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Compare method concordance for differential analysis results

Source: R/s4_functions.R
calculate_concordance.Rd

Compares statistical results from two different methods (typically LM/GAM for continuous data and Scheirer-Ray-Hare rank tests) to assess agreement and identify genes detected by one method but not the other.

Usage

calculate_concordance(analysis_lm, analysis_rank = NULL, ...)

# S4 method for class 'TSENATAnalysis'
calculate_concordance(
  analysis_lm,
  analysis_rank = NULL,
  verbose = FALSE,
  output_file = NULL,
  ...
)

# S4 method for class 'TSENATAnalysis'
calculate_concordance(
  analysis_lm,
  analysis_rank = NULL,
  verbose = FALSE,
  output_file = NULL,
  ...
)

Arguments

analysis_lm

TSENATAnalysis object containing LM/GAM analysis results (from calculate_lm()).

analysis_rank

TSENATAnalysis object or NULL. If NULL, uses legacy single-object API with analysis_lm containing both results. If provided, compares LM results from analysis_lm with rank-test results from analysis_rank.

...

Additional arguments for future extensibility.

verbose

logical. Print progress messages (default: FALSE).

output_file

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

Value

Modified TSENATAnalysis object with concordance results stored in: @metadata$method_concordance:

comparison_df

Data frame comparing results from both methods

spearman_rho

Spearman correlation between adjusted p-values

high_confidence

Genes with strong agreement

agreement_table

Contingency table of significant/non-significant calls

lm_method

Method name used for LM/GAM analysis

rank_method

Method name used for rank-based analysis

timestamp

When concordance was computed

Details

Compares results from two different statistical methods (typically GAM for continuous and Scheirer-Ray-Hare for rank-based analysis) on the same data. Identifies: - Genes significant in both methods (high confidence) - Genes detected by one method only (potential false positives or method-specific signal) - Spearman correlation of p-values (overall agreement trends)

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')

# Configure analysis parameters first (fail-fast principle)
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 and metadata as explicit parameter
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))
analysis <- calculate_divergence(analysis, q = c(0.5, 1.0, 1.5, 2.0, 2.5))
analysis <- suppressWarnings(calculate_lm(analysis, method = 'gam'))
# Note: calculate_concordance requires results from both
# calculate_srh and calculate_assumptions