dropClust2: An R package for resource efficient analysis of large scale single cell RNA-Seq data

DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. It makes ingenious use of structure persevering sampling and modality based principal component selection to rescue minor cell types. Existing implementation of dropClust involves...

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Bibliographic Details
Published inbioRxiv
Main Authors Sinha, Debajyoti, Sinha, Pradyumn, Saha, Ritwik, Bandyopadhyay, Sanghamitra, Sengupta, Debarka
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 03.04.2019
Subjects
Ribonucleic acid
RNA
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Summary:DropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. It makes ingenious use of structure persevering sampling and modality based principal component selection to rescue minor cell types. Existing implementation of dropClust involves interfacing with multiple programming languages viz. R, python and C, hindering seamless installation and portability. Here we present dropClust2, a complete R package that's not only fast but also minimally resource intensive. DropClust2 features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets. Footnotes * https://github.com/debsin/dropClust
DOI:10.1101/596924