gProfiler_cellWeighted_Foldchange function

Pathway enrichment for cwFold-changes

This function runs through each list of cell weighted Fold changes (cwFold-changes) and completes both pathway and transcription factor (TF) enrichment.

gProfiler_cellWeighted_Foldchange(
  cellWeighted_Foldchange_matrix,
  species,
  background,
  gene_cut,
  newGprofiler
)

Arguments

• cellWeighted_Foldchange_matrix: Matrix of cell weighted Fold changes from the deconvolute_and_contextualize functions.
• species: Human, mouse, or a name that is compatible with gProfileR (e.g. "mmusculus").
• background: A list of background genes to test against.
• gene_cut: The top number of genes in pathway analysis.
• newGprofiler: Using gProfileR or gprofiler2, (T/F).

Returns

List with the following elements: - BP: gprofiler enrichment of biological pathways for each cell-type

• TF: gprofiler enrichment of transcription factors for eachc cell-type.

Details

This function takes a matrix of cellWeighted_Foldchange and a species (human, mouse, or a character directly compatible with g:ProfileR). Before completing pathway analysis with g:ProfileR. Enriched pathways are stored in a list and returned.

Examples

data(PBMC_example)

bulk_DE_cors <- PBMC_example$bulk_DE_cors
bulk_normalized <- PBMC_example$bulk_normalized
odds_ratio_in <- PBMC_example$odds_ratio_in

case_grep <- "_female"
control_grep <- "_male"
max_proportion_change <- 10
print_plots <- FALSE
theSpecies <- "human"
norm <- deconvolute_and_contextualize(count_file = bulk_normalized,
                                     signature_matrix = odds_ratio_in,
                                      DEG_list = bulk_DE_cors, case_grep = case_grep, 
                                      control_grep = control_grep,
                                       max_proportion_change = max_proportion_change,
                                       print_plots = print_plots,
                                       theSpecies = theSpecies)
background = rownames(bulk_normalized)
STVs <- gProfiler_cellWeighted_Foldchange(
 cellWeighted_Foldchange_matrix = norm$cellWeighted_Foldchange,
 species = theSpecies, background = background, gene_cut = -9,
 newGprofiler = TRUE)