Mercurial > repos > iuc > raceid_inspecttrajectory

annotate scripts/cluster.R @ 2:5156383d3a5d draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit 1d6e79ba92ce98c7c91f0c4076c9ca5e4e3f3a20
author iuc
date Thu, 28 Feb 2019 17:38:32 -0500
parents 7967b3d036d1
children 4164c0da0a5d
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1 #!/usr/bin/env R
1
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2 VERSION = "0.3"
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4 args = commandArgs(trailingOnly = T)
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6 if (length(args) != 1){
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7 message(paste("VERSION:", VERSION))
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8 stop("Please provide the config file")
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9 }
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11 suppressWarnings(suppressPackageStartupMessages(require(RaceID)))
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12 suppressWarnings(suppressPackageStartupMessages(require(scran)))
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13 source(args[1])
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16 do.filter <- function(sc){
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17 if (!is.null(filt.lbatch.regexes)){
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18 lar <- filt.lbatch.regexes
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19 nn <- colnames(sc@expdata)
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20 filt$LBatch <- lapply(1:length(lar), function(m){ return( nn[grep(lar[[m]], nn)] ) })
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21 }
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23 sc <- do.call(filterdata, c(sc, filt))
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25 ## Get histogram metrics for library size and number of features
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26 raw.lib <- log10(colSums(as.matrix(sc@expdata)))
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27 raw.feat <- log10(colSums(as.matrix(sc@expdata)>0))
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28 filt.lib <- log10(colSums(getfdata(sc)))
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29 filt.feat <- log10(colSums(getfdata(sc)>0))
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31 br <- 50
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32 ## Determine limits on plots based on the unfiltered data
e0e9b24d76aa planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit f880060c478d42202df5b78a81329f8af56b1138
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33 ## (doesn't work, R rejects limits and norm data is too different to compare to exp data
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34 ## so let them keep their own ranges)
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36 ## betterrange <- function(floatval){
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37 ## return(10 * (floor(floatval / 10) + 1))
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38 ## }
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40 ## tmp.lib <- hist(raw.lib, breaks=br, plot=F)
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41 ## tmp.feat <- hist(raw.feat, breaks=br, plot=F)
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43 ## lib.y_lim <- c(0,betterrange(max(tmp.lib$counts)))
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44 ## lib.x_lim <- c(0,betterrange(max(tmp.lib$breaks)))
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46 ## feat.y_lim <- c(0,betterrange(max(tmp.feat$counts)))
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47 ## feat.x_lim <- c(0,betterrange(max(tmp.feat$breaks)))
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49 par(mfrow=c(2,2))
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50 print(hist(raw.lib, breaks=br, main="RawData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)
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51 print(hist(raw.feat, breaks=br, main="RawData Log10 NumFeat")) #, xlim=feat.x_lim, ylim=feat.y_lim)
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52 print(hist(filt.lib, breaks=br, main="FiltData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)
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53 tmp <- hist(filt.feat, breaks=br, main="FiltData Log10 NumFeat") # , xlim=feat.x_lim, ylim=feat.y_lim)
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54 print(tmp)
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55 ## required, for extracting midpoint
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56 unq <- unique(filt.feat)
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57 if (length(unq) == 1){
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58 abline(v=unq, col="red", lw=2)
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59 text(tmp$mids, table(filt.feat)[[1]] - 100, pos=1, paste(10^unq, "\nFeatures\nin remaining\nCells", sep=""), cex=0.8)
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60 }
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61
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62 if (filt.use.ccorrect){
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63 par(mfrow=c(2,2))
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64 sc <- do.call(CCcorrect, c(sc, filt.ccc))
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65 print(plotdimsat(sc, change=T))
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66 print(plotdimsat(sc, change=F))
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67 }
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68 return(sc)
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69 }
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71 do.cluster <- function(sc){
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72 sc <- do.call(compdist, c(sc, clust.compdist))
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73 sc <- do.call(clustexp, c(sc, clust.clustexp))
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74 if (clust.clustexp$sat){
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75 print(plotsaturation(sc, disp=F))
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76 print(plotsaturation(sc, disp=T))
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77 }
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78 print(plotjaccard(sc))
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79 return(sc)
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80 }
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82 do.outlier <- function(sc){
