annotating (potentially millions of) cells manually, which results in cells with inconsistent terms and labelings between groups. •This approach cannot scale. We need a solution for creating comprehensive references with a standardized nomenclature for all species. •There's no medium for researchers to compare annotations across studies, potentially resolving con fl icting results. •There’s no central location to access annotations used in publications. Motivation
Mary Futey Nick Akhmetov Anush Boyakhchyan Tigran Markosjan Konstantin Boyandin Uğur Bayindir David Osumi-Sutherland Pavel Istomin Dennis Bolgov Felix Fischer Mo Lotfollahi David Fischer Evan Biederstedt
and store annotations • Infrastructure to accumulate, share, and analyze annotation terms with associated molecular signatures to interpret cellular identities • Encourage researchers to converge upon consensus nomenclature • •
accession IDs for published cell annotations • Enable automated cell annotation service of cell types and cell states • Reference for cell identities based on molecular signatures across species
2. Upload already annotated data 3. Collaboratively edit and save 4. Publish version (with DOI) 5. Downloadable results 6. Browse / Search Current Release https://celltype.info/
2. Upload already annotated data 3. Collaboratively edit and save 4. Publish version (with DOI) 5. Downloadable results 6. Browse / Search Current Release https://celltype.info/
annotations • Publication: Version • Datasets: Cell annotations with molecular data • Cell Label: Term associated with a cell or molecular subpopulation.
“CD8+ T cell” is a subset of “T Lymphocyte” • Relationships between annotations • Why? Discover patterns of how the community is naming these entities.
data in ontologies. • sfaira.models: supervised models of cell types • atlas-based label transfer via query- reference projection (scArches, CellTypist, Azimuth, Human Lung Cell Atlas HLCA, …) e.g.: deploy models fi t per organ to annotated organ of query sample Fischer, Dony, et. al., “sfaira”, 2020 Predictive modeling of cell types