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Merge pull request #6370 from chanzuckerberg/staging
chore: prod deployment, dec 18th
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,37 +1,4 @@ | ||
| [ | ||
| { | ||
| "tier": "maintained", | ||
| "title": "Geneformer embeddings fine-tuned for CELLxGENE Census cell subclass classification", | ||
| "description": "Geneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.\nThese cell embeddings are derived from a Geneformer model CZI fine-tuned for cell subclass classification. As the fine-tuning procedure remains experimental and wasn’t performed by the Geneformer authors, these embeddings should not be used to assess performance of the Geneformer ", | ||
| "primary_contact": { | ||
| "name": "CELLxGENE Discover Team", | ||
| "email": "[email protected]", | ||
| "affiliation": "CZI" | ||
| }, | ||
| "DOI": "10.1038/s41586-023-06139-9", | ||
| "publication_info": "", | ||
| "publication_link": "", | ||
| "project_page": "", | ||
| "additional_information": "Beginning with the geneformer-12L-30M pretrained model published by Theodoris et al. (huggingface.co/ctheodoris/Geneformer), a BertForSequenceClassification model was trained to predict cell subclass (as annotated in CELLxGENE Discover see https://cellxgene.cziscience.com/collections). Embeddings were then generated using Geneformer’s EmbExtractor module with emb_layer=0.\nFor full details and a reproducible workflow please see: https://github.com/chanzuckerberg/cellxgene-census/blob/main/tools/models/geneformer/README.md", | ||
| "model_link": "s3://cellxgene-contrib-public/models/geneformer/2023-12-15/homo_sapiens/fined-tuned-model/", | ||
| "data_type": "obs_embedding", | ||
| "obsm_layer": "geneformer", | ||
| "census_version": "2023-12-15", | ||
| "experiment_name": "homo_sapiens", | ||
| "measurement_name": "RNA", | ||
| "n_cells": 62998417, | ||
| "n_columns": 512, | ||
| "n_features": 512, | ||
| "notebook_links": [ | ||
| [ | ||
| "Using trained model", | ||
| "https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_geneformer_prediction.html" | ||
| ] | ||
| ], | ||
| "submission_date": "2023-11-06", | ||
| "last_updated": null, | ||
| "revised_by": null | ||
| }, | ||
| { | ||
| "tier": "maintained", | ||
| "title": "scVI integrated-embeddings with explicit modeling of batch effects", | ||
|
|
@@ -130,6 +97,40 @@ | |
| "last_updated": null, | ||
| "revised_by": null | ||
| }, | ||
| { | ||
| "tier": "maintained", | ||
| "title": "Geneformer embeddings fine-tuned for CELLxGENE Census cell subclass classification", | ||
| "description": "Geneformer is a foundation transformer model pretrained on a large-scale corpus of ~30 million single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology.\nThese cell embeddings are derived from a Geneformer model CZI fine-tuned for cell subclass classification. As the fine-tuning procedure remains experimental and wasn’t performed by the Geneformer authors, these embeddings should not be used to assess performance of the pre-trained Geneformer model.", | ||
| "primary_contact": { | ||
| "name": "CELLxGENE Discover Team", | ||
| "email": "[email protected]", | ||
| "affiliation": "CZI" | ||
| }, | ||
| "DOI": "10.1038/s41586-023-06139-9", | ||
| "publication_info": "", | ||
| "publication_link": "", | ||
| "project_page": "", | ||
| "additional_information": "Beginning with the geneformer-12L-30M pretrained model published by Theodoris et al. (huggingface.co/ctheodoris/Geneformer), a BertForSequenceClassification model was trained to predict cell subclass (as annotated in CELLxGENE Discover see https://cellxgene.cziscience.com/collections). Embeddings were then generated using Geneformer’s EmbExtractor module with emb_layer=0.\nFor full details and a reproducible workflow please see: https://github.com/chanzuckerberg/cellxgene-census/blob/main/tools/models/geneformer/README.md", | ||
| "model_link": "s3://cellxgene-contrib-public/models/geneformer/2023-12-15/homo_sapiens/fined-tuned-model/", | ||
| "data_type": "obs_embedding", | ||
| "obsm_layer": "geneformer", | ||
| "census_version": "2023-12-15", | ||
| "experiment_name": "homo_sapiens", | ||
| "measurement_name": "RNA", | ||
| "n_cells": 62998417, | ||
| "n_columns": 512, | ||
| "n_features": 512, | ||
| "notebook_links": [ | ||
| [ | ||
| "Using trained model", | ||
| "https://chanzuckerberg.github.io/cellxgene-census/notebooks/analysis_demo/comp_bio_geneformer_prediction.html" | ||
| ] | ||
| ], | ||
| "submission_date": "2023-11-06", | ||
| "last_updated": null, | ||
