iProMix.eFDR.PermReplace: FDR controlled iProMix identification with...

In JiayiJi/iProMix: Cell-Type Specific Association Analysis for Bulk Profiling Data

Description

FDR controlled iProMix identification with permutation-replace procedure

Usage

iProMix.eFDR.PermReplace(
  yMatrix,
  x,
  cov = NULL,
  pi,
  reduce1 = c(2, 1),
  reduce2 = NULL,
  cl = FALSE,
  B = 1,
  seed = NULL,
  FDR = 0.1
)

Arguments

yMatrix	The quantitative measure (e.g. protein/expression) of a gene in the matrix form (Row: Genes; Columns: Samples)
x	The quantitative measure of anther gene (e.g. ACE2 protein levels) that we would like to know their cell-type specific dependency with Y
cov	The covariates for adjustment. Their impact on the mean value of X and Y are adjusted
pi	The proportion of cell type 1
reduce1	A index of the row and column of the variance-covariance matrix that should be forced to be zero in cell type 1. Default is NULL.
reduce2	A index of the row and column of the variance-covariance matrix that should be forced to be zero in cell type 2. Default is NULL.
cl	True of False. If true, parallel computing is used; need the library(doRNG). Default is FALSE
B	The number of permutation
seed	Seed for the permutation
FDR	FDR cutoff; Default 0.1

Value

list with 4 elements. It contains

NoSigGene:	Number of significant genes at the pre-determined FDR cutoff
IdxSigGene:	The index of significant genes
ft_data:	The iProMix output (e.g. estimated parameters, log likelihoods) using original data
ft_permutated:	The iProMix output (e.g. estimated parameters, log likelihoods) using permuted data

Examples

library(iProMix)
set.seed(111)
y <- matrix(rnorm(100,10,1), ncol=10)
x <- rnorm(10,10,1)
pi <- runif(10)
result <- iProMix.eFDR.PermReplace(yMatrix=y, x=x, pi=pi,reduce1=c(2,1), B=1, seed=1132, FDR=0.1)

JiayiJi/iProMix documentation built on Dec. 18, 2021, 1:30 a.m.