padmaRun: Calculate individualized deviation scores from multi-omic...

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

Description

Calculate individualized deviation scores from multi-omic data

Usage

padmaRun(
  omics_data,
  gene_map = padma::mirtarbase,
  base_ids = NULL,
  supp_ids = NULL,
  pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY",
  impute = FALSE,
  variance_threshold = 1e-04,
  full_results = TRUE,
  verbose = TRUE,
  ...
)

Arguments

omics_data	Object of class 'MultiAssayExperiment' containing omics data from n matched individuals.
gene_map	(optional) Data frame mapping arbitrary biological entities (e.g. miRNAs) to genes. Contains two columns, where the first provides the IDs of the entity and the second provides the IDs of the corresponding target gene. By default, the miRNA-gene interactions of type 'Functional MTI' from miRTarBase are used (see the preloaded 'mirtarbase' data in the package).
base_ids	(optional) Sample names to be used as reference base data. By default, all samples are used.
supp_ids	(optional) Sample names to be used as supplementary individuals to be projected onto the analysis based on the individuals identified in base_ids. By default, takes the value NULL, but should not overlap with base_ids if provided by the user.
pathway_name	Character of either a KEGG pathway identifier or MSigDB pathway names (e.g., see the pathway names in the 'geneset' column of the preloaded msigdb data in the package), or a vector of gene symbols.
impute	If TRUE, impute missing values separately in base and supplementary data using MFA as implemented in the missMDA package; otherwise simple mean imputation is used (default).
variance_threshold	Minimal variance required across samples to retain a biological entity in the analysis
full_results	If TRUE (default), include full MFA results in function output; otherwise, provide concise output to save space.
verbose	If TRUE, provide verbose output.
...	Optional additional arguments

Value

An S4 object of class padmaResults, where individualized pathway deviation scores are stored as the assay data, and the corresponding pathway name, full MFA results, number of genes, and names of imputed or filtered genes are stored as slots that can be retrieved using the appropriate accessor functions.

Examples

LUAD_subset <- padma::LUAD_subset
## Create MultiAssayExperiment object with LUAD data
omics_data <- 
  list(rnaseq = LUAD_subset$rnaseq,
       methyl = LUAD_subset$methyl,
       mirna = LUAD_subset$mirna,
       cna = LUAD_subset$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
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)

padma documentation built on Nov. 8, 2020, 4:56 p.m.