| "revised_by": null | ||
| }, | ||
|
|
||
| { | ||
| "tier": "community", | ||
| "title": "PINNACLE: Contextual AI Model for Single-Cell Protein Biology", | ||
|
|
@@ -213,12 +214,12 @@ | |
| "additional_contacts": [ | ||
| { | ||
| "name": "Jialong Jiang", | ||
| "email": "[email protected]" , | ||
| "email": "[email protected]", | ||
| "affiliation": "Thomson Lab, Caltech" | ||
| }, | ||
| { | ||
| "name": "Yingying Gong", | ||
| "email": "[email protected]" , | ||
| "email": "[email protected]", | ||
| "affiliation": "Thomson Lab, Caltech" | ||
| } | ||
| ], | ||
|
|
||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -33,6 +33,23 @@ We need the following collection metadata (i.e. details associated with your pub | |
| - Contact: name and email | ||
| - Publication/preprint DOI: can be added later | ||
| - URLs: any additional URLs for related data or resources, such as GEO or protocols.io - can be added later | ||
| - Consortia: optional, and can be added later. Can be one or more of: | ||
| - Allen Institute for Brain Science | ||
| - BRAIN Initiative | ||
| - CZ Biohub | ||
| - CZI Neurodegeneration Challenge Network | ||
| - CZI Single-Cell Biology | ||
| - European Union’s Horizon 2020 | ||
| - GenitoUrinary Development Molecular Anatomy Project (GUDMAP) | ||
| - Gut Cell Atlas | ||
| - Human BioMolecular Atlas Program (HuBMAP) | ||
| - Human Cell Atlas (HCA) | ||
| - Human Pancreas Analysis Program (HPAP) | ||
| - Human Tumor Atlas Network (HTAN) | ||
| - Kidney Precision Medicine Project (KPMP) | ||
| - LungMAP | ||
| - SEA-AD | ||
| - Wellcome HCA Strategic Science Support | ||
|
|
||
| Each dataset needs the following information added to a single h5ad (AnnData 0.8) format file: | ||
|
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|
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@@ -49,12 +66,13 @@ Each dataset needs the following information added to a single h5ad (AnnData 0.8 | |
| - donor_id: free-text identifier that distinguishes the unique individual that data were derived from. It is encouraged to be something not likely to be used in other studies (e.g. donor_1 is likely to not be unique in the data corpus) | ||
| - development_stage_ontology_term_id: [HsapDv](https://www.ebi.ac.uk/ols/ontologies/hsapdv) if human, [MmusDv](https://www.ebi.ac.uk/ols/ontologies/mmusdv) if mouse, `unknown` if information unavailable | ||
| - sex_ontology_term_id: `PATO:0000384` for male, `PATO:0000383` for female, or `unknown` if unavailable | ||
| - self_reported_ethnicity_ontology_term_id: [HANCESTRO](https://www.ebi.ac.uk/ols/ontologies/hancestro) use `multiethnic` if more than one ethnicity is reported. If human and information unavailable, use `unknown`. Use `na` if non-human. | ||
| - self_reported_ethnicity_ontology_term_id: [HANCESTRO](https://www.ebi.ac.uk/ols/ontologies/hancestro) multiple comma-separated terms may be used if more than one ethnicity is reported. If human and information unavailable, use `unknown`. Use `na` if non-human. | ||
| - disease_ontology_term_id: [MONDO](https://www.ebi.ac.uk/ols/ontologies/mondo) or `PATO:0000461` for 'normal' | ||
| - tissue_type: `tissue`, `organoid`, or `cell culture` | ||
| - tissue_ontology_term_id: [UBERON](https://www.ebi.ac.uk/ols/ontologies/uberon) | ||
| - cell_type_ontology_term_id: [CL](https://www.ebi.ac.uk/ols/ontologies/cl) | ||
| - assay_ontology_term_id: [EFO](https://www.ebi.ac.uk/ols/ontologies/efo) | ||
| - suspension_type: `cell`, `nucleus`, or `na`, as corresponding to assay. Use [this table](https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/3.1.0/schema.md#suspension_type) defined in the data schema for guidance. If the assay does not appear in this table, the most appropriate value MUST be selected and the [curation team informed](mailto:[email protected]) during submission so that the assay can be added to the table. | ||
| - suspension_type: `cell`, `nucleus`, or `na`, as corresponding to assay. Use [this table](https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/4.0.0/schema.md#suspension_type) defined in the data schema for guidance. If the assay does not appear in this table, the most appropriate value MUST be selected and the [curation team informed](mailto:[email protected]) during submission so that the assay can be added to the table. | ||
| - **Embeddings in obsm**: | ||
| - One or more two-dimensional embeddings, prefixed with 'X\_' | ||
| - **Features in var & raw.var (if present)**: | ||
|
|
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