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Nucleic Acids Res. 2019 Mar 13. pii: gkz172. doi: 10.1093/nar/gkz172. [Epub ahead of print]

Cell-specific network constructed by single-cell RNA sequencing data.

Dai H1, Li L1, Zeng T1, Chen L1,2,3,4.

Author information

1
Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.
2
Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
3
School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
4
Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.

Abstract

Single-cell RNA sequencing (scRNA-seq) is able to give an insight into the gene-gene associations or transcriptional networks among cell populations based on the sequencing of a large number of cells. However, traditional network methods are limited to the grouped cells instead of each single cell, and thus the heterogeneity of single cells will be erased. We present a new method to construct a cell-specific network (CSN) for each single cell from scRNA-seq data (i.e. one network for one cell), which transforms the data from 'unstable' gene expression form to 'stable' gene association form on a single-cell basis. In particular, it is for the first time that we can identify the gene associations/network at a single-cell resolution level. By CSN method, scRNA-seq data can be analyzed for clustering and pseudo-trajectory from network perspective by any existing method, which opens a new way to scRNA-seq data analyses. In addition, CSN is able to find differential gene associations for each single cell, and even 'dark' genes that play important roles at the network level but are generally ignored by traditional differential gene expression analyses. In addition, CSN can be applied to construct individual network of each sample bulk RNA-seq data. Experiments on various scRNA-seq datasets validated the effectiveness of CSN in terms of accuracy and robustness.

PMID:
30864667
DOI:
10.1093/nar/gkz172

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