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scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
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scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
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Journal Article
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
2019
Overview
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 reveals gene expression programs for rare populations of malaria parasites. Our framework is widely applicable to integration challenges in other domains.
Publisher
BioMed Central,Springer Nature B.V,BMC
Subject
/ Animal Genetics and Genomics
/ Animals
/ Biomedical and Life Sciences
/ Cells
/ Datasets
/ genome
/ Genomics
/ Humans
/ Islets of Langerhans - cytology
/ malaria
/ Method
/ Microbial Genetics and Genomics
/ Plasmodium falciparum - cytology
/ Plasmodium falciparum - genetics
/ Principal Component Analysis
/ Software
/ species
