SRGS: sparse partial least squares-based recursive gene selection for gene regulatory network inference.

ABSTRACT:

Background

The identification of gene regulatory networks (GRNs) facilitates the understanding of the underlying molecular mechanism of various biological processes and complex diseases. With the availability of single-cell RNA sequencing data, it is essential to infer GRNs from single-cell expression. Although some GRN methods originally developed for bulk expression data can be applicable to single-cell data and several single-cell specific GRN algorithms were developed, recent benchmarking studies have emphasized the need of developing more accurate and robust GRN modeling methods that are compatible for single-cell expression data.

Results

We present SRGS, SPLS (sparse partial least squares)-based recursive gene selection, to infer GRNs from bulk or single-cell expressio

SUBMITTER: Guan J 

PROVIDER: S-EPMC9710113 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature