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Flexible Statistical Methods for Differential Analysis of Single-Cell Datasets

Focus Cell Types

Project Goal

To develop benchmarks and flexible statistical methods and software tools for discovery in high throughput single-cell datasets.

Results & Resources

This project developed a flexible system for differential expression discovery in multi-sample multi-condition single-cell RNA-seq (scRNA-seq) experiments. They created a simulation to mimic real scRNA-seq datasets according to a number of reasonable features. Using this simulation, they were able to compare a set of current methods available across a wide range of test cases, as described in their preprint.


Lead Investigator

Mark Robinson
Mark Robinson