--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scater-plot-dist-scatter.R	Tue Sep 03 14:30:21 2019 -0400
@@ -0,0 +1,79 @@
+#!/usr/bin/env Rscript
+
+# Plot the distribution of read counts and feature counts, side by side, then a scatter plot of read counts vs feature counts below
+
+# Load optparse we need to check inputs
+
+library(optparse)
+library(workflowscriptscommon)
+library(LoomExperiment)
+library(scater)
+library(ggpubr)
+library(scales)
+
+# parse options
+
+option_list = list(
+  make_option(
+    c("-i", "--input-loom"),
+    action = "store",
+    default = NA,
+    type = 'character',
+    help = "A SingleCellExperiment object file in Loom format."
+  ),
+  make_option(
+    c("-o", "--output-plot-file"),
+    action = "store",
+    default = NA,
+    type = 'character',
+    help = "Path of the PDF output file to save plot to."
+  ),
+  make_option(
+    c("-l", "--log-scale"),
+    action="store_true",
+    default=FALSE,
+    type = 'logical',
+    help = "Plot on log scale (recommended for large datasets)."
+  )
+)
+
+opt <- wsc_parse_args(option_list, mandatory = c('input_loom', 'output_plot_file', 'log_scale'))
+
+# Check parameter values
+
+if ( ! file.exists(opt$input_loom)){
+  stop((paste('File', opt$input_loom, 'does not exist')))
+}
+
+# Input from Loom format
+
+scle <- import(opt$input_loom, format='loom', type='SingleCellLoomExperiment')
+
+#do the scatter plot of reads vs genes
+total_counts <- scle$total_counts
+total_features <- scle$total_features_by_counts
+count_feats <- cbind(total_counts, total_features)
+cf_dm <- as.data.frame(count_feats)
+
+# Calculate binwidths for reads and features plots. Use 20 bins
+read_bins <- max(total_counts / 1e6) / 20
+feat_bins <- max(total_features) / 20
+
+# Make the plots
+plot <- ggplot(cf_dm, aes(x=total_counts / 1e6, y=total_features)) + geom_point(shape=1) + geom_smooth() + xlab("Read count (millions)") +
+   ylab("Feature count") + ggtitle("Scatterplot of reads vs features")
+plot1 <- qplot(total_counts / 1e6, geom="histogram", binwidth = read_bins, ylab="Number of cells", xlab = "Read counts (millions)", fill=I("darkseagreen3")) + ggtitle("Read counts per cell")
+plot2 <- qplot(total_features, geom="histogram", binwidth = feat_bins, ylab="Number of cells", xlab = "Feature counts", fill=I("darkseagreen3")) + ggtitle("Feature counts per cell")
+plot3 <- plotColData(scle, y="pct_counts_MT", x="total_features_by_counts") + ggtitle("% MT genes") + geom_point(shape=1) + theme(text = element_text(size=15)) + theme(plot.title = element_text(size=15))
+
+if (! opt$log_scale){
+  final_plot <- ggarrange(plot1, plot2, plot, plot3, ncol=2, nrow=2)
+  ggsave(opt$output_plot_file, final_plot, device="pdf")
+} else {
+  plot_log_both <- plot + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')
+  plot1_log <- plot1 + scale_y_continuous(trans = 'log10')
+  plot2_log <- plot2 + scale_y_continuous(trans = 'log10')
+  plot3_log <- plot3 + scale_y_log10(labels=number)
+  final_plot_log <- ggarrange(plot1_log, plot2_log, plot_log_both, plot3_log, ncol=2, nrow=2)
+  ggsave(opt$output_plot_file, final_plot_log, device="pdf")
+}