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Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation by Renjing Liu; Kun Qian; Xinwei He; Hongwei Li is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract ## Background The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity. ## Results To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcom

Who reads Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation?

Author

Renjing Liu; Kun Qian; Xinwei He; Hongwei Li

Publisher

BioMed Central

Published

2024

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)