View source: R/plotEnrichment.R
Plot of the contingency tables for the chosen method. The top-left cells are colored, according to Fisher exact tests' p-values, if the number of features in those cells are enriched.
plotContingency(enrichment, method, levels_to_plot)
enrichment object produced by createEnrichment function.
name of the method to plot.
A character vector containing the levels of the enrichment variable to plot.
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"]) # Add some normalization/scaling factors to the phyloseq object my_norm <- setNormalizations(fun = c("norm_edgeR", "norm_CSS"), method = c("TMM", "CSS")) ps_plaque_16S <- runNormalizations(normalization_list = my_norm, object = ps_plaque_16S) # Initialize some limma based methods my_limma <- set_limma(design = ~ 1 + RSID + HMP_BODY_SUBSITE, coef = "HMP_BODY_SUBSITESupragingival Plaque", norm = c("TMM", "CSS")) # 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"]]) # Perform DA analysis Plaque_16S_DA <- runDA(method_list = my_limma, object = ps_plaque_16S) # Enrichment analysis enrichment <- createEnrichment(object = Plaque_16S_DA, priorKnowledge = priorInfo, enrichmentCol = "Type", namesCol = "GENUS", slot = "pValMat", colName = "adjP", type = "pvalue", direction = "logFC", threshold_pvalue = 0.1, threshold_logfc = 1, top = 10, verbose = TRUE) # Contingency tables plotContingency(enrichment = enrichment, method = "limma.TMM") # Barplots plotEnrichment(enrichment, enrichmentCol = "Type") # Mutual findings plotMutualFindings( enrichment = enrichment, enrichmentCol = "Type", n_methods = 1 )
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