An interpretable framework for clustering single-cell RNA-Seq datasets.

ABSTRACT:

Background

With the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering.

Results

In this work, we present DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets that addresses both the clustering interpretability and clustering subjectivity issues. DendroSplit offers a novel perspective on the single-cell RNA-Seq clustering problem motivated by the definition of "cell type", allowing us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. We analyze several landmark s

SUBMITTER: Zhang JM 

PROVIDER: S-EPMC5845381 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature