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scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data

Nature Machine Intelligence volume 4, pages 852–866 (2022)Cite this article

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Matters Arising to this article was published on 12 December 2024

A preprint version of the article is available at bioRxiv.

Abstract

Annotating cell types on the basis of single-cell RNA-seq data is a prerequisite for research on disease progress and tumour microenvironments. Here we show that existing annotation methods typically suffer from a lack of curated marker gene lists, improper handling of batch effects and difficulty in leveraging the latent gene–gene interaction information, impairing their generalization and robustness. We developed a pretrained deep neural network-based model, single-cell bidirectional encoder representations from transformers (scBERT), to overcome the challenges. Following BERT’s approach to pretraining and fine-tuning, scBERT attains a general understanding of gene–gene interactions by being pretrained on huge amounts of unlabelled scRNA-seq data; it is then transferred to the cell type annotation task of unseen and user-specific scRNA-seq data for supervised fine-tuning. Extensive and rigorous benchmark studies validated the superior performance of scBERT on cell type annotation, novel cell type discovery, robustness to batch effects and model interpretability.

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Fig. 1: Overview of the scBERT model.
Fig. 2: Benchmarking and robustness evaluation by intra-dataset cross-validation.
Fig. 3: Performance of scBERT across independent datasets generated by different single-cell sequencing technologies.
Fig. 4: Identification of novel cell types.
Fig. 5: Model interpretability.

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Data availability

All data used in this study are publicly available and the usages are fully illustrated in the Methods. The published Panglao dataset was downloaded from https://panglaodb.se/. The published Zheng68k dataset was downloaded from the ‘Fresh 68K PBMCs’ section at https://support.10xgenomics.com/single-cell-gene-expression/datasets (SRP073767)34. The published pancreatic datasets were downloaded from github at https://hemberg-lab.github.io/scRNA.seq.datasets/ (Baron: GSE84133, Muraro: GSE85241, Segerstolpe: E-MTAB-5061, Xin: GSE81608)35,36,37,38. The MacParland dataset was downloaded from https://www.ncbi.nlm.nih.gov/geo/ (GSE115469)50. The heart datasets were downloaded from https://data.humancellatlas.org/explore/projects/ad98d3cd-26fb-4ee3-99c9-8a2ab085e737 and https://singlecell.broadinstitute.org/single_cell/study/SCP498/transcriptional-and-cellular-diversity-of-the-human-heart (refs. 51,52). The lung dataset for COVID-19 study was downloaded from https://doi.org/10.6084/m9.figshare.11981034.v1 (ref. 53). The adult Human Cell Atlas of 15 major organs dataset was downloaded from https://www.ncbi.nlm.nih.gov/geo/ (GSE159929)54. Source Data are provided with this paper.

Code availability

The source code of the pre-processing, scBERT modelling and fine-tuning processes are freely available on Github (https://github.com/TencentAILabHealthcare/scBERT) and Zenodo (https://doi.org/10.5281/zenodo.6572672)60 with detailed instructions. The source code for the other comparison methods are publicly available (see Supplementary Table 2).

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Acknowledgements

We thank B. Jiang and Y. Ji for their valuable suggestions on model building and experimental design. We thank T. Shen for advice on the large-scale model pretraining. H.L. was supported by the National Key R&D Program of China (grant no. 2018YFC0910500), a SJTU-Yale Collaborative Research Seed Fund, and Neil Shen’s SJTU Medical Research and Key-Area Research. F.Y. was supported by Development Program of Guangdong Province (grant no. 2021B0101420005).

Author information

Author notes

  1. These authors contributed equally: Fan Yang, Wenchuan Wang, Fang Wang.

Authors and Affiliations

  1. AI Lab, Tencent, Shenzhen, China

    Fan Yang, Wenchuan Wang, Fang Wang, Yuan Fang, Duyu Tang & Jianhua Yao

  2. SJTU-Yale Joint Center for Biostatistics and Data Science, School of Life Sciences and Biotechnology, MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China

    Wenchuan Wang & Hui Lu

  3. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

    Yuan Fang

  4. Department of Immunology, Harvard Medical School, Boston, MA, USA

    Yuan Fang

  5. Department of Computer Science and Engineering, the University of Texas at Arlington, Arlington, TX, USA

    Junzhou Huang

  6. Center for Biomedical Informatics, Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Shanghai Children’s Hospital, Shanghai, China

    Hui Lu

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Contributions

F.Y. and J.Y. conceived and designed the project. W.W. developed and implemented the algorithms under the guidance of F.Y. and J.Y.. W.W. and F.W. collected the datasets. W.W., F.Y. and F.W. conducted the experiments, data analysis and method comparisons. F.Y. and W.W. drew the figures and wrote the manuscript, with the guidance of J.Y. and H.L. Y.F. and F.W. finalized the manuscript and figures. D.T. gave suggestions for the design of the Transformer architecture, and the application of the NLP technology. J.H. gave suggestions on improving the manuscript. F.Y. and F.W. revised the figures and manuscript. All of the authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Hui Lu or Jianhua Yao.

