iProFun.eFDR: iProFun eFDR assessment based on permutation for multiple...

In songxiaoyu/iProFun: An integrative analysis tool to screen for Proteogenomic Functional traits perturbed by DNA alterations

iProFun eFDR assessment based on permutation for multiple outcome data type.

Description

iProFun empirical false discovery rate (eFDR) assessment based on permutation for multiple outcome data types.

Usage

iProFun.eFDR(
  reg.all,
  yList,
  xList,
  covariates,
  pi1,
  NoProbXIndex = NULL,
  permutate_number = 10,
  var.ID = c("Gene_ID"),
  Y.rescale = F,
  var.ID.additional = NULL,
  seed = NULL
)

Arguments

reg.all	The regression summary (unformatted) such as from iProFun.reg.
yList	yList is a list of data matrix for outcomes.
xList	xList is a list of data matrix for predictors.
covariates	covariates is a list of data matrix for covariate.
pi1	pi1 is pre-specified prior of proportion of non-null statistics. It cane be a number in (0, 1) or a vector of numbers with length of ylist.
NoProbXIndex	NoProbXIndex allows users to provide the index for the predictor data type(s) that are not considered for calculating posterior probabilities of association patterns.
permutate_number	Number of permutation, default 10
var.ID	var.ID gives the variable name (e.g. gene/protein name) to match different data types. If IDs are not specified, the first columns will be considered as ID variable.
Y.rescale	Y.rescale (default = False) gives whether each outcome variable should be standardized to mean 0 and sd 1 before regression.
var.ID.additional	var.ID.additional allows to output additional variable names from the input. Often helpful if multiple rows (e.g. probes) are considered per gene to allow clear index of the rows.
seed	seed allows users to externally assign seed to replicate results.

Value

xName:	Name of the predictors.
PostProb:	The association probability for each gene on each data type.
Gene_efdr:	The eFDR for each gene on each data type.

songxiaoyu/iProFun documentation built on Dec. 8, 2022, 3:54 p.m.