scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier

Ajita Shree, Musale Krushna Pavan and Hamim Zafar ()
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Ajita Shree: Indian Institute of Technology Kanpur
Musale Krushna Pavan: Indian Institute of Technology Kanpur
Hamim Zafar: Indian Institute of Technology Kanpur

Nature Communications, 2023, vol. 14, issue 1, 1-19

Abstract: Abstract Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER ( https://github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.

Date: 2023
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DOI: 10.1038/s41467-023-43590-8

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