CReSCENT: CanceR Single Cell ExpressioN Toolkit

Suluxan Mohanraj; J Javier Díaz-Mejía; Martin D Pham; Hillary Elrick; Mia Husić; Shaikh Rashid; Ping Luo; Prabnur Bal; Kevin Lu; Samarth Patel; Alaina Mahalanabis; Alaine Naidas; Erik Christensen; Danielle Croucher; Laura M Richards; Parisa Shooshtari; Michael Brudno; Arun K Ramani; Trevor J Pugh

doi:10.1093/nar/gkaa437

CReSCENT: CanceR Single Cell ExpressioN Toolkit

Nucleic Acids Res. 2020 Jul 2;48(W1):W372-W379. doi: 10.1093/nar/gkaa437.

Authors

Suluxan Mohanraj¹, J Javier Díaz-Mejía¹, Martin D Pham², Hillary Elrick², Mia Husić², Shaikh Rashid², Ping Luo¹, Prabnur Bal², Kevin Lu², Samarth Patel², Alaina Mahalanabis², Alaine Naidas³, Erik Christensen³, Danielle Croucher¹, Laura M Richards¹, Parisa Shooshtari^{3

4

5}, Michael Brudno^{2

6

7}, Arun K Ramani², Trevor J Pugh^{1

5

8}

Affiliations

¹ Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 0A3, Canada.
² Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
³ University of Western Ontario, London, ON N6A 3K7, Canada.
⁴ Children's Health Research Institute, London, ON N6C 2R5, Canada.
⁵ Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada.
⁶ Techna Institute, University Health Network, Toronto, ON M5G 0A3, Canada.
⁷ Department of Computer Science, University of Toronto, Toronto, ON M5S 3K1, Canada.
⁸ Department of Medical Biophysics, University of Toronto, Toronto, ON M5S 3K1, Canada.

Abstract

CReSCENT: CanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Neoplasms / genetics*
Neoplasms / immunology
RNA-Seq / methods*
Single-Cell Analysis / methods*
Software*
T-Lymphocytes / metabolism