Mercurial > repos > iuc > raceid_filtnormconf

annotate scripts/cluster.R @ 3:d55e29ac02e3 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/raceid3 commit d94b3b8a4c7cf8c604279eb1eea24d32b3868922
author iuc
date Mon, 15 Apr 2019 17:55:17 -0400
parents 56a093c2a3f9
children 5d5b14dbd092
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1 #!/usr/bin/env R
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2 VERSION = "0.4"
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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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30
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31 if (filt.geqone){
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32 filt.feat <- log10(colSums(getfdata(sc)>=1))
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33 }
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35 br <- 50
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36 ## Determine limits on plots based on the unfiltered data
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37 ## (doesn't work, R rejects limits and norm data is too different to compare to exp data
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38 ## so let them keep their own ranges)
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39
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40 ## betterrange <- function(floatval){
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41 ## return(10 * (floor(floatval / 10) + 1))
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42 ## }
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44 ## tmp.lib <- hist(raw.lib, breaks=br, plot=F)
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45 ## tmp.feat <- hist(raw.feat, breaks=br, plot=F)
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47 ## lib.y_lim <- c(0,betterrange(max(tmp.lib$counts)))
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48 ## lib.x_lim <- c(0,betterrange(max(tmp.lib$breaks)))
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49
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50 ## feat.y_lim <- c(0,betterrange(max(tmp.feat$counts)))
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51 ## feat.x_lim <- c(0,betterrange(max(tmp.feat$breaks)))
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52
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53 par(mfrow=c(2,2))
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54 print(hist(raw.lib, breaks=br, main="RawData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)
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55 print(hist(raw.feat, breaks=br, main="RawData Log10 NumFeat")) #, xlim=feat.x_lim, ylim=feat.y_lim)
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56 print(hist(filt.lib, breaks=br, main="FiltData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)
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57 tmp <- hist(filt.feat, breaks=br, main="FiltData Log10 NumFeat") # , xlim=feat.x_lim, ylim=feat.y_lim)
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58 print(tmp)
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59 ## required, for extracting midpoint
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60 unq <- unique(filt.feat)
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61 if (length(unq) == 1){
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62 abline(v=unq, col="red", lw=2)
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63 text(tmp$mids, table(filt.feat)[[1]] - 100, pos=1, paste(10^unq, "\nFeatures\nin remaining\nCells", sep=""), cex=0.8)
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64 }
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65
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66 if (filt.use.ccorrect){
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67 par(mfrow=c(2,2))
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68 sc <- do.call(CCcorrect, c(sc, filt.ccc))
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69 print(plotdimsat(sc, change=T))
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70 print(plotdimsat(sc, change=F))
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71 }
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72 return(sc)
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73 }
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75 do.cluster <- function(sc){
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76 sc <- do.call(compdist, c(sc, clust.compdist))
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77 sc <- do.call(clustexp, c(sc, clust.clustexp))
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78 if (clust.clustexp$sat){
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79 print(plotsaturation(sc, disp=F))
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80 print(plotsaturation(sc, disp=T))
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81 }
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82 print(plotjaccard(sc))
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83 return(sc)
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84 }
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85
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86 do.outlier <- function(sc){
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87 sc <- do.call(findoutliers, c(sc, outlier.findoutliers))
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88 if (outlier.use.randomforest){
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89 sc <- do.call(rfcorrect, c(sc, outlier.rfcorrect))
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90 }
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91 print(plotbackground(sc))
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92 print(plotsensitivity(sc))
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93 print(plotoutlierprobs(sc))
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94 ## Heatmaps
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95 test1 <- list()
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96 test1$side = 3
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97 test1$line = 0 #1 #3
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98
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99 x <- clustheatmap(sc, final=FALSE)
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100 print(do.call(mtext, c(paste("(Initial)"), test1))) ## spacing is a hack
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101 x <- clustheatmap(sc, final=TRUE)
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102 print(do.call(mtext, c(paste("(Final)"), test1))) ## spacing is a hack
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103 return(sc)
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104 }
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105
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106 do.clustmap <- function(sc){
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107 sc <- do.call(comptsne, c(sc, cluster.comptsne))
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108 sc <- do.call(compfr, c(sc, cluster.compfr))
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109 return(sc)
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110 }
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111
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112
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113 mkgenelist <- function(sc){
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114 ## Layout
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115 test <- list()
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116 test$side = 3
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117 test$line = 0 #1 #3
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118 test$cex = 0.8
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119
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120 df <- c()
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121 options(cex = 1)
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122 lapply(unique(sc@cpart), function(n){
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123 dg <- clustdiffgenes(sc, cl=n, pvalue=genelist.pvalue)
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124
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125 dg.goi <- dg[dg$fc > genelist.foldchange,]
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126 dg.goi.table <- head(dg.goi, genelist.tablelim)
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127 df <<- rbind(df, cbind(n, dg.goi.table))
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128
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129 goi <- head(rownames(dg.goi.table), genelist.plotlim)
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130 print(plotmarkergenes(sc, goi))
3
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131 buffer <- paste(rep("", 36), collapse=" ")
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132 print(do.call(mtext, c(paste(buffer, "Cluster ",n), test))) ## spacing is a hack
0
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133 test$line=-1
3
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134 print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test))) ## spacing is a hack
0
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135 test$line=-2
3
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136 print(do.call(mtext, c(paste(buffer, "(fc > ", genelist.foldchange,")"), test))) ## spacing is a hack
0
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137
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138 })
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139 write.table(df, file=out.genelist, sep="\t", quote=F)
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140 }
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141
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142 pdf(out.pdf)
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143
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144 if (use.filtnormconf){
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145 sc <- do.filter(sc)
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146 message(paste(" - Source:: genes:",nrow(sc@expdata),", cells:",ncol(sc@expdata)))
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147 message(paste(" - Filter:: genes:",nrow(getfdata(sc)),", cells:",ncol(getfdata(sc))))
0
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148 message(paste(" :: ",
3
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149 sprintf("%.1f", 100 * nrow(getfdata(sc))/nrow(sc@expdata)), "% of genes remain,",
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150 sprintf("%.1f", 100 * ncol(getfdata(sc))/ncol(sc@expdata)), "% of cells remain"))
0
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151 }
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152
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153 if (use.cluster){
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154 par(mfrow=c(2,2))
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155 sc <- do.cluster(sc)
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156
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157 par(mfrow=c(2,2))
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158 sc <- do.outlier(sc)
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159
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160 par(mfrow=c(2,2), mar=c(1,1,6,1))
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161 sc <- do.clustmap(sc)
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162
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163 mkgenelist(sc)
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164 }
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165
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166 dev.off()
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167
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168 saveRDS(sc, out.rdat)