Recent advances in single-cell technologies have enabled the characterization of epigenomic heterogeneity at the cellular level. Computational methods for automatic cell type annotation are urgently needed given the exponential growth in the number of cells. In particular, annotation of single-cell chromatin accessibility sequencing (scCAS) data, which can capture the chromatin regulatory landscape that governs transcription in each cell type, has not been fully investigated. Here we propose EpiAnno, a probabilistic generative model integrated with a Bayesian neural network, to annotate scCAS data automatically in a supervised manner. We systematically validate the superior performance of EpiAnno for both intra- and inter-dataset annotation on various datasets. We further demonstrate the advantages of EpiAnno for interpretable embedding and biological implications via expression enrichment analysis, partitioned heritability analysis, enhancer identification, cis-coaccessibility analysis and pathway enrichment analysis. In addition, we show that EpiAnno has the potential to reveal cell type-specific motifs and facilitate scCAS data simulation.
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A unified computational framework for single-cell data integration with optimal transport
Nature Communications Open Access 01 December 2022
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The CLP_LMPP_MPP and CLP_CMP_MPP datasets were collected from NCBI Gene Expression Omnibus (GEO) under accession no. GSE96772. The forebrain dataset can be accessed from GEO under accession number GSE100033. The InSilico dataset was collected from GEO with accession no. GSE65360. The leukaemia dataset can be accessed from GEO with accession no. GSE74310. The mouse brain datasets are available at http://atlas.gs.washington.edu/mouse-atac/data/. The PBMC5k and PBMC10k datasets are available at https://support.10xgenomics.com/single-cell-atac/datasets.
The EpiAnno software, including detailed documents and tutorial, is freely available on GitHub (https://github.com/xy-chen16/EpiAnno) and Zenodo (https://doi.org/10.5281/zenodo.5716525)61.
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This work was supported by the National Key Research and Development Program of China grant no. 2021YFF1200902 (R.J.), the National Natural Science Foundation of China grants nos. 61873141 (R.J.), 61721003 (X.Z.), 61573207 (R.J.), U1736210 (H.L.), a grant from the Guoqiang Institute, Tsinghua University (R.J.), and the Tsinghua-Fuzhou Institute for Data Technology. We thank S. Lei for helpful suggestions and L. Xiong for cell type labels of the forebrain dataset.
The authors declare no competing interests.
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Chen, X., Chen, S., Song, S. et al. Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. Nat Mach Intell 4, 116–126 (2022). https://doi.org/10.1038/s42256-021-00432-w
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