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An Interactive Tool for Convenient Visual Analysis and Exploration of Single-Cell Data

 
Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression of cells at the single cell level. Analyzing scRNA-seq data allows analysts to discover and characterize cell types. In most cell type estimation processes, analysts need to identify differentially expressed genes in all cell populations and need to know which genes are associated with which cell types. This process is cumbersome and takes a long time. To solve this problem, we developed a visual analysis tool that quickly finds differentially expressed genes in each cell population and gives references for better cell type estimation. Our tool makes single-cell analysis more convenient. We proved the convenience of our tool through a user study which compares ours with existing tools.