ShinyCell: simple and sharable visualization of single-cell gene expression data.

MOTIVATION: As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. RESULTS: In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customized in order to maximize their usability and can be easily uploaded to online platforms to facilitate wider access to published data. AVAILABILITY AND IMPLEMENTATION: ShinyCell is available at https://github.com/SGDDNB/ShinyCell and https://figshare.com/projects/ShinyCell/100439. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GeneSwitches: ordering gene expression and functional events in single-cell experiments.

SUMMARY: Emerging single-cell RNA-sequencing data technologies has made it possible to capture and assess the gene expression of individual cells. Based on the similarity of gene expression profiles, many tools have been developed to generate an in silico ordering of cells in the form of pseudo-time trajectories. However, these tools do not provide a means to find the ordering of critical gene expression changes over pseudo-time. We present GeneSwitches, a tool that takes any single-cell pseudo-time trajectory and determines the precise order of gene expression and functional-event changes over time. GeneSwitches uses a statistical framework based on logistic regression to identify the order in which genes are either switched on or off along pseudo-time. With this information, users can identify the order in which surface markers appear, investigate how functional ontologies are gained or lost over time and compare the ordering of switching genes from two related pseudo-temporal processes. AVAILABILITY: GeneSwitches is available at https://geneswitches.ddnetbio.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.