Improved dropClust R package with integrative analysis support for scRNA-seq data

Debajyoti Sinha; Pradyumn Sinha; Ritwik Saha; Sanghamitra Bandyopadhyay; Debarka Sengupta

doi:10.1093/bioinformatics/btz823

Improved dropClust R package with integrative analysis support for scRNA-seq data

Bioinformatics. 2019 Nov 6:btz823. doi: 10.1093/bioinformatics/btz823. Online ahead of print.

Authors

Debajyoti Sinha^{1

2}, Pradyumn Sinha³, Ritwik Saha³, Sanghamitra Bandyopadhyay¹, Debarka Sengupta⁴

Affiliations

¹ SyMeC Data Center, Indian Statistical Institute, Kolkata, India.
² Department of Computer Science & Engineering, University of Calcutta, Kolkata, India.
³ Department of Computer Science & Engineering, Delhi Technological University, Delhi, India.
⁴ Department of Computer Science & Engineering, Department of Computational Biology, Center for Artificial Intelligence, Indraprastha Institute of Information Technology, New Delhi, India.

PMID: 31693086
DOI: 10.1093/bioinformatics/btz823

Abstract

Summary: DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. Here we present the improved dropClust, a complete R package that is, fast, interoperable and minimally resource intensive. The new dropClust features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets.

Availability and implementation: dropClust is freely available at https://github.com/debsin/dropClust as an R package. A lightweight online version of the dropClust is available at https://debsinha.shinyapps.io/dropClust/.

Supplementary information: Supplementary data are available at Bioinformatics online.