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 interfacing with multiple programming languages viz. R, python and C, hindering seamless installation and portability. Here we present dropClust2, a complete R package thats 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.\n\nAvailability and implementationdropClust2 is freely available at https://debsinha.shinyapps.io/dropClust/ as an online web service and at https://github.com/debsin/dropClust as an R package.