runassignGFRN: Run optimized single pathway ASSIGN

In ASSIGN: Adaptive Signature Selection and InteGratioN (ASSIGN)

Description

This function runs eight ASSIGN runs based on the pathway optimizations from the paper. You can run all eight pathways in serial, or call this function and specify the run parameter to run a specific pathway. Some ASSIGN parameters can be customized using this function. The default values were used in the analysis for the paper.

Usage

runassignGFRN(
  indata,
  run = c("akt", "bad", "egfr", "her2", "igf1r", "krasgv", "raf"),
  optimized_geneList = NULL,
  use_seed = 1234,
  sigma_sZero = 0.05,
  sigma_sNonZero = 0.5,
  S_zeroPrior = FALSE,
  iter = 1e+05,
  burn_in = 50000,
  exclude_common_genes = FALSE,
  adaptive_S = TRUE,
  ECM = FALSE
)

Arguments

indata	The list of data frames from ComBat.step2
run	specifies the pathways to predict. The default list will cause all eight pathways to be run in serial. Specify a pathway ("akt", "bad", "egfr", etc.) or list of pathways to run those pathways only.
optimized_geneList	a list of custom optimized gene lists for the gfrn pathways either created manually or output by optimizeGFRN
use_seed	Set the seed before running ASSIGN. This will make the result consistent between runs. The default is 1234. Set use_seed as FALSE to not set a seed.
sigma_sZero	Each element of the signature matrix (S) is modeled by a spike-and-slab mixture distribution. Sigma_sZero is the variance of the spike normal distribution. The default is 0.05.
sigma_sNonZero	Each element of the signature matrix (S) is modeled by a spike-and-slab mixture distribution. Sigma_sNonZero is the variance of the slab normal distribution. The default is 0.5.
S_zeroPrior	Logicals. If TRUE, the prior distribution of signature follows a normal distribution with mean zero. The default is FALSE.
iter	The number of iterations in the MCMC. The default is 100000.
burn_in	The number of burn-in iterations. These iterations are discarded when computing the posterior means of the model parameters. The default is 50000.
exclude_common_genes	Remove commonly differentially expressed genes for overexpression signatures. The default is FALSE.
adaptive_S	Logical. If TRUE, the model adapts the signatures (S) of genomic measures for the test samples. The default for GFRN analysis is TRUE.
ECM	Logicals. If TRUE, ECM algorithm, rather than Gibbs sampling, is applied to approximate the model parameters. The default is FALSE.

Value

Data is output to the current working directory in a results directory.

Examples

## Not run: 
testData <- read.table(paste0("https://drive.google.com/uc?authuser=0&",
                              "id=1mJICN4z_aCeh4JuPzNfm8GR_lkJOhWFr",
                              "&export=download"),
                       sep='\t', row.names=1, header=1)
combat.data <- ComBat.step2(testData, pcaPlots = TRUE)
runassignGFRN(combat.data)

## End(Not run)

ASSIGN documentation built on Nov. 8, 2020, 8:29 p.m.