Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Pengfei Liang; Wuritu Yang; Xing Chen; Chunshen Long; Lei Zheng; Hanshuang Li; Yongchun Zuo

doi:10.1016/j.omtn.2020.02.004

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Mol Ther Nucleic Acids. 2020 Jun 5:20:155-163. doi: 10.1016/j.omtn.2020.02.004. Epub 2020 Feb 13.

Authors

Pengfei Liang¹, Wuritu Yang¹, Xing Chen¹, Chunshen Long¹, Lei Zheng¹, Hanshuang Li¹, Yongchun Zuo²

Affiliations

¹ The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China.
² The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot 010070, China. Electronic address: yczuo@imu.edu.cn.

Abstract

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.

Keywords: DGE; developmental mRNA signature; differential gene expression; feature selection; machine learning; prediction; single-cell transcriptome.