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83 sc <- do.call(findoutliers, c(sc, outlier.findoutliers))
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84 if (outlier.use.randomforest){
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85 sc <- do.call(rfcorrect, c(sc, outlier.rfcorrect))
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86 }
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87 print(plotbackground(sc))
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88 print(plotsensitivity(sc))
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89 print(plotoutlierprobs(sc))
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90 ## Heatmaps
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91 test1 <- list()
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92 test1$side = 3
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93 test1$line = 0 #1 #3
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94
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95 x <- clustheatmap(sc, final=FALSE)
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96 print(do.call(mtext, c(paste("(Initial)"), test1))) ## spacing is a hack
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97 x <- clustheatmap(sc, final=TRUE)
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98 print(do.call(mtext, c(paste("(Final)"), test1))) ## spacing is a hack
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99 return(sc)
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100 }
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101
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102 do.clustmap <- function(sc){
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103 sc <- do.call(comptsne, c(sc, cluster.comptsne))
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104 sc <- do.call(compfr, c(sc, cluster.compfr))
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105 return(sc)
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106 }
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107
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108
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109 mkgenelist <- function(sc){
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110 ## Layout
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111 test <- list()
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112 test$side = 3
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113 test$line = 0 #1 #3
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114 test$cex = 0.8
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115
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116 df <- c()
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117 options(cex = 1)
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118 lapply(unique(sc@cpart), function(n){
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119 dg <- clustdiffgenes(sc, cl=n, pvalue=genelist.pvalue)
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120
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121 dg.goi <- dg[dg$fc > genelist.foldchange,]
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122 dg.goi.table <- head(dg.goi, genelist.tablelim)
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123 df <<- rbind(df, cbind(n, dg.goi.table))
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124
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125 goi <- head(rownames(dg.goi.table), genelist.plotlim)
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126 print(plotmarkergenes(sc, goi))
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127 print(do.call(mtext, c(paste(" Cluster ",n), test))) ## spacing is a hack
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128 test$line=-1
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129 print(do.call(mtext, c(paste(" Sig. Genes"), test))) ## spacing is a hack
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130 test$line=-2
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131 print(do.call(mtext, c(paste(" (fc > ", genelist.foldchange,")"), test))) ## spacing is a hack
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132
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133 })
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134 write.table(df, file=out.genelist, sep="\t", quote=F)
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135 }
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136
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137 pdf(out.pdf)
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138
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139 if (use.filtnormconf){
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140 sc <- do.filter(sc)
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141 message(paste(" - Source:: genes:",nrow(sc@expdata),", cells:",ncol(sc@expdata)))
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142 message(paste(" - Filter:: genes:",nrow(sc@ndata),", cells:",ncol(sc@ndata)))
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143 message(paste(" :: ",
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144 sprintf("%.1f", 100 * nrow(sc@ndata)/nrow(sc@expdata)), "% of genes remain,",
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145 sprintf("%.1f", 100 * ncol(sc@ndata)/ncol(sc@expdata)), "% of cells remain"))
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146 }
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147
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148 if (use.cluster){
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149 par(mfrow=c(2,2))
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150 sc <- do.cluster(sc)
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151
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152 par(mfrow=c(2,2))
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153 sc <- do.outlier(sc)
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154
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155 par(mfrow=c(2,2), mar=c(1,1,6,1))
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156 sc <- do.clustmap(sc)
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157
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158 mkgenelist(sc)
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159 }
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160
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161 dev.off()
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162
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163 saveRDS(sc, out.rdat)