runRA3: Run RA3 for integrative analysis of scATAC-seq data

In cuhklinlab/RA3: The R-based implementation of "A reference-guided approach for epigenetic characterization of single cells".

Description

RA3 refers to Reference-guided Approach for the Analysis of scATAC-seq data. It can simultaneously incorporate shared biological variation from reference data and identify distinct subpopulations, and thus achieves superior performance to existing methods in comprehensive experiments.

Usage

runRA3(sc_data, ref_data, K2 = 5, K3 = 5)

Arguments

sc_data	scATAC-seq count matrix, the rows should refer to features/regions and columns refer to cells.
ref_data	reference data matrix, the columns should refer to features/regioins and rows refer to observations.
K2	the number of components in RA3's second part, the default value is K2 = 5.
K3	the number of components in RA3's third part, the default value is K3 = 5.

Value

A list containing the following components:

H	the extracted latent features H.
W	the estimated matrix of parameter matrix W.
Beta	the estimated covariance parameter vector β.
Gamma	the estimated indicator matrix Γ.
A	the estimated precision matrix A.
sigma_s	the estimated σ^2.
lgp	the largest log posterior value when EM algorithm converges.

Examples

result <- runRA3(sc_example, reference_example)
result <- runRA3(sc_example, reference_example, 10, 5)

cuhklinlab/RA3 documentation built on March 18, 2021, 4:38 p.m.