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annotate scater-plot-dist-scatter.R @ 0:4887c4c69847 draft

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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
author iuc
date Thu, 18 Jul 2019 11:12:33 -0400
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4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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1 #!/usr/bin/env Rscript
4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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3 # Plot the distribution of read counts and feature counts, side by side, then a scatter plot of read counts vs feature counts below
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5 # Load optparse we need to check inputs
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7 library(optparse)
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8 library(workflowscriptscommon)
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9 library(LoomExperiment)
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10 library(scater)
4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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11 library(ggpubr)
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13 # parse options
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15 option_list = list(
4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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16 make_option(
4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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17 c("-i", "--input-loom"),
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18 action = "store",
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19 default = NA,
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20 type = 'character',
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21 help = "A SingleCellExperiment object file in Loom format."
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22 ),
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23 make_option(
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24 c("-o", "--output-plot-file"),
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25 action = "store",
4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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26 default = NA,
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27 type = 'character',
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28 help = "Path of the PDF output file to save plot to."
4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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29 )
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30 )
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32 opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_plot_file'))
4887c4c69847 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/scater commit 5fdcafccb6c645d301db040dfeed693d7b6b4278
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34 # Check parameter values
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36 if ( ! file.exists(opt$input_loom)){
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37 stop((paste('File', opt$input_loom, 'does not exist')))
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38 }
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40 # Input from Loom format
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42 scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment')
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44 #do the scatter plot of reads vs genes
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45 total_counts <- scle$total_counts
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46 total_features <- scle$total_features_by_counts
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47 count_feats <- cbind(total_counts, total_features)
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48 cf_dm <- as.data.frame(count_feats)
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49
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50 # Calculate binwidths for reads and features plots. Use 20 bins
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51 read_bins <- max(total_counts / 1e6) / 20
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52 feat_bins <- max(total_features) / 20
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53
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54 #make the plots
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55 plot <- ggplot(cf_dm, aes(x=total_counts / 1e6, y=total_features)) + geom_point(shape=1) + geom_smooth() + xlab("Read count (millions)") +
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56 ylab("Feature count") + ggtitle("Scatterplot of reads vs features")
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57 plot1 <- qplot(total_counts / 1e6, geom="histogram", binwidth = read_bins, ylab="Number of cells", xlab = "Read counts (millions)", fill=I("darkseagreen3")) + ggtitle("Read counts per cell")
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58 plot2 <- qplot(total_features, geom="histogram", binwidth = feat_bins, ylab="Number of cells", xlab = "Feature counts", fill=I("darkseagreen3")) + ggtitle("Feature counts per cell")
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59 plot3 <- plotColData(scle, y="pct_counts_MT", x="total_features_by_counts") + ggtitle("% MT genes") + geom_point(shape=1) + theme(text = element_text(size=15)) + theme(plot.title = element_text(size=15))
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61 final_plot <- ggarrange(plot1, plot2, plot, plot3, ncol=2, nrow=2)
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62 ggsave(opt$output_plot_file, final_plot, device="pdf")