Plot a correlation heatmap from a RiboClass object — plot_heatmap

Shows the correlation distance between samples.

Usage

plot_heatmap_annotated(ribo = ribo, qcdata = qcdata)

Arguments

ribo: A RiboClass object.
qcdata: a dataframe with metadata and outliers information

Value

ComplexHeatmap object

Examples

data("ribo_toy")
qcdata <- ribo_toy[[2]]
ribo_matrix <- extract_data(ribo_toy, "count", position_to_rownames = TRUE)
medcov <- apply(ribo_matrix,2,function(x) median(x,na.rm=TRUE)) 
qcdata <- cbind(qcdata,median_coverage=medcov[rownames(qcdata)]) 
qcdata$coverage_quality <- "pass" 
qcdata$coverage_quality[qcdata$median_coverage < 100] <- "warning" 
plot_rlc <- rRMSAnalyzer::plot_rlc(ribo_toy,show_outlier = TRUE)
mad <- plot_rlc$plot_env$mad  
rlc_median <- as.vector(plot_rlc$plot_env$rlc_grouped$median)
keys <- as.vector(plot_rlc$plot_env$rlc_grouped$key)
outlier_table <- data.frame(key = keys, rlc_median = rlc_median)
outlier <- data.frame()
for (i in 1:nrow(outlier_table)) {
  if(outlier_table$rlc_median[i] < mad) {
    outlier <- rbind(outlier, outlier_table[i,])}}
qcdata$rlc_median <- outlier_table$rlc_median[match(qcdata$samplename, outlier_table$key)]
qcdata$rlc_median_quality <- "pass" 
qcdata$rlc_median_quality[qcdata$rlc_median < mad] <- "warning" 
qcdata$total_outliers <- rowSums(qcdata[, c("coverage_quality", "rlc_median_quality")] == "warning")
ribo_toy$metadata$outlier_level <- qcdata$total_outliers
qcdata$total_outliers <- as.character(qcdata$total_outliers)
plot_heatmap_annotated(ribo_toy, qcdata)