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

Author:

Sinha Debajyoti12,Sinha Pradyumn3,Saha Ritwik3,Bandyopadhyay Sanghamitra1,Sengupta Debarka456

Affiliation:

1. SyMeC Data Center, Indian Statistical Institute, Kolkata 700108, India

2. Department of Computer Science & Engineering, University of Calcutta, Kolkata 700098, India

3. Department of Computer Science & Engineering, Delhi Technological University, New Delhi 110042, India

4. Department of Computer Science & Engineering

5. Department of Computational Biology, Center for Artificial Intelligence, Indraprastha Institute of Information Technology, New Delhi 110020, India

6. Center for Artificial Intelligence, Indraprastha Institute of Information Technology, New Delhi 110020, India

Abstract

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.

Funder

INSPIRE Faculty

Dept. of Sci. and Tech., Govt. of India

SyMeC Project

Dept. of BioTech., Govt. of India.

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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