scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data

Johansen, N.; Quon, G.

doi:10.1101/504944

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bioRxiv · 10.1101/504944

scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data

Johansen, N.·Quon, G.

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Abstract

scRNA-seq dataset integration occurs in different contexts, such as the identification of cell type-specific differences in gene expression across conditions or species, or batch effect correction. We present scAlign, an unsupervised deep learning method for data integration that can incorporate partial, overlapping or a complete set of cell labels, and estimate per-cell differences in gene expression across datasets. scAlign performance is state-of-the-art and robust to cross-dataset variation in cell type-specific expression and cell type composition. We demonstrate that scAlign identifies a rare cell population likely to drive malaria transmission. Our framework is widely applicable to integration challenges in other domains.

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Johansen, N., Quon, G.. 2018-12-22. scAlign: a tool for alignment, integration and rare cell identification from scRNA-seq data. https://doi.org/10.1101/504944

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