README.md

In cuhklinlab/scAMACE: scAMACE (integrative Analysis of single-cell Methylation, chromatin ACcessibility, and gene Expression)

scAMACE (R implementation)

scAMACE (integrative Analysis of single-cell Methylation, chromatin ACcessibility, and gene Expression)

A model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation.

1. Installation

You can install the released version of scAMACE from Github:

library(devtools)
devtools::install_github("cuhklinlab/scAMACE")

Package 'betareg' is also required for the implementation of beta regression:

install.packages('betareg')

2. Main Functions

cal_M_step: scAMACE expectation-maximization (EM) implementation, a model-based approach to the joint clustering of single-cell data on chromatin accessibility, gene expression and methylation.

cal_post: Calculate the posterior probability for one iteration in the EM algorithm.

cal_E_rna: Perform E-step (i.e. calculate the expectations of missing data) for one iteration of scRNA-Seq or sc-methylation data in the EM algorithm.

cal_E_acc: Perform E-step (i.e. calculate the expectations of missing data) for one iteration of scCAS data in the EM algorithm.

simData_3data: Generate simulation data x, y and t.

3. Datasets and Examples

Please refer to the vigenette with several examples for a quick guide to scAMACE package.

4. Reference

Jiaxuan Wangwu, Zexuan Sun, Zhixiang Lin: scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation.

cuhklinlab/scAMACE documentation built on Dec. 19, 2021, 7:03 p.m.