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bioRxiv · 10.1101/2022.10.12.512003

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity

Sun, Y.·Shim, W. J.·Shen, S.·Sinniah, E.·Pham, D.·Su, Z.·White, M. D.·Ho, J. W. K.·Nguyen, Q.·Boden, M.·Palpant, N.
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Abstract

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across diverse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

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Sun, Y., Shim, W. J., Shen, S., Sinniah, E., Pham, D., Su, Z., White, M. D., Ho, J. W. K., Nguyen, Q., Boden, M., Palpant, N.. 2022-10-17. Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity. https://doi.org/10.1101/2022.10.12.512003

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