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Ritwik Saha

dblp:261/4867 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 67% Computational science and engineering · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › omics data analysis
batch effect correction		 0.412020
Improved dropClust R package with integrative analysis support for scRNA-seq data · Bioinform. 2020
Computational science and engineering
locality-sensitive hashing		 0.412020
Improved dropClust R package with integrative analysis support for scRNA-seq data · Bioinform. 2020
Bioinformatics and computational biology › single-cell analysis
single-cell transcriptomics		 0.412020
Improved dropClust R package with integrative analysis support for scRNA-seq data · Bioinform. 2020

Methods — techniques the papers use, named apart from their topics

locality-sensitive hashing · 0.4clustering · 0.4
YearPublicationVenuePosition
2020 Improved dropClust R package with integrative analysis support for scRNA-seq data
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.
Debajyoti Sinha, Pradyumn Sinha, Ritwik Saha, Sanghamitra Bandyopadhyay, Debarka Sengupta
 Bioinform.3