PT  - JOURNAL ARTICLE
AU  - Tamim Abdelaal
AU  - Jeroen Eggermont
AU  - Thomas Höllt
AU  - Ahmed Mahfouz
AU  - Marcel J.T. Reinders
AU  - Boudewijn P.F. Lelieveldt
TI  - Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequencing data analysis
AID  - 10.1101/2020.12.11.421883
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.12.11.421883
4099  - http://biorxiv.org/content/early/2020/12/11/2020.12.11.421883.short
4100  - http://biorxiv.org/content/early/2020/12/11/2020.12.11.421883.full
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.