Debajyoti Sinha1,2, Pradyumn Sinha3, Ritwik Saha3, Sanghamitra Bandyopadhyay1, Debarka Sengupta4. 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.
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.
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.