Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

AbstractSingle-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.

Original publication




Journal article

Publication Date