Perform the Fisher exact test for all the possible 2x2 contingency tables, considering differential abundance direction and enrichment variable.
enrichmentTest(method, enrichmentCol, alternative = "greater")
Output of differential abundance detection method in which
DA information is extracted by the
name of the column containing information for enrichment analysis.
indicates the alternative hypothesis and must be
a list of objects:
data.frame object with DA directions,
statistics, and feature names;
tables a list of 2x2 contingency tables;
tests the list of Fisher exact tests' p-values for each
summaries a list with the first element of each
contingency table and its p-value (for graphical purposes);
data("ps_plaque_16S") data("microbial_metabolism") # Extract genera from the phyloseq tax_table slot genera <- phyloseq::tax_table(ps_plaque_16S)[, "GENUS"] # Genera as rownames of microbial_metabolism data.frame rownames(microbial_metabolism) <- microbial_metabolism$Genus # Match OTUs to their metabolism priorInfo <- data.frame(genera, "Type" = microbial_metabolism[genera, "Type"]) # Unmatched genera becomes "Unknown" unknown_metabolism <- is.na(priorInfo$Type) priorInfo[unknown_metabolism, "Type"] <- "Unknown" priorInfo$Type <- factor(priorInfo$Type) # Add a more informative names column priorInfo[, "newNames"] <- paste0(rownames(priorInfo), priorInfo[, "GENUS"]) # DA Analysis # Make sure the subject ID variable is a factor phyloseq::sample_data(ps_plaque_16S)[, "RSID"] <- as.factor( phyloseq::sample_data(ps_plaque_16S)[["RSID"]]) # Add scaling factors ps_plaque_16S <- norm_edgeR(object = ps_plaque_16S, method = "TMM") # DA analysis da.limma <- DA_limma( object = ps_plaque_16S, design = ~ 1 + RSID + HMP_BODY_SUBSITE, coef = "HMP_BODY_SUBSITESupragingival Plaque", norm = "TMM" ) DA <- getDA(method = da.limma, slot = "pValMat", colName = "adjP", type = "pvalue", direction = "logFC", threshold_pvalue = 0.05, threshold_logfc = 1, top = NULL) # Add a priori information DA_info <- addKnowledge(method = DA, priorKnowledge = priorInfo, enrichmentCol = "Type", namesCol = "newNames") # Create contingency tables and compute F tests DA_info_enriched <- enrichmentTest(method = DA_info, enrichmentCol = "Type", alternative = "greater")
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