# Measure cell-type specificity of cell-weighted Fold-changes This function normalizes cwFold-changes by each gene to help visualize the cell-type specificity of DEGs. It then tests if a cell-type has a large change in correlation from bulk DEGs. Finally, it identifies genes that may be specific to each cell-type. ```r cwFoldChange_evaluate( cwFC, celltype_prop, DEG_list, gene_cutoff = NULL, sd_cutoff = 3 ) ``` ## Arguments - `cwFC`: A matrix or data frame of cell-weighted fold-changes of DEGs. Rows are DEGs and columns are cell-types. - `celltype_prop`: A matrix or data frame of cell-type proportions. Rows are different cell-types and columns are different samples. These cell-type proportions can come from any source (not just scMappR). - `DEG_list`: An object with the first column as gene symbols within the bulk dataset (doesn't have to be in signature matrix), second column is the adjusted p-value, and the third the log2FC path to a .tsv file containing this info is also acceptable. - `gene_cutoff`: Additional cut-off of normalized cwFold-change to see if a gene is cut-off. - `sd_cutoff`: Number of standard deviations or median absolute deviations to calculate outliers. ## Returns List with the following elements: - **gene_level_investigation**: data frame of genes showing the Euclidian distances between cwFold-change and null vector as well as if cwFold-changes are distributed. - **celltype_level_investigation**: data frame of Spearman's and Pearson's correlation between bulk DEGs and cwFold-changes. - **cwFoldchange_vs_bulk_rank_change**: data frame of the change in rank of DEG between the bulk fold-change and cwFold-change. - **cwFoldChange_normalized**: cwFold-change normalized such that each gene sums to 1. - **cwFoldchange_gene_assigned**: List of cell-types where genes are designated to cell-type specific differential expression. - **cwFoldchange_gene_flagged_FP**: Mapped cwFoldchanges that are flagged as false-positives. These are genes that are driven by the reciprical ratio of cell-type proportions between case and control. These genes may be DE in a non-cell-type specific manner but are falsely assigned to cell-types with very large differences in proportion between condition. ## Details cwFold-changes and re-normalized and re-processed to interrogate cell-type specificity at the level of the cell-type and at the level of the gene. At the level of the cell-type, cwFold-changes are correlated to bulk DEGs. The difference in rank between bulk DEGs and cwFold-changes are also compared. At the level of the gene, cwFold-changes are re-normalized so that each gene sums to 1. Normalization of their distributions are tested with a Shapiro test. Then, outlier cell-types for each gene are measured by testing for `sd_cutoff`'s mad or sd's greater than the median or mean depending on if the cwFold-change is non-normally or normally distributed respectively. Cell-types considered outliers are then further filtered so their normalized cwFold-changes are greater than the cell-type proportions of that gene and `gene_cutoff` if the user sets it. ## Examples ```r data(PBMC_example) bulk_DE_cors <- PBMC_example$bulk_DE_cors bulk_normalized <- PBMC_example$bulk_normalized odds_ratio_in <- PBMC_example$odds_ratio_in case_grep <- "_female" control_grep <- "_male" max_proportion_change <- 10 print_plots <- FALSE theSpecies <- "human" toOut <- scMappR_and_pathway_analysis(count_file = bulk_normalized, signature_matrix = odds_ratio_in, DEG_list = bulk_DE_cors, case_grep = case_grep, control_grep = control_grep, rda_path = "", max_proportion_change = 10, print_plots = TRUE, plot_names = "tst1", theSpecies = "human", output_directory = "tester", sig_matrix_size = 3000, up_and_downregulated = FALSE, internet = FALSE) cwFC1 <- toOut$cellWeighted_Foldchange prop1 <- toOut$cellType_Proportions DE <- bulk_DE_cors eval_test <- cwFoldChange_evaluate(cwFC = cwFC1, celltype_prop = prop1, DEG_list = DE) ```

cwFoldChange_evaluate function