Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

Frédéric Pont (1, 2) , Marie Tosolini (1, 3) , Qing Gao (4) , Marion Perrier (1, 2) , Miguel Madrid-Mencía (1, 2) , Tse shun Huang (4) , Pierre Neuvial (5) , Maha Ayyoub (1, 2) , Kristopher Nazor (4) , Jean-Jacques Fournié (1, 2)

1 CRCT - Centre de Recherches en Cancérologie de Toulouse
2 IUCT Oncopole - UMR 1037 - Institut Universitaire du Cancer de Toulouse - Oncopole
3 Service Hématologie - IUCT-Oncopole [CHU Toulouse]
4 Biolegend
5 IMT - Institut de Mathématiques de Toulouse UMR5219

The development of single-cell transcriptomic technologies yields large datasets comprising multi-modal informations, such as transcriptomes and im-munophenotypes. Despite the current explosion of methods for pre-processing and integrating multi-modal single-cell data, there is currently no user-friendly software to display easily and simultaneously both immunophenotype and transcriptome-based UMAP/t-SNE plots from the pre-processed data. Here, we introduce Single-Cell Virtual Cytome-ter, an open-source software for flow cytometry-like visualization and exploration of pre-processed multi-omics single cell datasets. Using an original CITE-seq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of tran-scriptomes and epitopes of functional maturation in human peripheral T lymphocytes. So this free and open-source algorithm constitutes a unique resource for biologists seeking for a user-friendly analytic tool for multimodal single cell datasets.

Domaines: Bio-informatique [q-bio.QM]

Frédéric Pont, Marie Tosolini, Qing Gao, Marion Perrier, Miguel Madrid-Mencía, et al.. Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets. NAR Genomics and Bioinformatics, 2020, 2 (2), ⟨10.1093/nargab/lqaa025⟩. ⟨hal-02861405⟩