Cytosplore-Transcriptomics: a scalable inter-active framework for single-cell RNA sequenc-ing data analysis
Abdelaal, T.; Eggermont, J.; Hollt, T.; Mahfouz, A.; Reinders, M.; Lelieveldt, B.
Show 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
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Cell Layers: Uncovering clustering structure and knowledge in unsupervised single-cell transcriptomic analysis 95%
- Sparse dimensionality reduction for analyzing single-cell-resolved interactions 95%
- AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources 94%
Similar papers in this journal
Similar papers in this journal
- Giotto, a toolbox for integrative analysis and visualization of spatial expression data 96%
- sCCIgen: A high-fidelity spatially resolved transcriptomics data simulator for cell-cell interaction studies. 94%
- Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.