{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:YFE5KW2O2NMQYVJA373EN7SDKR","short_pith_number":"pith:YFE5KW2O","schema_version":"1.0","canonical_sha256":"c149d55b4ed3590c5520dff646fe4354460ae98a2682cb0bdc20a78dacb18f45","source":{"kind":"arxiv","id":"2007.12616","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Combinatorial Multi-Study Factor Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Giovanni Parmigiani, Isabella N. Grabski, Lorenzo Trippa, Roberta De Vito","submitted_at":"2020-07-24T16:17:47Z","abstract_excerpt":"Analyzing multiple studies allows leveraging data from a range of sources and populations, but until recently, there have been limited methodologies to approach the joint unsupervised analysis of multiple high-dimensional studies. A recent method, Bayesian Multi-Study Factor Analysis (BMSFA), identifies latent factors common to all studies, as well as latent factors specific to individual studies. However, BMSFA does not allow for partially shared factors, i.e. latent factors shared by more than one but less than all studies. We extend BMSFA by introducing a new method, Tetris, for Bayesian co"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2007.12616","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-07-24T16:17:47Z","cross_cats_sorted":[],"title_canon_sha256":"fd61b4f86ab9b32eef0027011140f98a66a30a73be169b9266953ffb4458e671","abstract_canon_sha256":"7ff6571843c240ca3d7320ef7867babccc48a2ccc6d680ef48eee55290f88d2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:21:53.924663Z","signature_b64":"32xAuMsKcGt+e5cia5N+xC6kXB0vF0SoeT6WwJSmCz0/aaPpJkr50sc0l+8jGd4yu8z+kDF8gSIic5YSkhuKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c149d55b4ed3590c5520dff646fe4354460ae98a2682cb0bdc20a78dacb18f45","last_reissued_at":"2026-07-05T01:21:53.924302Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:21:53.924302Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Combinatorial Multi-Study Factor Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Giovanni Parmigiani, Isabella N. Grabski, Lorenzo Trippa, Roberta De Vito","submitted_at":"2020-07-24T16:17:47Z","abstract_excerpt":"Analyzing multiple studies allows leveraging data from a range of sources and populations, but until recently, there have been limited methodologies to approach the joint unsupervised analysis of multiple high-dimensional studies. A recent method, Bayesian Multi-Study Factor Analysis (BMSFA), identifies latent factors common to all studies, as well as latent factors specific to individual studies. However, BMSFA does not allow for partially shared factors, i.e. latent factors shared by more than one but less than all studies. We extend BMSFA by introducing a new method, Tetris, for Bayesian co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.12616","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2007.12616/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2007.12616","created_at":"2026-07-05T01:21:53.924371+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.12616v1","created_at":"2026-07-05T01:21:53.924371+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.12616","created_at":"2026-07-05T01:21:53.924371+00:00"},{"alias_kind":"pith_short_12","alias_value":"YFE5KW2O2NMQ","created_at":"2026-07-05T01:21:53.924371+00:00"},{"alias_kind":"pith_short_16","alias_value":"YFE5KW2O2NMQYVJA","created_at":"2026-07-05T01:21:53.924371+00:00"},{"alias_kind":"pith_short_8","alias_value":"YFE5KW2O","created_at":"2026-07-05T01:21:53.924371+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.15855","citing_title":"Bayesian Non-Negative Matrix Factorization with Correlated Mutation Type Probabilities for Mutational Signatures","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR","json":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR.json","graph_json":"https://pith.science/api/pith-number/YFE5KW2O2NMQYVJA373EN7SDKR/graph.json","events_json":"https://pith.science/api/pith-number/YFE5KW2O2NMQYVJA373EN7SDKR/events.json","paper":"https://pith.science/paper/YFE5KW2O"},"agent_actions":{"view_html":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR","download_json":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR.json","view_paper":"https://pith.science/paper/YFE5KW2O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.12616&json=true","fetch_graph":"https://pith.science/api/pith-number/YFE5KW2O2NMQYVJA373EN7SDKR/graph.json","fetch_events":"https://pith.science/api/pith-number/YFE5KW2O2NMQYVJA373EN7SDKR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR/action/storage_attestation","attest_author":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR/action/author_attestation","sign_citation":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR/action/citation_signature","submit_replication":"https://pith.science/pith/YFE5KW2O2NMQYVJA373EN7SDKR/action/replication_record"}},"created_at":"2026-07-05T01:21:53.924371+00:00","updated_at":"2026-07-05T01:21:53.924371+00:00"}