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Perform statistical analysis on scores using a specified test

scores.stat.analysis.Rd

Unified interface for testing associations between any score matrix and a clinical/experimental trait. Accepts cell group scores, NMF latent factors (output of compute.latent_factors()), or any samples x features matrix.

Usage

scores.stat.analysis(
  scores,
  coldata,
  trait,
  method = c("fisher", "wilcox", "anova", "kruskal", "ttest"),
  pval = 0.05
)

Arguments

scores

A list, NMF output from compute.latent_factors(), or a score matrix (samples x features). When a list, the first element must be the score matrix; optional second and third elements are passed through to the returned result unchanged.

coldata

A data frame containing clinical or experimental metadata for samples. Must include the column specified in trait.

trait

Character. The name of the column in coldata representing the clinical or experimental trait to test against (e.g., response, subtype, etc.).

method

Character. Statistical test to perform. One of:

  • "fisher" - Fisher's exact test (scores binarised at median)

  • "wilcox" - Wilcoxon rank-sum test (non-parametric, binary traits)

  • "anova" - One-way ANOVA (parametric, >2 groups)

  • "kruskal" - Kruskal-Wallis test (non-parametric, >2 groups)

  • "ttest" - Student's t-test (parametric, binary traits)

Defaults to all available options, but only one can be used per call.

pval

Numeric. P-value threshold for significance (default: 0.05).

Value

A list of significant features, where the first element contains the subset of the original score matrix for significant features. Optional second and third elements (from the input list) are subsetted accordingly. Returns NULL if no significant features are found.

Details

The function automatically calls the corresponding statistical test function based on the method argument:

Each test produces both a statistical result and visual outputs (PDF plots) stored in the "Results/" folder. These visualizations include the relevant test results (p-values) annotated on the plots.

Examples

if (FALSE) { # \dontrun{
# Cell group scores
sig <- scores.stat.analysis(cell.groups, coldata, trait = "response",
                            method = "kruskal", pval = 0.05)

# NMF latent factors
nmf <- compute.latent.factors(cell.groups)
sig <- scores.stat.analysis(nmf, coldata, trait = "response", method = "anova")
} # }