Plot method concordance results from TSENATAnalysis

Usage

plot_concordance(analysis, verbose = FALSE)

# S4 method for class 'TSENATAnalysis'
plot_concordance(analysis, verbose = FALSE)

Arguments

analysis: TSENATAnalysis object with computed method concordance (from calculate_concordance()).
verbose: logical. Print progress messages. Default: FALSE

Value

A ggplot/cowplot object showing:

Panel 1: Scatter plot of -log10(p-values) comparing methods
Panel 2: Histogram of p-value distributions by method

Details

Creates visualization of method concordance including: - Comparison of significance across two methods (with color-coded agreement) - P-value distribution histograms for both methods - Significance threshold lines at p < 0.05

Requires that calculate_concordance() has already been run to populate @metadata$method_concordance.

Examples

# Load example data (matching TSENAT.Rmd workflow)
data(readcounts)
readcounts <- as.matrix(readcounts)
mode(readcounts) <- 'numeric'
metadata_df <- read.table(
  system.file('extdata', 'metadata.tsv', package = 'TSENAT'),
  header = TRUE, sep = '\t'
)
gff3_dataset <- system.file('extdata', 'annotation.gff3.gz', package =
'TSENAT')

# Build analysis from vignette data and create small subset
config <- TSENAT_config(sample_col = 'sample', condition_col = 'condition')
analysis <- build_analysis(readcounts = readcounts, tx2gene =
gff3_dataset, metadata = metadata_df, config = config,
  tpm = tpm, effective_length = effective_length)
analysis <- filter_analysis(analysis, min_samples = 1, subset_n_genes
= 200)

# Note: calculate_concordance requires additional LM and Scheirer-Ray-Hare rank test
# results computed. For demo purposes, we show that
# plot_concordance needs pre-computed concordance in @metadata