inst/doc/padma.R

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

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ---- fig.align = "center", out.width = "50%", echo=FALSE---------------------
knitr::include_graphics("hex_padma_v2.png")

## ---- message = FALSE---------------------------------------------------------
library(padma)

## ---- eval = FALSE------------------------------------------------------------
#  run_padma <-
#    padma(mae, pathway_name = "c2_cp_BIOCARTA_D4GDI_PATHWAY")

## ---- eval = FALSE------------------------------------------------------------
#  assay(run_padma)
#  
#  factorMap(run_padma)
#  factorMap(run_padma, partial_id = "TCGA-78-7536")
#  omicsContrib(run_padma)

## ---- explore_LUAD_subset-----------------------------------------------------
names(LUAD_subset)

lapply(LUAD_subset, class)

lapply(LUAD_subset, dim)

## ---- msigdb------------------------------------------------------------------
head(msigdb)

## -----------------------------------------------------------------------------
head(mirtarbase)

## -----------------------------------------------------------------------------
library(MultiAssayExperiment)

LUAD_subset <- padma::LUAD_subset
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))

## ---- runpadma----------------------------------------------------------------
D4GDI <- msigdb[grep("D4GDI", msigdb$geneset), "geneset"]
run_padma <- 
  padma(mae, pathway_name = D4GDI, verbose = FALSE)

## ---- runpadmalist------------------------------------------------------------
clinical_data <- data.frame(LUAD_subset$clinical)
rownames(clinical_data) <- clinical_data$bcr_patient_barcode

run_padma_list <- 
  padma(LUAD_subset[-which(names(LUAD_subset) == "clinical")],
        colata = clinical_data,
        pathway_name = D4GDI,
        verbose = FALSE)

## ---- runpadma2---------------------------------------------------------------
D4GDI_genes <- unlist(strsplit(
  msigdb[grep("D4GDI", msigdb$geneset), "symbol"], ", "))
D4GDI_genes

run_padma_again <- 
  padma(mae, pathway_name = D4GDI_genes, verbose = FALSE)

## ---- factorMAP---------------------------------------------------------------
factorMap(run_padma, dim_x = 1, dim_y = 2)

## ---- factorMAP2--------------------------------------------------------------
factorMap(run_padma, dim_x = 1, dim_y = 2, ggplot = FALSE)

## ---- factorMappartial--------------------------------------------------------
factorMap(run_padma,
           partial_id = "TCGA-78-7536",
           dim_x = 1, dim_y = 2)

## ---- factorMappartial2-------------------------------------------------------
factorMap(run_padma,
           partial_id = "TCGA-78-7536",
           dim_x = 1, dim_y = 2, ggplot = FALSE)

## ---- omicscontrib------------------------------------------------------------
omicsContrib(run_padma, max_dim = 10)

## ---- omicscontrib2-----------------------------------------------------------
omicsContrib(run_padma, max_dim = 10, ggplot = FALSE)

## -----------------------------------------------------------------------------
run_padma_supp <- 
  padma(mae, pathway_name = D4GDI, verbose = FALSE,
        base_ids = sampleMap(mae)$primary[1:10],
        supp_ids = sampleMap(mae)$primary[15:20])

## -----------------------------------------------------------------------------
run_padma_impute <- 
  padma(mae, pathway_name = D4GDI, 
        impute = TRUE, verbose = FALSE)

## ---- eval = FALSE------------------------------------------------------------
#  run_padma_concise <-
#    padma(mae, pathway_name = D4GDI, full_results = FALSE)