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

knitr::opts_chunk$set(
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scHiCBayes

scHiCBayes (Xie, Han, Jin, and Lin, 2021) is a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. scHiCBayes takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available.

Installation

The scHiCBayes package has the following R-package dependencies: Rcpp, RcppArmadillo, parallel, Rtsne, ggplot2, ggpubr, and mclust. The dependent packages will be automatically installed along with scHiCBayes. You can use the following commands to install scHiCBayes from GitHub.

# Install and load "devtools" package. 
install.packages("devtools")
library("devtools")

# Install "scHiCBayes" package from github.
install_github("Queen0044/scHiCBayes")

If you are Windows user, please install Rtools40 (https://cran.r-project.org/bin/windows/Rtools/) first, and restart R to install scHiCBayes package. Note that Rtools40 is for R 4.0.0+ so that you might have to update your R version.

If you have OneDrive backing-up "C:\User\Your_user_name\Documents", the installation may fail. You can download the zip file from Github and install scHiCBayes manually.

Example

This is a basic example which shows you how to solve a common problem:

library(scHiCBayes)
#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)

For more information of functions, please read the vignettes.

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