Recent advances in single-cell technologies, including single-cell ATAC-seq (scATAC-seq), have enabled large-scale profiling of the chromatin accessibility landscape at the single-cell level. However, the characteristics of scATAC-seq data, including high sparsity and high dimensionality, have greatly complicated the computational analysis. Here, we propose scDEC, a computational tool for scATAC-seq analysis with deep generative neural networks. scDEC is built on a pair of generative adversarial networks, and is capable of simultaneously learning the latent representation and inferring cell labels. In a series of experiments, scDEC demonstrates superior performance over other tools in scATAC-seq analysis across multiple datasets and experimental settings. In downstream applications, we demonstrate that the generative power of scDEC helps to infer the trajectory and intermediate state of cells during differentiation and the latent features learned by scDEC can potentially reveal both biological cell types and within-cell-type variations. We also show that it is possible to extend scDEC for the integrative analysis of multi-modal single cell data.
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The InSilico dataset was collected from the GEO database with accession number GSE65360. The mouse Forebrain dataset was downloaded from the GEO database with accession number GSE100033. The Splenocyte dataset can be accessed at ArrayExpress database with accession number E-MTAB-6714. The All blood dataset can be accessed at the GEO database with accession number GSE96772. The mouse atlas data are available at http://atlas.gs.washington.edu/mouse-atac. The human PBMCs dataset used in multi-modal single cell analysis was downloaded from 10x Genomics (https://support.10xgenomics.com/single-cell-multiome-atac-gex) with entry ‘pbmc_granulocyte_sorted_10k’. The preprocessed scATAC-seq data used as input for scDEC model in this study can be downloaded from https://doi.org/10.5281/zenodo.397785856.
scDEC is open-source software based on the TensorFlow library57, which is available on Github (https://github.com/kimmo1019/scDEC) and Zenodo (https://doi.org/10.5281/zenodo.4560834)58. A CodeOcean capsule with several example datasets is available at https://codeocean.com/capsule/0746056/tree/v159. The pretrained models on both benchmark single-cell datasets and 10x Genomics PBMCs multi-modal single-cell dataset were provided.
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This work was supported by NIH grants R01 HG010359 (W.H.W.) and P50 HG007735 (W.H.W.). This work was also supported by the National Key Research and Development Program of China no. 2018YFC0910404 (R.J.), the National Natural Science Foundation of China nos 61873141 (R.J.), 61721003 (R.J.) and 61573207 (R.J.).
The authors declare no competing interests.
Peer review information Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.
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Liu, Q., Chen, S., Jiang, R. et al. Simultaneous deep generative modelling and clustering of single-cell genomic data. Nat Mach Intell 3, 536–544 (2021). https://doi.org/10.1038/s42256-021-00333-y
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