clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets
Authors: Elizabeth Purdom
Sandrine Dudoit
Liam Purvis
John Ngai
Russell B Fletcher
Davide Risso
Diya Das
Journal: PLoS Computational Biology (2018)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: Gene clustering; Sample clustering; Subtypes; Single-cell transcriptome sequencing; RNA-Seq
Affiliations: Division of Biostatistics and Epidemiology, Weill Cornell Medicine, New York, New York, United States of America., Department of Statistics, University of California - Berkeley, Berkeley, California, United States of America., Department of Molecular and Cell Biology, University of California - Berkeley, Berkeley, California, United States of America., Berkeley Institute for Data Science, UC University of California - Berkeley, Berkeley, California, United States of America., Division of Epidemiology and Biostatistics, University of California - Berkeley, Berkeley, California, United States of America.

Abstract:  Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function. 

Retracted: No

PubPeer comments:
 Risso et al. introduce a consensus clustering method that uses a hierarchical clustering on the dissimilarity matrix, defined as 1 - proportions of times two resampled genes are clustered together. With the only difference of using a hierarchical clustering, this method overlaps significantly with that introduced by Monti et al. Unfortunately, the authors do not provide any comparison between their method and the one introduced by Monti et al. but they only cite that article in the introduction. Furthermore, the usage of a hierarchical clustering does not provide a method to determine the optimal number of clusters, as done with the Monti et al. method., and it is subject to different results based on different linkages. ,