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<jats:title>Abstract</jats:title><jats:p>Single-cell RNA sequencing (scRNA-Seq) datasets that are produced from clinical samples are often confounded by batch effects and inter-patient variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across patients. Here we present a novel meta-clustering workflow, CIDER, based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in both simulated and real datasets. Moreover, we show that CIDER can be used to assess the biological correctness of integration in real datasets, while it does not require the existence of prior cellular annotations.</jats:p>

Original publication

DOI

10.1101/2021.03.29.437525

Type

Journal article

Publisher

Cold Spring Harbor Laboratory

Publication Date

30/03/2021