View source: R/shifting_plots.R
plot_shift_count | R Documentation |
Generate a stacked barplot for the number of TSRs with significant upstream and/or downstream shifts for each comparison.
plot_shift_count( experiment, samples = "all", ncol = 3, return_table = FALSE, ... )
experiment |
TSRexploreR object. |
samples |
A vector of sample names to analyze. |
ncol |
Integer specifying the number of columns to arrange multiple plots. |
return_table |
Return a table of results instead of a plot. |
... |
Arguments passed to geom_col. |
The 'tss_shifting' function uses the earth mover's score (EMS) to assess shifts in TSS distribution in consensus TSRs between two samples. This function generates a stacked barplot of the number of upstream and downstream shifts in each comparison.
If 'return_table' is TRUE, a data.frame is returned that provides the underlying counts for each sample.
ggplot2 of stacked barplot of shifted TSS clusters. If 'return_table' is TRUE, a data.frame of underlying counts is returned instead.
tss_shift
For TSS cluster shifting calculation.
data(TSSs) assembly <- system.file("extdata", "S288C_Assembly.fasta", package = "TSRexploreR") samples <- data.frame( sample_name=c(sprintf("S288C_D_%s", seq_len(3)), sprintf("S288C_WT_%s", seq_len(3))), file_1=rep(NA, 6), file_2=rep(NA, 6), condition=c(rep("Diamide", 3), rep("Untreated", 3)) ) exp <- TSSs %>% tsr_explorer(sample_sheet=samples, genome_assembly=assembly) %>% format_counts(data_type="tss") %>% tss_clustering(threshold=3) %>% merge_samples(data_type="tss", merge_group="condition") %>% merge_samples(data_type="tsr", merge_group="condition") %>% tss_shift( sample_1=c(TSS="S288C_WT_1", TSR="S288C_WT_1"), sample_2=c(TSS="S288C_D_1", TSR="S288C_D_1"), comparison_name="Untreated_vs_Diamide", max_distance = 100, min_threshold = 10, n_resamples = 1000L ) p <- plot_shift_count(exp)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.