iProMix.eFDR.PermAdd: FDR controlled iProMix identification with permutation-add...
In JiayiJi/iProMix: Cell-Type Specific Association Analysis for Bulk Profiling Data

Description Usage Arguments Value Examples

FDR controlled iProMix identification with permutation-add procedure

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

`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.
`CellType`	The cell type to be estimated. Default is 1.
`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

list with 10 elements. It contains

`NoSigGene:`	Number of significant genes at the pre-determined FDR cutoff, using consensus voting procedure if B>1
`IdxSigGene:`	The index of significant genes at the pre-determined FDR cutoff, using consensus voting procedure if B>1
`IdxSigGeneEach:`	The index of significant genes at each permutation
`ft_add:`	The iProMix output (e.g. estimated parameters, log likelihoods) using combined original and permutation data (y, x, x_tilde).

library(iProMix)
set.seed(111)
y <- matrix(rnorm(1000,10,1), ncol=10)
x <- rnorm(100,10,1)
pi <- runif(100)
result <- iProMix.eFDR.PermAdd(yMatrix=y, x=x, pi=pi,reduce1=c(2,1), B=1, seed=1132, FDR=0.1)