Test statistical assumptions on diversity data in TSENATAnalysis

Usage

calculate_assumptions(
  analysis,
  q = NULL,
  checks = "rank",
  alpha = 0.05,
  format = "text",
  ...
)

# S4 method for class 'TSENATAnalysis'
calculate_assumptions(
  analysis,
  q = NULL,
  checks = "rank",
  alpha = 0.05,
  format = "text",
  ...
)

Arguments

analysis: TSENATAnalysis object with diversity results stored in @diversity_results.
q: numeric. Q-value(s) to extract from diversity results. If NULL, uses the first available diversity result or q=1.0.
checks: character. Which assumptions to test (default: 'rank'). Presets: - 'rank': core assumption checks (exchangeability, monotonicity, consistency) - 'all': all checks including GAM diagnostics Explicit: character vector like c('exchangeability', 'monotonicity').
alpha: numeric. Significance level for tests (default: 0.05).
format: character. Output format when used with results(). 'text' (default): formatted text output for display 'list': returns structured list for programmatic access.
...: Additional arguments (output_file, verbose for file output).

Value

Modified TSENATAnalysis object with assumption test results stored in @metadata$rankbased_assumptions.

Details

This wrapper calls .calculate_assumptions() on diversity data extracted from the analysis object. Evaluates data stability and consistency across dimensions. Results include:

exchangeability: Permutation test for independence and temporal/spatial structure
monotonicity: Spearman correlation consistency across rows
consistency: Kendall's W concordance and ICC across samples

**Data Extraction Priority:** 1. If q specified: uses diversity result for that q-value 2. If q NULL: uses first available diversity result 3. If no diversity results: extracts from cached combined result (@metadata$diversity_combined)

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',
  q_values = seq(0, 2, by = 0.05),
  paired = FALSE
)
analysis <- build_analysis(
  readcounts = readcounts,
  metadata = metadata_df,
  tx2gene = gff3_dataset,
  config = config,
  tpm = tpm,
  effective_length = effective_length
)
analysis <- filter_analysis(analysis, min_samples = 1, subset_n_genes = 200)
analysis <- calculate_diversity(analysis, q = c(0.5, 1.0, 1.5))
analysis <- calculate_assumptions(analysis, q = 1.0)
# Check results using rank_test accessor
results_df <- results(analysis, type = 'rank_test')