c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Bas...

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Autores principales:	Lin Li, Hao Dai, Zhaoyuan Fang, Luonan Chen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Network flow entropy Cell-specific network Single-cell network Direct association Conditional independence Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/0204d71c39c3498098aeb49775987852
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spelling	oai:doaj.org-article:0204d71c39c3498098aeb497759878522021-11-16T04:09:26Zc-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network1672-022910.1016/j.gpb.2020.05.005https://doaj.org/article/0204d71c39c3498098aeb497759878522021-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1672022921000589https://doaj.org/toc/1672-0229The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene–gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.Lin LiHao DaiZhaoyuan FangLuonan ChenElsevierarticleNetwork flow entropyCell-specific networkSingle-cell networkDirect associationConditional independenceBiology (General)QH301-705.5ENGenomics, Proteomics & Bioinformatics, Vol 19, Iss 2, Pp 319-329 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Network flow entropy Cell-specific network Single-cell network Direct association Conditional independence Biology (General) QH301-705.5
spellingShingle	Network flow entropy Cell-specific network Single-cell network Direct association Conditional independence Biology (General) QH301-705.5 Lin Li Hao Dai Zhaoyuan Fang Luonan Chen c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network
description	The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene–gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.
format	article
author	Lin Li Hao Dai Zhaoyuan Fang Luonan Chen
author_facet	Lin Li Hao Dai Zhaoyuan Fang Luonan Chen
author_sort	Lin Li
title	c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network
title_short	c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network
title_full	c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network
title_fullStr	c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network
title_full_unstemmed	c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network
title_sort	c-csn: single-cell rna sequencing data analysis by conditional cell-specific network
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0204d71c39c3498098aeb49775987852
work_keys_str_mv	AT linli ccsnsinglecellrnasequencingdataanalysisbyconditionalcellspecificnetwork AT haodai ccsnsinglecellrnasequencingdataanalysisbyconditionalcellspecificnetwork AT zhaoyuanfang ccsnsinglecellrnasequencingdataanalysisbyconditionalcellspecificnetwork AT luonanchen ccsnsinglecellrnasequencingdataanalysisbyconditionalcellspecificnetwork
_version_	1718426704491315200

c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

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