanedR)), mask_list) + inflatedR = inflateArrayMulti(cleanedR, mask_list) for chrom in xrange(len(chromosome_sizes)): chromData = inflatedZ[sum(chromosome_sizes[:chrom]):sum(chromosome_sizes[:chrom + 1])] resultsZ.append(chromData) chromData = inflatedR[sum(chromosome_sizes[:chrom]):sum(chromosome_sizes[:chrom + 1])] resultsR.append(chromData) + # log2 & median centering + autosomesR = [] + for chrom in xrange(22): + autosomesR.extend(resultsR[chrom]) + mR = np.median(np.log2([x for x in autosomesR if x != 0])) + for chrom in xrange(len(chromosome_sizes)): + resultsR[chrom] = np.log2(resultsR[chrom]) - mR + # Apply blacklist if args.blacklist != None: applyBlacklist(args, binsize, resultsR, resultsZ, sample, gender) @@ -332,9 +328,9 @@ def get_gender(args): Y = float(np.sum(sample["24"])) permilleY = Y / (nonY + Y) * 1000.0 if permilleY > args.cutoff: - print("Gender = M") + print("Male") else: - print("Gender = F") + print("Female") exit(1)