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

• Published in: Genome Biology Vol. 22; no. 1; p. 337
• Main Authors: Hu, Zhiyuan, Ahmed, Ahmed A., Yau, Christopher
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.12.2021; BMC
• Subjects: Algorithms; Animal Genetics and Genomics; Benchmarking; Bioinformatics; Biomedical and Life Sciences; Breast Neoplasms; Cider; ciders; Cluster Analysis; Clustering; Confounding (Statistics); Confounding factors; data collection; Datasets; Evolutionary Biology; Gene expression; genome; Human Genetics; Humans; Hypotheses; Integration; Life Sciences; Method; Microbial Genetics and Genomics; Plant Genetics and Genomics; RNA-Seq; sequence analysis; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Single-cell RNA-Seq; Single-cell RNA-Seq; Clustering; Confounding factors
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-021-02561-2

Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across samples. Here, we present CIDER, 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.