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Compute Effect Sizes from Divergence Results (S4 Wrapper)

Source: R/s4_functions_effect_size.R
calculate_effect_sizes.Rd

S4 wrapper for . calculate_effect_sizes() that extracts divergence and LM results directly from a TSENATAnalysis object.

Usage

calculate_effect_sizes(
  analysis,
  significance_threshold = NULL,
  enrich_per_q_pattern = NULL,
  verbose = NULL,
  output_file = NULL,
  ...
)

Arguments

analysis

TSENATAnalysis. An S4 object containing divergence results (from calculate_divergence()) and SAIT interaction results (from calculate_sait()).

significance_threshold

numeric. Adjusted p-value threshold for filtering significant genes (default: 0.05).

enrich_per_q_pattern

logical. If TRUE, enriches results with per-q divergence patterns (default: TRUE).

verbose

logical. If TRUE, print diagnostic messages (default: TRUE).

output_file

character or NULL. Optional file path to save results. Supported formats: .rds (for S4 objects). Default: NULL (no file output).

...

Additional arguments passed to the base function.

Value

Modified TSENATAnalysis with effect size results stored via metadata(analysis)$effect_sizes_divergence. Returns the analysis object visibly to support piping and method chaining.

Details

**Workflow steps:**

Validating

Input analysis object and required results

Extracting

Divergence SE and SAIT results from analysis slots

Enriching

Divergence SE with gene names via tx2gene or direct mapping

Computing

Effect sizes using base .calculate_effect_sizes()

Storing

Results in metadata with function call tracking

**Parameter resolution priority** (explicit > config > default):

  • significance_threshold: Uses explicit arg, else analysis@config$significance_threshold, else 0.05

  • enrich_per_q_pattern: Uses explicit arg, else analysis@config$enrich_per_q_pattern, else TRUE

  • verbose: Uses explicit arg, else analysis@config$verbose, else TRUE

Results are accessed via: metadata(analysis)$effect_sizes_divergence

See also

calculate_divergence for divergence wrapper, calculate_sait for SAIT interaction wrapper

Examples

# Setup: Create test analysis with divergence and SAIT interaction results
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 (best practice for reproducibility)
if (FALSE) { # \dontrun{
config <- TSENAT_config(
  sample_col = 'sample',
  condition_col = 'condition',
  subject_col = 'paired_samples',
  paired = TRUE,
  control = 'normal',
  q = seq(0, 2, by = 0.5)  # Multiple q-values for SAIT (5 unique values)
)
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)
analysis <- calculate_divergence(analysis)
analysis <- suppressWarnings(calculate_sait(analysis, method = 'gam'))

# Compute effect sizes from divergence results
analysis <- calculate_effect_sizes(analysis,
  significance_threshold = 0.05)

# Access results using unified results accessor
effect_size_results <- results(analysis, type = 'effect_sizes_divergence')

# View structure of results
str(effect_size_results, max.level = 1)
} # }