Journal article
scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data
PLoS computational biology, Vol.16(7), pp.e1007471-e1007471
07/01/2020
DOI: 10.1371/journal.pcbi.1007471
PMCID: PMC7410337
PMID: 32716923
Abstract
Disease development and cell differentiation both involve dynamic changes; therefore, the reconstruction of dynamic gene regulatory networks (DGRNs) is an important but difficult problem in systems biology. With recent technical advances in single-cell RNA sequencing (scRNA-seq), large volumes of scRNA-seq data are being obtained for various processes. However, most current methods of inferring DGRNs from bulk samples may not be suitable for scRNA-seq data. In this work, we present scPADGRN, a novel DGRN inference method using "time-series" scRNA-seq data. scPADGRN combines the preconditioned alternating direction method of multipliers with cell clustering for DGRN reconstruction. It exhibits advantages in accuracy, robustness and fast convergence. Moreover, a quantitative index called Differentiation Genes' Interaction Enrichment (DGIE) is presented to quantify the interaction enrichment of genes related to differentiation. From the DGIE scores of relevant subnetworks, we infer that the functions of embryonic stem (ES) cells are most active initially and may gradually fade over time. The communication strength of known contributing genes that facilitate cell differentiation increases from ES cells to terminally differentiated cells. We also identify several genes responsible for the changes in the DGIE scores occurring during cell differentiation based on three real single-cell datasets. Our results demonstrate that single-cell analyses based on network inference coupled with quantitative computations can reveal key transcriptional regulators involved in cell differentiation and disease development.
Details
- Title: Subtitle
- scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data
- Creators
- Xiao Zheng - Wuhan UniversityYuan Huang - Yale UniversityXiufen Zou - Wuhan University
- Resource Type
- Journal article
- Publication Details
- PLoS computational biology, Vol.16(7), pp.e1007471-e1007471
- DOI
- 10.1371/journal.pcbi.1007471
- PMID
- 32716923
- PMCID
- PMC7410337
- NLM abbreviation
- PLoS Comput Biol
- ISSN
- 1553-734X
- eISSN
- 1553-7358
- Publisher
- Public Library Science
- Number of pages
- 22
- Grant note
- 11831015; 61672388 / Chinese National Natural Science Foundation; National Natural Science Foundation of China (NSFC) 2019CFA007 / Natural Science Foundation of Hubei Province 2018YFC1314600 / National Key Research and Development Program of China
- Language
- English
- Date published
- 07/01/2020
- Academic Unit
- Biostatistics
- Record Identifier
- 9984364442602771
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