# two_method_pathway_enrichment Pathway analysis of each cell-type based on cell-type specificity and rank improvement by scMappR. ```r two_method_pathway_enrichment( DEG_list, theSpecies, scMappR_vals, background_genes = NULL, output_directory = "output", plot_names = "reweighted", number_genes = -9, newGprofiler = TRUE, toSave = FALSE, path = NULL ) ``` ## Arguments - `DEG_list`: Differentially expressed genes (gene_name, padj, log2fc). - `theSpecies`: Human, mouse, or a character that is compatible with g:ProfileR. - `scMappR_vals`: cell weighted Fold-changes of differentially expressed genes. - `background_genes`: A list of background genes to test against. NULL assumes all genes in g:profileR gene set databases. - `output_directory`: Path to the directory where files will be saved. - `plot_names`: Names of output. - `number_genes`: Number of genes to if there are many, many DEGs. - `newGprofiler`: Whether to use g:ProfileR or gprofiler2 (T/F). - `toSave`: Allow scMappR to write files in the current directory (T/F). - `path`: If toSave == TRUE, path to the directory where files will be saved. ## Returns List with the following elements: - **rank_increase**: A list containing the degree of rank change between bulk DE genes and cwFold-changes. Pathway enrichment and TF enrichment of these reranked genes. - **non_rank_increase**: list of DFs containing the pathway and TF enrichment of cwFold-changes. ## Details This function re-ranks cwFoldChanges based on their absolute cell-type specificity scores (per-celltype) as well as their rank increase in cell-type specificity before completing an ordered pathway analysis. In the second method, only genes with a rank increase in cell-type specificity were included. ## Examples ```r # load data for scMappR data(PBMC_example) bulk_DE_cors <- PBMC_example$bulk_DE_cors bulk_normalized <- PBMC_example$bulk_normalized odds_ratio_in <- PBMC_example$odds_ratio_in case_grep <- "_female" control_grep <- "_male" max_proportion_change <- 10 print_plots <- FALSE theSpecies <- "human" # calculate cwFold-changes toOut <- scMappR_and_pathway_analysis(count_file = bulk_normalized, signature_matrix = odds_ratio_in, DEG_list = bulk_DE_cors, case_grep = case_grep, control_grep = control_grep, rda_path = "", max_proportion_change = 10, print_plots = TRUE, plot_names = "tst1", theSpecies = "human", output_directory = "tester", sig_matrix_size = 3000, up_and_downregulated = FALSE, internet = FALSE) # complete pathway enrichment using both methods twoOutFiles <- two_method_pathway_enrichment(DEG_list = bulk_DE_cors,theSpecies = "human", scMappR_vals = toOut$cellWeighted_Foldchange, background_genes = rownames(bulk_normalized), output_directory = "newfun_test",plot_names = "nonreranked_", toSave = FALSE) ```

two_method_pathway_enrichment function