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Deciphering cell types by integrating scATAC-seq data with genome sequences

Nature Computational Science volume 4, pages 285–298 (2024)Cite this article

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Abstract

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

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Fig. 1: Schematic overview of SANGO framework for annotating cells within scATAC-seq data by integrating the genome sequence.
Fig. 2: Performance of cell-type annotation for intra-datasets.
Fig. 3: Performance across platform or tissue datasets.
Fig. 4: Performance utilizing multisource data or atlas data as the reference.
Fig. 5: Revealing biological implications for normal tissues.
Fig. 6: Identifying multilevel cell types in basal cell carcinoma data.

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Data availability

We downloaded the raw scATAC matrix data directly from the website and followed previous works5,47,54 to binarize the matrix. (1) The datasets BoneMarrowA, BoneMarrowB, LungA, LungB, Kidney, Liver, Heart, LargeIntestineA, LargeIntestineB, SmallIntestine, WholeBrainA, WholeBrainB, Cerebellum and PreFrontalCortex are derived from the adult mouse atlas data55, downloading from either GEO accession number GSE111586 or the website http://atlas.gs.washington.edu/mouse-atac/data/. These datasets are sequenced using the sciATAC-seq technology56 and annotated through the mm9 reference genome. (2) The anterior datasets (MosA1, MosA2), middle datasets (MosM1, MosM2) and posterior datasets (MosP1, MosP2) are from the different sections of the secondary motor cortex in mouse brain57, which can be accessed through GEO accession number GSE126724. These datasets are sequenced using snATAC-seq technology58 and annotated through the GRCm38 reference genome. (3) The Mouse Brain (10x) dataset and the normal cortex dataset are sequenced using the 10x sequencing technology and annotated using the mm10 reference genome. These two datasets can be downloaded from https://support.10xgenomics.com/single-cell-atac/datasets/1.1.0/atac_v1_adult_brain_fresh_5k and https://www.10xgenomics.com/resources/datasets/fresh-cortex-from-adult-mouse-brain-p-50-1-standard-1-2-0, respectively. (4) The forebrain dataset can be downloaded through GEO accession number GSE100033, which is sequenced by the snATAC and annotated using the mm9 reference genome. (5) The PBMC atlas data, BCC–TIL and the basal cell carcinoma sample data are obtained from refs. 3,15. These datasets are annotated using the ENCODE hg19 reference genome and can be accessed through GEO accession number GSE129785 or the download website https://www.synapse.org/#!Synapse:syn52559388/files/. (6) The PBMC (10x) data are obtained from the official 10x website: https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets/1.0.0/pbmc_granulocyte_sorted_10k, which is annotated using the GRCh38 reference genome. (7) The raw HHLA data can be obtained from GEO accession number GSE184462 and the processed data can be downloaded from the website https://www.synapse.org/#!Synapse:syn52559388/files/. All of these datasets were preprocessed as described in Dataset preprocessing. Source data are provided with this paper.

Code availability

All source codes used in our experiments have been deposited at https://github.com/cquzys/SANGO. A Zenodo version is also available at https://doi.org/10.5281/zenodo.10826453 (ref. 59).

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Acknowledgements

This study has been supported by the National Key R&D Program of China (2022YFF1203100), the National Natural Science Foundation of China (T2394502), the Research and Development Project of Pazhou Lab (Huangpu) (2023K0606) and the Postdoctoral Fellowship Program of CPSF (GZC20233321).

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  1. These authors contributed equally: Yuansong Zeng, Mai Luo.

Authors and Affiliations

  1. School of Big Data and Software Engineering, Chongqing University, Chongqing, China

    Yuansong Zeng

  2. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Yuansong Zeng, Mai Luo, Ningyuan Shangguan, Junxi Feng, Ken Chen, Yutong Lu, Weijiang Yu & Yuedong Yang

  3. State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China

    Peiyu Shi & Jin Xu

  4. Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Guangzhou, China

    Yuedong Yang

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Contributions

Y.Y. conceived and supervised the project. Y.Z., M.L. and N.S developed and implemented the SANGO algorithm. Y.Y., W.Y. and Y.Z. validated the methods and wrote the paper. P.S., J.F. and J.X. conducted the biological analysis. Y.L. and K.C. discussed and performed the rebuttal experiments. All authors read and approved the final paper.

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Correspondence to Yuedong Yang.

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Nature Computational Science thanks Andrea Tangherloni, Guangyu Wang and Hao Wu for their contribution to the peer review of this work. Primary Handling Editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Zeng, Y., Luo, M., Shangguan, N. et al. Deciphering cell types by integrating scATAC-seq data with genome sequences. Nat Comput Sci 4, 285–298 (2024). https://doi.org/10.1038/s43588-024-00622-7

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