iProFun.eFDR.1y: iProFun eFDR assessment for one outcome data type.

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

iProFun eFDR assessment for one outcome data type.

Description

iProFun empirical false discovery rate (eFDR) assessment for one outcome data type.

Usage

iProFun.eFDR.1y(
  reg.all,
  which.y,
  yList,
  xList,
  covariates,
  pi1,
  NoProbXIndex = NULL,
  Y.rescale = F,
  permutate_number = 10,
  var.ID = c("Gene_ID"),
  var.ID.additional = NULL,
  seed = NULL
)

Arguments

reg.all	The regression summary (unformatted) such as from iProFun.reg.
which.y	The index for one outcome data type that the eFDR assessment is for.
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.
Y.rescale	Y.rescale (default = False) gives whether each outcome variable should be standardized to mean 0 and sd 1 before regression.
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.
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

eFDR.grid:	eFDR by the grid of posterior probability cutoffs.
fdr_cutPob:	the cutoff values for pre-specified eFDR rate and the posterior probabilities for a pair of data types based on permutation.
No.Identified.filter:	the number of identified variables for each pair of data types.
No.Identified.no.filter:	the number of identified variables for each pair of data types.
Gene_fdr:	A table summarizing the posterior probabilities (PostProb), the eFDR (eFDR.no.filter), the significance under different criteria (nominal FDR, PostProb cutoffs and filter) for each variable (e.g. gene) under consideration.

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