Cytosplore-Transcriptomics: a Scalable Interactive Framework for Single-Cell RNA Sequencing Data Analysis

Tamim Abdelaal, Jeroen Eggermont, Thomas Höllt, Ahmed Mahfouz, Marcel Reinders, Boudewijn P.F. Lelieveldt
bioRxiv - 2020
Download the publication : 2020_cyto_transcriptomics.pdf [1.6Mo]  
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.

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@Article { AEHMRL20,
  author       = "Abdelaal, Tamim and Eggermont, Jeroen and H\öllt, Thomas and Mahfouz, Ahmed and Reinders, Marcel and
                  Lelieveldt, Boudewijn P.F.",
  title        = "Cytosplore-Transcriptomics: a Scalable Interactive Framework for Single-Cell RNA Sequencing Data Analysis",
  journal      = "bioRxiv",
  year         = "2020",
  doi          = "10.1101/2020.12.11.421883",
  url          = ""