RT Journal Article
SR Electronic
T1 Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequencing data analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.12.11.421883
DO 10.1101/2020.12.11.421883
A1 Tamim Abdelaal
A1 Jeroen Eggermont
A1 Thomas Höllt
A1 Ahmed Mahfouz
A1 Marcel J.T. Reinders
A1 Boudewijn P.F. Lelieveldt
YR 2020
UL http://biorxiv.org/content/early/2020/12/11/2020.12.11.421883.abstract
AB The ever-increasing number of analyzed cells in Single-cell RNA sequencing (scRNA-seq) experiments imposes several challenges on the data analysis. Current analysis methods lack scalability to large datasets hampering interactive visual exploration of the data. We present Cytosplore-Transcriptomics, a framework to analyze scRNA-seq data, including data preprocessing, visualization and downstream analysis. At its core, it uses a hierarchical, manifold preserving representation of the data that allows the inspection and annotation of scRNA-seq data at different levels of detail. Consequently, Cytosplore-Transcriptomics provides interactive analysis of the data using low-dimensional visualizations that scales to millions of cells.Availability Cytosplore-Transcriptomics can be freely downloaded from transcriptomics.cytosplore.orgContact b.p.f.lelieveldt{at}lumc.nlCompeting Interest StatementThe authors have declared no competing interest.