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Run multivariate feature-based GSEA using limma and Hallmark gene sets

compute_factor_gsea.Rd

This function fits a multivariate linear model for each gene using all features in features_df as continuous covariates. For each feature, it extracts the moderated t-statistics and p-values, ranks genes, and performs GSEA using the Hallmark gene sets from MSigDB. Optional dotplots for the top enriched pathways can be saved as PDFs.

Usage

compute_factor_gsea(
  RNA.tpm,
  features_df,
  plot_dot = TRUE,
  top_n = 10,
  file_name = NULL,
  width = 8,
  height = 10
)

Arguments

RNA.tpm

A numeric matrix or data frame of gene expression values (genes in rows, samples in columns).

features_df

A data frame of continuous features (samples in rows, features in columns) to be modeled as covariates.

plot_dot

Logical; if TRUE, generates and saves dotplots of top enriched Hallmark pathways for each feature. Default is TRUE.

top_n

Integer; number of top pathways to display in the dotplot. Default is 10.

file_name

Character; optional suffix for saved PDF files. Default is NULL.

width

Numeric; width of the PDF plot in inches. Default is 8.

height

Numeric; height of the PDF plot in inches. Default is 10.

Value

A list containing:

DE_results

A named list of topTable results for each feature, including logFC, moderated t-statistics, p-values, and adjusted p-values.

GSEA_results

A named list of GSEA results from clusterProfiler for each feature.

Details

The function works as follows:

  1. Hallmark gene sets are retrieved from MSigDB using msigdbr.

  2. A multivariate linear model is fitted for each gene using limma::lmFit.

  3. Empirical Bayes moderation is applied via limma::eBayes.

  4. For each feature:

    1. Differential expression results are extracted using topTable for the coefficient of that feature.

    2. Genes are ranked by moderated t-statistics.

    3. Hallmark GSEA is performed using the ranked gene list.

    4. Optionally, a dotplot of the top enriched pathways is generated.

Examples

if (FALSE) { # \dontrun{
  results <- compute_factor_gsea(RNA.tpm = expression_matrix,
                              features_df = feature_table,
                              plot_dot = TRUE,
                              top_n = 10,
                              file_name = "Feature1_vs_all")
} # }