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Plot Multi-Q Delta Influence Heatmaps from TSENATAnalysis Object

Source: R/s4_functions.R
plot_jis_delta.Rd

S4 wrapper for . plot_jis_delta() that extracts results directly from a TSENATAnalysis object. Automatically retrieves jackknife switching results from the analysis object slots.

Usage

plot_jis_delta(
  analysis,
  n_genes = 4,
  lm_results = NULL,
  verbose = FALSE,
  output_file = NULL,
  ...
)

Arguments

analysis

TSENATAnalysis. An S4 object containing completed jackknife isoform switching analysis across multiple q-values.

n_genes

numeric. Number of top genes to display in heatmaps (default: 4). Genes are ranked by LM p-values if available, otherwise by order of appearance in results.

lm_results

data.frame or NULL. Optional LM interaction results for ranking genes (default: NULL). If NULL, attempts to extract from analysis@lm_results$lm_interaction.

verbose

logical. If TRUE, print diagnostic messages during plot generation (default: FALSE).

output_file

character or NULL. Optional file path to save the plot. Default: NULL (no file output).

...

Additional arguments passed to the base function.

Value

A file path (character) to the saved heatmap PNG file, invisibly.

Details

This function extracts the following from analysis:

Jackknife results

From analysis@jackknife_results, which should contain multi-q switching results keyed by q-value (e.g., 'q_1.00')

LM results

From analysis@lm_results$lm_interaction if not explicitly provided, for ranking genes by significance

The wrapper automatically handles parameter extraction and provides a simplified interface compared to the base function.

See also

calculate_jis for computing switching results

Examples

# Plot 5: Multi-q delta influence (isoform switching) heatmaps
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')

# Configure analysis parameters first
config <- TSENAT_config(
  sample_col = 'sample',
  condition_col = 'condition',
  subject_col = 'paired_samples',
  paired = TRUE,
  control = 'normal',
  q = seq(0, 2, by = 0.1)
)

# Build analysis with configured parameters
analysis <- build_analysis(
  readcounts = readcounts,
  tx2gene = gff3_dataset,
  metadata = metadata_df,
  config = config,
  tpm = tpm,
  effective_length = effective_length
)

analysis <- filter_analysis(analysis, stringency = 'severe')
analysis <- calculate_diversity(
  analysis,
  q = c(0.5, 1.0, 1.5, 2.0, 2.5),
  verbose = FALSE
)
analysis <- calculate_divergence(
  analysis,
  q = c(0.5, 1.0, 1.5, 2.0, 2.5)
)
analysis <- suppressWarnings(calculate_lm(analysis, method = 'gam'))
analysis <- calculate_jis(
  analysis,
  q = c(0.5, 1, 1.5),
  n_bootstrap = 50
)
heatmap_file <- plot_jis_delta(analysis, n_genes
= 2)