Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequenc-ing data analysis
Tamim AbdelaaliD, Jeroen EggermontiD, Thomas HölltiD, Ahmed Essam MahfouziD, Marcel J. T. ReindersiD, Boudewijn P. F. LelieveldtiD
Abstract
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. AvailabilityCytosplore-Transcriptomics can be freely downloaded from transcriptomics.cytosplore.org Contactb.p.f.lelieveldt@lumc.nl
02 /Specification & Classification
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