![]() | Cytosplore-Transcriptomics: a Scalable Interactive Framework for Single-Cell RNA Sequencing Data Analysis |
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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BibTex references
@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 = "http://graphics.tudelft.nl/Publications-new/2020/AEHMRL20" }
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