scODIN is more accurate and faster than any method published so far, and for the first time able to identify transitional cell states (e.g. the state between a naïve cell and a central memory cell) and dual cell states (e.g. Th1/Th17 or Tfh/Th17), thus enabling discovery of new biology.

Struggling with immune cell annotations in scRNAseq data? If so, check out our new preprint. The scODIN algorithm uses an "informed holistic" approach combining expert knowledge with machine learning to rapidly assign cell type IDs to large scRNA-seq dataset. www.biorxiv.org/content/10.1...

Excited to see our new single cell immunology tool out now in @naturecomms.bsky.social. We made "Single cell suppression profiling of human regulatory T cells" to generate a human model to screen for cell-type specific effects on all immune cells simultaneously.
www.nature.com/articles/s41...