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

Yunqing Liu; Na Lu; Changwei Bi; Tingyu Han; Guo Zhuojun; Yunchi Zhu; Yixin Li; Chunpeng He; Zuhong Lu

doi:10.7717/peerj.12570

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

PeerJ. 2021 Nov 30:9:e12570. doi: 10.7717/peerj.12570. eCollection 2021.

Authors

Yunqing Liu¹, Na Lu¹, Changwei Bi^{1

2}, Tingyu Han¹, Guo Zhuojun¹, Yunchi Zhu¹, Yixin Li¹, Chunpeng He¹, Zuhong Lu¹

Affiliations

¹ State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjin, Jiangsu, China.
² Nanjing Forestry University, School of Information Science and Technology, Nanjin, Jiangsu, China.

Abstract

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).

Keywords: Functional expression matrix; Gene set enrichment analysis; Single-cell RNA sequencing.

Grants and funding

This work was supported by the National Science and Technology Major Project of China (6307030004). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.