MCMCImpute: Impute single cell HiC data

In Queen0044/scHiCBayes: Single cell HiC data imputation using Bayesian hierarchy model.

Description

This function imputes the single cell data by borrowing information from beighborbood, similar cells, and bulk data.

Usage

MCMCImpute(
  niter = 30000,
  burnin = 15000,
  single,
  bulk = bulk,
  startval = c(100, 100, 10, 8, 10, 0.1, 900, 0.2, 0, replicate(dim(single)[2], 8)),
  n,
  epsilon1 = 0.5,
  epsilon2 = 5,
  mc.cores = 1,
  cutoff = 0.5
)

Arguments

niter	Number of MCMC iteration.
burnin	Number of MCMC burnin.
single	Single cell matrix with each column being the upper triangular matrix of a single cell.
bulk	Bulk data. If NULL, bulk is set to be the sum of all single cells.
startval	starting value of MCMC chain.
n	Dimension of single cell matrix(original matrix is square and symmetrical).
epsilon1	Range size of delta, default is 0.5.
epsilon2	Range size of B, default is 5.
mc.cores	Number of cores to be used in mclapply function, default is 1.
cutoff	The threshold of π_{ij} that is used to define structural zeros.

Value

A list of posterior mean of probability, imputed data without defining SZ, and imputed data with SZ.

Examples

data("K562_T1_7k")
data("K562_bulk")
single=K562_T1_7k
T1_7k_res=MCMCImpute(niter=100000,burnin=5000,single=K562_T1_7k,bulk=K562_bulk,
startval=c(100,100,10,8,10,0.1,900,0.2,0,replicate(dim(single)[2],8)),n=61,mc.cores = 1,cutoff=0.5)

Queen0044/scHiCBayes documentation built on Dec. 18, 2021, 8:43 a.m.