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

ABSTRACT:

Background

Generating 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

SUBMITTER: Patterson-Cross RB 

PROVIDER: S-EPMC7852188 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature