bioRxiv

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

Jeroen Eggermont, Thomas Höllt, Tamim Abdelaal, Ahmed Mahfouz, Marcel Reinders, and Boudewijn P. F. Lelieveldt

Publisher Paper Cite More

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.

More Information

Citation

BibTex

@article{bib:eggermont:2020,
    author       = { Eggermont, Jeroen and Höllt, Thomas and Abdelaal, Tamim 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 },
    pages        = { 1--20 },
    doi          = { 10.1101/2020.12.11.421883 },
    url          = { https://publications.graphics.tudelft.nl/papers/148 },
}