FEM: mining biological meaning from cell level in single-cell RNA sequencing data

Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is val...

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Autores principales:	Yunqing Liu, Na Lu, Changwei Bi, Tingyu Han, Guo Zhuojun, Yunchi Zhu, Yixin Li, Chunpeng He, Zuhong Lu
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Single-cell RNA sequencing Gene set enrichment analysis Functional expression matrix Medicine R
Acceso en línea:	https://doaj.org/article/6903d3e9920b448798549a3635d4c60d
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id	oai:doaj.org-article:6903d3e9920b448798549a3635d4c60d
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spelling	oai:doaj.org-article:6903d3e9920b448798549a3635d4c60d2021-12-02T15:05:18ZFEM: mining biological meaning from cell level in single-cell RNA sequencing data10.7717/peerj.125702167-8359https://doaj.org/article/6903d3e9920b448798549a3635d4c60d2021-11-01T00:00:00Zhttps://peerj.com/articles/12570.pdfhttps://peerj.com/articles/12570/https://doaj.org/toc/2167-8359Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).Yunqing LiuNa LuChangwei BiTingyu HanGuo ZhuojunYunchi ZhuYixin LiChunpeng HeZuhong LuPeerJ Inc.articleSingle-cell RNA sequencingGene set enrichment analysisFunctional expression matrixMedicineRENPeerJ, Vol 9, p e12570 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Single-cell RNA sequencing Gene set enrichment analysis Functional expression matrix Medicine R
spellingShingle	Single-cell RNA sequencing Gene set enrichment analysis Functional expression matrix Medicine R Yunqing Liu Na Lu Changwei Bi Tingyu Han Guo Zhuojun Yunchi Zhu Yixin Li Chunpeng He Zuhong Lu FEM: mining biological meaning from cell level in single-cell RNA sequencing data
description	Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).
format	article
author	Yunqing Liu Na Lu Changwei Bi Tingyu Han Guo Zhuojun Yunchi Zhu Yixin Li Chunpeng He Zuhong Lu
author_facet	Yunqing Liu Na Lu Changwei Bi Tingyu Han Guo Zhuojun Yunchi Zhu Yixin Li Chunpeng He Zuhong Lu
author_sort	Yunqing Liu
title	FEM: mining biological meaning from cell level in single-cell RNA sequencing data
title_short	FEM: mining biological meaning from cell level in single-cell RNA sequencing data
title_full	FEM: mining biological meaning from cell level in single-cell RNA sequencing data
title_fullStr	FEM: mining biological meaning from cell level in single-cell RNA sequencing data
title_full_unstemmed	FEM: mining biological meaning from cell level in single-cell RNA sequencing data
title_sort	fem: mining biological meaning from cell level in single-cell rna sequencing data
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/6903d3e9920b448798549a3635d4c60d
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FEM: mining biological meaning from cell level in single-cell RNA sequencing data

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