The Rosetta Project: Translating Data Across Single-Cell Technologies to Define Human Cell Types
To map connections between a cell’s morphology and its transcriptome towards uncovering quantitative and predictive links among the various modalities.
Results & Resources
In this project, the Carpenter-Singh lab introduced a benchmarking framework to define, formulate, and evaluate applications utilizing multi-modal data, in the context of cellular morphology and gene expression modalities. The team gathered and provided a collection of parallel morphology (captured by cell-painting assay) and gene-expression (captured by L1000 assay) data from four genetic and chemical perturbation datasets. The data resource is publicly available at https://broad.io/rosetta/.