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

Cytosplore-Transcriptomics: A Scalable Interactive Framework for Single-Cell RNA Sequencing Data Analysis teaser image

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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Citation

Tamim Abdelaal, Jeroen Eggermont, Thomas Höllt, Ahmed Mahfouz, Marcel Reinders, and Boudewijn Lelieveldt. Cytosplore-Transcriptomics: A Scalable Interactive Framework for Single-Cell RNA Sequencing Data Analysis. bioRxiv, 2020.

BibTeX

@article{ bib:2020_cyto_transcriptomics,
author = {Tamim Abdelaal and Jeroen Eggermont and Thomas H{\"o}llt and Ahmed Mahfouz and Marcel Reinders and Boudewijn Lelieveldt},
title = { Cytosplore-Transcriptomics: A Scalable Interactive Framework for Single-Cell RNA Sequencing Data Analysis },
year = { 2020 },
doi = { 10.1101/2020.12.11.421883 },
}