UC Riverside

Motivation

Single-cell RNA sequencing technologies facilitate the characterization of transcriptomic landscapes in diverse species, tissues and cell types with unprecedented molecular resolution. In order to better understand animal development, physiology, and pathology, unsupervised clustering analysis is often used to identify relevant cell populations. Although considerable progress has been made in terms of clustering algorithms in recent years, it remains challenging to evaluate the quality of the inferred single-cell clusters, which can greatly impact downstream analysis and interpretation.

Results

We propose a bioinformatics tool named Phitest to analyze the homogeneity of single-cell populations. Phitest is able to distinguish between homogeneous and heterogeneous cell populations, providing an objective and automatic method to optimize the performance of single-cell clustering analysis.

Availability and implementation

The PhitestR package is freely available on both Github (https://github.com/Vivianstats/PhitestR) and the Comprehensive R Archive Network (CRAN). There is no new genomic data associated with this article. Published data used in the analysis are described in detail in the Supplementary Data.

Supplementary information

Supplementary data are available at Bioinformatics online.