{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6NOKJHAKBLUG4ZQGJ7KS37FW4B","short_pith_number":"pith:6NOKJHAK","schema_version":"1.0","canonical_sha256":"f35ca49c0a0ae86e66064fd52dfcb6e07d5dd6831004538f4ab2d653da0df283","source":{"kind":"arxiv","id":"2207.07734","version":2},"attestation_state":"computed","paper":{"title":"COEM: Cross-Modal Embedding for MetaCell Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.GL"],"primary_cat":"q-bio.GN","authors_text":"Haiyi Mao, Haotian Zhang, Jason Xiaotian Dou, Minxue Jia, Panayiotis V. Benos","submitted_at":"2022-07-15T20:17:50Z","abstract_excerpt":"Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \\textbf{C}ross-M\\textbf{O}dal \\textbf{E}mbedding for \\textbf{M}etaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient"},"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":"2207.07734","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.GN","submitted_at":"2022-07-15T20:17:50Z","cross_cats_sorted":["cs.AI","cs.GL"],"title_canon_sha256":"3431dd5a61dd71ebb9db973510279db5be8ccbc01009079e98bf3ea1f26a786c","abstract_canon_sha256":"003ee58405c98169ef8f2dc090842194f233d5149a39bd92b6e98fb05a1fd7d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:43:03.499736Z","signature_b64":"BWASIbwSUYzSVBHMjPqaQ0kAF281RxpjJJ7GBXPYDWG3rBRSnv81KwmnKJB+1wHxTbuXB0TCAxnNLXzcZDvEAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f35ca49c0a0ae86e66064fd52dfcb6e07d5dd6831004538f4ab2d653da0df283","last_reissued_at":"2026-07-05T04:43:03.499404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:43:03.499404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COEM: Cross-Modal Embedding for MetaCell Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.GL"],"primary_cat":"q-bio.GN","authors_text":"Haiyi Mao, Haotian Zhang, Jason Xiaotian Dou, Minxue Jia, Panayiotis V. Benos","submitted_at":"2022-07-15T20:17:50Z","abstract_excerpt":"Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \\textbf{C}ross-M\\textbf{O}dal \\textbf{E}mbedding for \\textbf{M}etaCell Identification (COEM), which utilizes an embedded space leveraging the information of both scATAC-seq and scRNA-seq to perform aggregation, balancing the trade-off between fine resolution and sufficient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.07734","kind":"arxiv","version":2},"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/2207.07734/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":"2207.07734","created_at":"2026-07-05T04:43:03.499452+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.07734v2","created_at":"2026-07-05T04:43:03.499452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.07734","created_at":"2026-07-05T04:43:03.499452+00:00"},{"alias_kind":"pith_short_12","alias_value":"6NOKJHAKBLUG","created_at":"2026-07-05T04:43:03.499452+00:00"},{"alias_kind":"pith_short_16","alias_value":"6NOKJHAKBLUG4ZQG","created_at":"2026-07-05T04:43:03.499452+00:00"},{"alias_kind":"pith_short_8","alias_value":"6NOKJHAK","created_at":"2026-07-05T04:43:03.499452+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.07880","citing_title":"Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B","json":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B.json","graph_json":"https://pith.science/api/pith-number/6NOKJHAKBLUG4ZQGJ7KS37FW4B/graph.json","events_json":"https://pith.science/api/pith-number/6NOKJHAKBLUG4ZQGJ7KS37FW4B/events.json","paper":"https://pith.science/paper/6NOKJHAK"},"agent_actions":{"view_html":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B","download_json":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B.json","view_paper":"https://pith.science/paper/6NOKJHAK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.07734&json=true","fetch_graph":"https://pith.science/api/pith-number/6NOKJHAKBLUG4ZQGJ7KS37FW4B/graph.json","fetch_events":"https://pith.science/api/pith-number/6NOKJHAKBLUG4ZQGJ7KS37FW4B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B/action/storage_attestation","attest_author":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B/action/author_attestation","sign_citation":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B/action/citation_signature","submit_replication":"https://pith.science/pith/6NOKJHAKBLUG4ZQGJ7KS37FW4B/action/replication_record"}},"created_at":"2026-07-05T04:43:03.499452+00:00","updated_at":"2026-07-05T04:43:03.499452+00:00"}