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

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 sc

SUBMITTER: Shree A 

PROVIDER: S-EPMC10682386 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature