sc2DAT: workflow for targeting tumor subpopulations of single cells

The rapid increase in volume, diversity, and quality of single-cell omics profiling opens new opportunities for drug and target discovery. While there are already many workflows developed for analysis and visualization of data collected with single-cell RNA-seq, few workflows output ranked drugs and targets specific for subpopulation of single cells. Here, we present the single cells to drugs and targets (sc2DAT) workflow, a web-based software application for predicting cell surface targets and therapeutic compounds tailored to target-specific cell types automatically identified from scRNA-seq and bulk RNA-seq datasets. sc2DAT can be used to develop hypotheses about selectively eliminating malignant subpopulation of cells in cancer, or reprogram disease tissues toward a healthier phenotype using compounds from the LINCS L1000 dataset. Such compounds are hypothesized to either reverse or mimic the direction of changes in gene expression signatures, restoring the subpopulation of cells towards a healthier phenotype.

sc2DAT is available from: https://sc2dat.maayanlab.cloud; the source code is available from: https://github.com/MaayanLab/sc2DAT.