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

Description Usage Arguments Value Examples

A decomposition model

iProMix(
  y,
  x,
  cov = NULL,
  pi,
  tuningPar = 1e-08,
  diffNum = 1e-04,
  numitersNum = 200,
  reduce1 = NULL,
  reduce2 = NULL,
  inital.mu1 = NULL,
  inital.mu2 = NULL,
  inital.var1 = NULL,
  inital.var2 = NULL
)

`y`	The quantitative measure (e.g. protein/expression) of a gene
`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
`tuningPar`	Default is 1e-8. It is used in the embedded graphic lasso procedure for estimating correlation. A larger tuningPar can be selected if one is interested in penalized estimates
`diffNum`	Default 0.0001. The convergency criterion for EM algorithm.
`numitersNum`	Default 200. The number of iterations in EM algorithm.
`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.
`inital.mu1`	The initial mean value of (Y,X) for cell type 1, starting in Y and then in X. At default inital.mu1 =NULL, tissue-level mean is used.
`inital.mu2`	The initial mean value of (Y,X) for cell type 2, starting in Y and then in X. At default inital.mu2 =NULL, tissue-level mean is used.
`inital.var1`	The initial variance-covariance matrix of (Y,X) for cell type 1. At default inital.var1 =NULL, tissue-level variance-covariance matrix is used.
`inital.var2`	The initial variance-covariance matrix of (Y,X) for cell type 2. At default inital.var2 =NULL, tissue-level variance-covariance matrix is used.

list with 9 elements. It contains

`var1:`	The estimated (y,x) variance of cell type 1
`var2:`	The estimated (y,x) variance of cell type 2
`mu1:`	The estimated (y,x) mean function of cell type 1
`mu2:`	The estimated (y,x) mean function of cell type 2
`cor.score1:`	The estimated X-Y correlation in cell type 1
`cor.score2:`	The estimated X-Y correlation in cell type 2
`ll:`	The estimated log likelihood function
`coef1:`	The estimated covariate effects on (y,x) in cell type 1
`coef2:`	The estimated covariate effects on (y,x) in cell type 2

library(iProMix)
set.seed(111)
y <- rnorm(100,10,1)
x <- rnorm(100,10,1)
pi <- runif(100)
iProMix_result <- iProMix(y = y, x = x, pi = pi, reduce1=c(2,1), reduce2=NULL)