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Extended data

Extended Data Fig. 1 The system analysis of the architecture design of scBERT.

a, Performance of scBERT (with/without pre-training) measured by accuracy and F1-score on Zheng68K dataset using 5-fold cross-validation. scBERT with pre-training is trained on over 1,000,000 cells from public scRNA-seq data from PanglaoDB. In the contrast, the model weights of scBERT without pre-training are initiated randomly. Box plot shows the median (centre lines), interquartile range (hinges) and 1.5 times the interquartile range (whiskers). b, Performance evaluation on the effect of gradually removing marker genes (no deletion, deletion of 10%, deletion of 50% and deletion of 100% markers) on accuracy. Box plot shows the median (centre lines), interquartile range (hinges), and 1.5 times the interquartile range (whiskers). The green dashed line represents the best performance achieved by other cell type annotation methods with all marker genes. c, UMAP representation of alpha, beta, delta, and gamma cells from Muraro dataset coloured by gene2vec embedding (sum of 200-dimension vectors) (top) and scBERT embedding (bottom) of alpha-specific gene LOXL4. d, The heatmap of average attention matrix obtained by taking an element-wise average across all attention matrices in multi-head multi-layer Performers. Each value \(A\left( {i,j} \right)\) (i and j indicate the index of row and column) represents how much attention from gene i was paid to gene j. e, Sensitivity analysis of hyperparameters includes the number of bins (top left), the dimension of scBERT embedding vector (top right), the number of attention heads (bottom left) and the number of Performer encoder layers (bottom right).

Source data

Extended Data Fig. 2 Performance comparison between scBERT and other cell type annotation methods on intra-datasets.

a, Performance of scBERT and other automatic cell type annotation methods measured by F1-score on n = 6 datasets (Zheng68K, Baron, Muraro, Xin, Segerstolpe, and MacParland) using 5-fold cross-validation. Box plots show the median (centre lines), interquartile range (hinges), and 1.5 times the interquartile range (whiskers). b, Performance of scBERT and marker-based methods (SCINA, Garnett, scSorter) measured by accuracy (left) and F1-score (right) on Zheng68K dataset using 5-fold cross-validation. Box plot shows the median (centre lines), interquartile range (hinges), and 1.5 times the interquartile range (whiskers). c-d, Performance of scBERT and other automatic cell type annotation methods measured by accuracy (c) and F1-score (d) on n = 3 datasets (Tucker dataset, lung dataset and Human Cell Atlas dataset) using 5-fold cross-validation. Box plots show the median (centre lines), interquartile range (hinges), and 1.5 times the interquartile range (whiskers).

Source data

Extended Data Fig. 3 Heatmaps for the confusion matrices of the results on Zheng68k dataset for other comparison methods.

a, The tSNE plots show the cell type annotation results of comparison methods (scNym, SciBet, Seurat, SingleR, CellID_cell, CellID_group, scmap_cell, scmap_cluster, SCINA, Garnett, scSorter) on Zheng68K dataset. The colours indicate the cell types annotation result from each individual method.

Extended Data Fig. 4 t-SNE plots of the cell type annotation results on Zheng68K dataset (n = 68,450 cells).

a, Heatmaps for the prediction confusion matrices on Zheng68K dataset for scNym, SciBet, SingleR, CellID_group, scmap_cell, and scmap_cluster. b, Heatmaps for the prediction confusion matrices on the imbalanced dataset constructed from Zheng68K dataset for Seurat, SingleR, CellID_cell, CellID_group, scmap_cell, and scmap_cluster.

Extended Data Fig. 5 Performance comparison between scBERT and other cell type annotation methods on cross-cohort dataset and cross-organ dataset.

a, t-SNE representation of alpha, beta, delta, and gamma cells from four pancreas datasets (n = 10,220 cells). The top left t-SNE plot is coloured by the annotated cell types provided by the atlas from the original paper, meanwhile other t-SNE plots are coloured by the cell type annotation results of comparison methods (SciBet, Seurat, SingleR, CellID_cell, CellID_group, scmap_cell, and scmap_cluster). b, Performance of scBERT and other cell type annotation methods measured by accuracy (left) and F1-score (right) on datasets from 3 organs (n = 17,384) using 5-fold cross-validation. Box plots show the median (centre lines), interquartile range (hinges), and 1.5 times the interquartile range (whiskers).

Source data

Extended Data Fig. 6 The distribution of the top attention sum genes across the four cell types of the Muraro dataset.

a, UMAP representation of alpha, beta, delta, and gamma cells from Muraro dataset coloured by expression distribution of top attention sum genes that are consistent with reported marker genes for alpha, beta, delta and gamma cells, respectively. b, UMAP representation of alpha, beta, delta, and gamma cells from Muraro dataset coloured by expression distribution of top attention sum genes that have distinguishing patterns on corresponding cell types but have not been reported as markers yet.

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Yang, F., Wang, W., Wang, F. et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat Mach Intell 4, 852–866 (2022). https://doi.org/10.1038/s42256-022-00534-z

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