Dimensional reduction of phenotypes from 53 000 mouse models reveals a diverse landscape of gene function.

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Through the research efforts of thousands of research groups, systematic curation of published literature and high-throughput phenotyping screens, the collective body of knowledge for the mouse now covers the majority of protein-coding genes. We here collected data for over 53 000 mouse models with mutations in over 15 000 genomic markers and characterized by more than 254 000 annotations using more than 9000 distinct ontology terms. We investigated dimensional reduction and embedding techniques as means to facilitate access to this diverse and high-dimensional information. Our analyses provide the first visual maps of the landscape of mouse phenotypic diversity. We also summarize some of the difficulties in producing and interpreting embeddings of sparse phenotypic data. In particular, we show that data preprocessing, filtering and encoding have as much impact on the final embeddings as the process of dimensional reduction. Nonetheless, techniques developed in the context of dimensional reduction create opportunities for explorative analysis of this large pool of public data, including for searching for mouse models suited to study human diseases.\nAVAILABILITY AND IMPLEMENTATION: Source code for analysis scripts is available on GitHub at https://github.com/tkonopka/mouse-embeddings. The data underlying this article are available in Zenodo at https://doi.org/10.5281/zenodo.4916171.\nCONTACT: t.konopka@qmul.ac.uk.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Advances online.", "claims": [{"id": "claim-method", "statement": "Statistical significance (P-value) is evaluated using the Fisher test; the significance threshold is Bonferroni-corrected P = 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34870209", "locator": "sections[4] \"2.2 Embeddings of full phenotype profiles capture the diversity of animal phenotypes\" paragraph 4", "quote": "Statistical significance (P-value) is evaluated using the Fisher test; the significance threshold is Bonferroni-corrected P = 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article are available in Zenodo at https://doi.org/10.5281/zenodo.4916171.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34870209", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The data underlying this article are available in Zenodo at https://doi.org/10.5281/zenodo.4916171."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Data preprocessing, filtering, and encoding have as much impact on the final mouse-phenotype embeddings as the process of dimensional reduction.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34870209", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In particular, we show that data preprocessing, filtering and encoding have as much impact on the final embeddings as the process of dimensional reduction."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Data preprocessing, filtering, and encoding have as much impact on the final mouse-phenotype embeddings as the process of dimensional reduction.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34870209"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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