Machine learning of single-cell transcriptome highly identifies mRNA signature by comparing F-score selection with DGE analysis

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, researchers first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, they 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.

The Workflow of Construction and Validation of the Embryo Development Signatures


The best signature from the F-score method consisted of 1,881 genes related to embryo development that was constructed and validated using gene expression from publicly available datasets.

Liang P, Yang W, Chen X, Long C, Zheng L, Li H, Zuo Y. (2020) Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis. Mol Ther Nucleic Acids 20:155-163. [article]

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