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scIDST (single-cell Identification of Disease progressive STages)

scIDST is designed to identify progressive disease states of individual cells from single-cell/nuclei RNA-seq (scRNA-seq) by weakly-supervised deep learning approach (Wehbe et al., submitted)

Organoid Atlas (UCSC Cell Browser)

We implemented the web-based interface together with University of California Santa Cruz (UCSC) Cell Browser and provided a powerful interactive tool to explore all the composited scRNA-seq datasets that provide the identity of cell clusters, reference genes for each cluster, composition of each datasets, and aging of organoids among other important information. The raw UMI count matrix combining all described single-cell transcriptome profiles is available at our Mendeley repository (Tanaka et al., Cell Report, 2020).