Selecting single cell clustering parameter values using subsampling-based robustness metrics

BMC Bioinformatics

Vilas Menon¹  Ariel J. Levine²  Ryan B. Patterson-Cross² 

[1] Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York City, NY, USA;Spinal Circuits and Plasticity Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA;

关键词: Single cell RNAseq;    Parameter selection;    Clustering;    Resolution;   

DOI  :  10.1186/s12859-021-03957-4

来源: Springer

【 摘 要 】

BackgroundGenerating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems.ResultsHere, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality.ConclusionchooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

【 授权许可】

CC BY