omicsContrib: Plot the omics contribution per MFA axis and the overall...

In andreamrau/padma: Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis

Plot the omics contribution per MFA axis and the overall weighted contribution

Description

Plot barplots indicating the percent contribution of each omics to each MFA dimension, as well as the overall weighted (by eigenvalue) percent contribution to the full analysis.

Usage

omicsContrib(
  padma_obj,
  max_dim = min(10, nrow(MFA_results(padma_obj)$eig)),
  ggplot = TRUE
)

Arguments

padma_obj	Output from running the padma function (with 'full_results = TRUE')
max_dim	Maximum dimension number of the MFA to be plotted
ggplot	If TRUE, use ggplot2 for plotting (and cowplot for combining ggplots)

Value

Barplots of percent variance contribution, optionally of class ggplot.

Examples

LUAD_subset <- padma::LUAD_subset
## Create MultiAssayExperiment object with LUAD data
omics_data <- 
  list(rnaseq = as.matrix(LUAD_subset$rnaseq),
       methyl = as.matrix(LUAD_subset$methyl),
       mirna = as.matrix(LUAD_subset$mirna),
       cna = as.matrix(LUAD_subset$cna))
pheno_data <- 
  data.frame(LUAD_subset$clinical, 
             row.names = LUAD_subset$clinical$bcr_patient_barcode)
mae <-
  suppressMessages(
    MultiAssayExperiment::MultiAssayExperiment(
      experiments = omics_data, colData = pheno_data))

## Run padma
run_padma <- 
  padma(mae, gene_map = padma::mirtarbase,
        pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY", verbose = FALSE)

summary(run_padma)

## padma plots
## Not run: 
factorMap(run_padma, dim_x = 1, dim_y = 2)
factorMap(run_padma, dim_x = 1, dim_y = 2,
           partial_id = "TCGA-78-7536")
omicsContrib(run_padma, max_dim = 10)

## End(Not run)

andreamrau/padma documentation built on Oct. 8, 2022, 9 a.m.