CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation

Our latest paper, based on the thesis work of former DPhil student Zhiyuan, has been published in Genome Biology. CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation describes a meta-clustering workflow 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.