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Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders

Nature Machine Intelligence volume 2, pages 800–809 (2020)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Recent advances in single-cell RNA sequencing have driven the simultaneous measurement of the expression of thousands of genes in thousands of single cells. These growing datasets allow us to model gene sets in biological networks at an unprecedented level of detail, in spite of heterogeneous cell populations. Here, we propose a deep neural network model that is a hybrid of matrix factorization and variational autoencoders, which we call restricted latent variational autoencoder (resVAE). The model uses weights as factorized matrices to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. This artificial neural network model seamlessly integrates functional gene set inference, experimental covariate effect isolation, and static gene identification, which we conceptually demonstrate here for four single-cell RNA sequencing datasets.

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Fig. 1: resVAE network architecture.
Fig. 2: Performance metrics.
Fig. 3: Gene set and housekeeping gene inference from C. elegans single-cell RNA sequencing.
Fig. 4: Upstream regulator identification in mouse testis data.
Fig. 5: Batch effect isolation in human pancreatic islet data from different sources.

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Data availability

All example datasets were used in previously published studies. The C. elegans dataset was downloaded according to the instructions provided at http://atlas.gs.washington.edu/worm-rna/docs/#use-case-1-expression-pattern-of-a-gene-of-interest23. The testis single-cell RNA sequencing data with the accession number GSE104556 was downloaded from GEO29. The set of pancreas single-cell RNA sequencing datasets including annotations was downloaded according to the instructions on https://satijalab.org/seurat/v3.0/integration.html. The peripheral blood mononuclear cell (PBMC) dataset was obtained as outlined on https://satijalab.org/seurat/v3.1/immune_alignment.html. The individual datasets can be accessed on GEO (GSE81076: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81076, GSE85241: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85241, GSE86469: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE86469, GSE96583: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96583) and SRA (E-MTAB-5061: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5061/)33,34,35,36,39.

Code availability

The code for our implementation of the resVAE algorithm is available on GitHub (https://github.com/lab-conrad/resVAE) and Zenodo (https://doi.org/10.5281/zenodo.4088371)44. A CodeOcean capsule together with a minimal example dataset is available at https://codeocean.com/capsule/2076269/tree/v145.

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Acknowledgements

This publication is part of the Human Cell Atlas at http://www.humancellatlas.org/publications.

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Author notes

  1. These authors contributed equally: Soeren Lukassen, Foo Wei Ten

Authors and Affiliations

  1. Charité—Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany

    Soeren Lukassen, Foo Wei Ten, Lukas Adam, Roland Eils & Christian Conrad

  2. Berlin Institute of Health (BIH), Berlin, Germany

    Soeren Lukassen, Foo Wei Ten, Lukas Adam, Roland Eils & Christian Conrad

  3. Health Data Science Unit, University Hospital Heidelberg, Heidelberg, Germany

    Roland Eils

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Contributions

S.L. and C.C. conceived this study. S.L. implemented the algorithm. S.L. and F.W.T. analysed the data. L.A. assisted with the implementation and data analysis. C.C. and R.E. supervised the study.

Corresponding authors

Correspondence to Roland Eils or Christian Conrad.

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The authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 VRC dependence on gene set size.

VRC values for gene sets comprised of randomly sampled genes, tested on the C. elegans dataset. The number of sampled genes is displayed on the x axis.

Extended Data Fig. 2 Additional performance metrics.

Performance metrics for the mouse testis (a), human pancreas (b) and PBMC (c) datasets. The VRC, mean squared reconstruction error, ARI of cluster label inference, and Spearman correlation coefficient between genes in the input and in the weight mappings are shown. Calculations were performed as in Fig. 2.

Extended Data Fig. 3 Hyperparameter tuning.

VRC values for different hyperparameters in latent and decoder neuron layers, tested on the C. elegans dataset. Abbreviations: dec_reg: decoder activity regularizer; lat_scale: Latent scale; dec_bias: decoder bias. Boxes indicate the inner-quartile range (IQR), whiskers extend to 1.5 times the IQR. All outliers shown. Horizontal lines indicate the median.

Extended Data Fig. 4 Additional gene set enrichments for C. elegans cell types I.

Pathway and gene set enrichment P values for cell type-specific gene sets identified from the cell type to gene mapping obtained by resVAE for the C. elegans dataset.

Extended Data Fig. 5 Additional gene set enrichments for C. elegans cell types II.

Pathway and gene set enrichment P values for cell type-specific gene sets identified from the cell type to gene mapping obtained by resVAE for the C. elegans dataset.

Extended Data Fig. 6 Comparison of GO term enrichments across cell types.

Pathway and GO term fold enrichment for the top three enriched pathways per cell type (rows) across all cell types (columns) in the C. elegans dataset.

Extended Data Fig. 7 Comparison of motif enrichment across cell types and gene sets.

Motif enrichment P values for all motifs identified as the top enriched in any gene set/neuron (rows) across all gene sets/neurons (columns) in the mouse testis dataset.

Extended Data Fig. 8 GO term enrichment comparison after batch effect isolation.

Heatmap of Spearman’s rank correlation coefficient of the similarity of GO term enrichments in the cell-type-specific gene sets for the human pancreas dataset.

Extended Data Fig. 9 Gene set enrichment and covariate isolation benchmarking on PBMCs.

a–f, Metascape enrichment results for interferon stimulated and unstimulated PBMCs. a–d, Cell-type-specific gene set enrichment. e,f, Treatment-specific gene set enrichment. Asterisk indicates the term name has been shortened for display purposes. g, ARI values for benchmarking of resVAE and Seurat batch effect isolation. ARIs for cell type labels and treatment after batch correction using Seurat (blue colored bars), ARIs for cell type and treatment using resVAE covariate isolation on an uncorrected top 2000 gene matrix (purple colored bars), ARI for Seurat-corrected matrix without providing treatment labels (gray coloured bar), ARI for cell type using resVAE on the uncorrected matrix without treatment labels (black coloured bar). h, resVAE clearly showed Erythrocytes-related term for the Erythrocytes cluster, whereas Seurat did not yield obvious terms while still being cluttered by Stimulated-related terms. Meanwhile, resVAE also performed competitively against Seurat in the Stimulated gene set enrichment terms, showing comparable results. Gene set enrichments for other cell types that did not show obvious or significant differences between resVAE and Seurat were omitted here as they do not add additional information.

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Lukassen, S., Ten, F.W., Adam, L. et al. Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders. Nat Mach Intell 2, 800–809 (2020). https://doi.org/10.1038/s42256-020-00269-9

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