Multi-batch single-cell comparative atlas construction by deep learning disentanglement

Allen W. Lynch, Myles Brown and Clifford A. Meyer ()
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Allen W. Lynch: Harvard Medical School
Myles Brown: Dana-Farber Cancer Institute
Clifford A. Meyer: Dana-Farber Cancer Institute

Nature Communications, 2023, vol. 14, issue 1, 1-22

Abstract: Abstract Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL’s capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched data.

Date: 2023
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DOI: 10.1038/s41467-023-39494-2

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