# 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. ```r 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 ```r 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) ```

gProfiler_cellWeighted_Foldchange function