Abstract: Detailed exploration of large transcriptomics datasets, increasingly available at single-cell resolution, is a time-consuming task which often requires the complementary skill sets of data analysts and experimental scientists to complete analyses and interpretation in an efficient manner. The iSEE (Interactive SummarizedExperiment Explorer) R/Bioconductor software package (https://bioconductor.org/packages/iSEE/), built on the shiny R framework, provides a general-purpose graphical interface for exploring any rectangular dataset with additional sample and feature annotations, such as single-cell RNA-seq data. Users can create, configure, and interact with the iSEE interface, enabling quick iterations of data visualization. This facilitates generation of new scientific hypotheses and insights into biological phenomena, and empowers a wide range of researchers to explore their data in depth. iSEE also guarantees the reproducibility of the analysis, by reporting the code generating all the output elements as well as the layout and configuration of the user interface. The combination of interactivity and reproducibility makes iSEE an ideal candidate to bridge and complement the expertise of researchers, who are able to design flexible, accessible, and robust dashboards that can also be directly shared and deployed in collaborative contexts - connecting large data collections to broad audiences, thus further increasing the value of generated research data.
Speaker: Charlotte Soneson, Kevin Rue-Albrecht, Federico Marini, Aaron Lun
Title: Interactive and collaborative exploration of large-scale transcriptomics data with iSEE
Abstract_Sanitzed: Detailed exploration of large transcriptomics datasets, increasingly available at single-cell resolution, is a time-consuming task which often requires the complementary skill sets of data analysts and experimental scientists to complete analyses and interpretation in an efficient manner. The iSEE (Interactive SummarizedExperiment Explorer) R/Bioconductor software package (https//bioconductor.org/packages/iSEE/), built on the shiny R framework, provides a general-purpose graphical interface for exploring any rectangular dataset with additional sample and feature annotations, such as single-cell RNA-seq data. Users can create, configure, and interact with the iSEE interface, enabling quick iterations of data visualization. This facilitates generation of new scientific hypotheses and insights into biological phenomena, and empowers a wide range of researchers to explore their data in depth. iSEE also guarantees the reproducibility of the analysis, by reporting the code generating all the output elements as well as the layout and configuration of the user interface. The combination of interactivity and reproducibility makes iSEE an ideal candidate to bridge and complement the expertise of researchers, who are able to design flexible, accessible, and robust dashboards that can also be directly shared and deployed in collaborative contexts - connecting large data collections to broad audiences, thus further increasing the value of generated research data.