publish fi gures with cell labels in scienti fi c journals Such an approach cannot scale This approach is not consistent enough to build a precise & accurate atlases Motivation: Cell Annotations 6 https://www.nature.com/articles/s41588-021-00818-x Problem
compare annotations across studies, potentially resolving con fl icting results. (B) Individual research groups end up annotating (potentially millions of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive, accurate references with a standardized nomenclature. Problems
compare annotations across studies, potentially resolving con fl icting results. (B) Individual research groups end up annotating (potentially millions of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive, accurate references with a standardized nomenclature. Problems
end up di ff erent cells by the same entity • Study the same thing & call it something di ff erent Comparisons between publication plots usually not informative How did research group A de fi ne cell types? Why/how do research groups A & B disagree?
to compare annotations across studies, potentially resolving con fl icting results. (B) Individual research groups end up annotating (potentially millions of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive references with a standardized nomenclature for all species. Problems
to compare annotations across studies, potentially resolving con fl icting results. (B) Individual research groups end up annotating (potentially millions of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive, accurate references with a standardized nomenclature. Problems
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 • •
datasets • Publication: Version for public, corresponding to scienti fi c publication • Datasets: Cell annotations with molecular data • Cell Label: Term associated with a cell or molecular subpopulation. Workspace Publication
other relevant metadata • Advanced user form • Allow user to “hide” irrelevant metadata within dataset • Specify which annotations & which metadata fi elds are relevant • Allow user to “hide” irrelevant metadata within dataset 23
user may: • Explore the annotations associated with this dataset • Select cells on embedding • Explore the heat maps with precalculated DE values for each annotation • Using the selection tool, select cells and calculate new DE values
• Users select cells (based either on prede fi ned clusters, or selections via the selection tool), and add cell annotations • UI basis for cell predictions (next slides)
compare annotations across studies, potentially resolving con fl icting results. (B) Individual research groups end up annotating (potentially millions of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive, accurate references with a standardized nomenclature. Problems
potentially resolving con fl icting results. (B) Individual research groups end up annotating (potentially millions of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive references with a standardized nomenclature for all species. • Researchers can now explore DEGs de fi ning cell types (on all HCA data hosted on CAP) • UI allows users within BioNetworks to begin resolving di ff erences based on the molecular data (more on this later) Solution
of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive references with a standardized nomenclature for all species. Solution • Required metadata associated with cell labels • Single string +/- ontology ID not enough Su ffi cient for scienti fi c publication Su ffi cient for building accurate atlases
of) cells manually, which results in cells with inconsistent terms and labelings between groups. (C) This approach cannot scale. We need a solution for creating comprehensive references with a standardized nomenclature for all species. Solution • Required metadata associated with cell labels • Single string +/- ontology ID not enough • Free-text • Free text label paired with associated Cell Ontology label • Stronger metadata requirements for cell annotations Existing strategies Goal • Build precise, accurate atlases • Re fi ne the Cell Ontology
Cell Ontology? • What fi elds/information have biologists asked us to see on CAP? • What information would we need to resolve differences between cell annotations? • What information could we collect to accurately construct a cell atlas? Solution Annotation schema proposal Discussions underway
Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat Protoc 16, 2749–2764 (2021). • REF dataset used to transfer cell annotations to QUERY dataset • Promise to overcome bottleneck posed by cell annotations
Atif, J. et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat Protoc 16, 2749–2764 (2021). • User choses model & transfer algorithm • View predictions imposed on molecular data: accept/ edit/decline • Publish/share/compare
new models!) • Improvements to UI for exploring molecular data + manual annotations • Feature requests tailored to individual BioNetworks e.g. consensus for annotating integrated atlases • “CellCards” summary pages
to develop tailored features and assist with human cell atlas annotations • Seminars & hands-on workshops: User can learn to use/navigate CAP for annotating new and/or integrated datasets • BioNetwork annotation jamborees: Leverage CAP to empower BioNetwork to reach consensus annotation for their HCA v1.0 integration e ff orts through jamboree to be organized in collaboration with di ff erent partners (e.g. CZI) CAP Outreach Activities
Mary Futey Nick Akhmetov Lusine Barseghyan Tigran Markosjan Konstantin Boyandin Uğur Bayindir Pavel Istomin Dennis Bolgov Andrey Isaev Evan Biederstedt 49
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