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bioRxiv · 10.1101/2023.02.05.526424

SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer

Xu, Y.·Wang, Y.·Ma, S.
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Abstract

Gene co-expression analysis of single-cell transcriptomes that aims to define functional relationships between genes is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules to be gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at a level greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging-by-GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

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Xu, Y., Wang, Y., Ma, S.. 2023-02-05. SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer. https://doi.org/10.1101/2023.02.05.526424

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