velociraptor, an R toolkit for single-cell velocity computation Kevin Christophe Rue-Albrecht,Charlotte Soneson,Michael B. Stadler University of Oxford Abstract RNA velocity has become a popular computational method to investigate dynamical signals in single-cell RNA-seq data sets and predict the future state of individual cells from the analysis of spliced and unspliced RNA-seq reads. While some of the most popular software for estimating RNA velocity are available exclusively as Python packages, the reticulate (CRAN) and basilisk (Bioconductor) packages allow users to run Python code and interact with Python data structures from within R sessions.
scDataviz: single cell dataviz and downstream analyses Kevin Blighe Clinical Bioinformatics Research Ltd. Abstract In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a ‘plug and play’ feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz.
SCArray – Large-scale single-cell RNA-seq data manipulation with GDS files Xiuwen Zheng Genomics Research Center, AbbVie Inc., 1 North Waukegan Rd., North Chicago, IL 60064 Abstract The technology development and decreasing costs of single-cell RNA-seq are leading to larger and larger numbers of cells assayed per experiment, and the scalability leveraging on-disk data processing remains an important issue to address. Here I introduce a new Bioconductor package SCArray, and it provides large-scale single-cell RNA-seq data manipulation using Genomic Data Structure (GDS) files.
A graphical model for single-cell RNA-seq data Davide Risso University of Padova Abstract Recent technological advances in molecular biology allow the sequencing of RNA from individual cells (single-cell RNA-seq). Typically, the genes whose expressions are differential between cell states or across experimental conditions are identified with univariate (gene-wise) models. However, it may be beneficial to explicitly account for gene dependencies in multivariate statistical models. In this talk, I will introduce a graphical model for single-cell RNA-seq and show how to use it to explore the dynamics of transcription factors in development.