{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:IIAPGRTCNMEFYSDPIZVHAGPDBN","short_pith_number":"pith:IIAPGRTC","canonical_record":{"source":{"id":"2504.12353","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.GN","submitted_at":"2025-04-15T22:03:38Z","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"title_canon_sha256":"d0be595bbe7fb931a0c30475157e3c6f9fd74f86b48dabc1c414470b5d379708","abstract_canon_sha256":"c1b5c5612419ea679cc9a4d8b03f116ab2d9e208c4f2dc78b02acf39ba939f36"},"schema_version":"1.0"},"canonical_sha256":"4200f346626b085c486f466a7019e30b4537fff14a1f5e039f6cba6bb08138bc","source":{"kind":"arxiv","id":"2504.12353","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12353","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12353v1","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12353","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_12","alias_value":"IIAPGRTCNMEF","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_16","alias_value":"IIAPGRTCNMEFYSDP","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_8","alias_value":"IIAPGRTC","created_at":"2026-07-05T10:50:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:IIAPGRTCNMEFYSDPIZVHAGPDBN","target":"record","payload":{"canonical_record":{"source":{"id":"2504.12353","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.GN","submitted_at":"2025-04-15T22:03:38Z","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"title_canon_sha256":"d0be595bbe7fb931a0c30475157e3c6f9fd74f86b48dabc1c414470b5d379708","abstract_canon_sha256":"c1b5c5612419ea679cc9a4d8b03f116ab2d9e208c4f2dc78b02acf39ba939f36"},"schema_version":"1.0"},"canonical_sha256":"4200f346626b085c486f466a7019e30b4537fff14a1f5e039f6cba6bb08138bc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:14.699281Z","signature_b64":"r++4+aQsdABBLokG1qnymWikjgzxAu9Qs/Xe7/maCmZQx8OytIwRmOTCXk88kKdshuTLr5T9tkyxipYyyXFFAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4200f346626b085c486f466a7019e30b4537fff14a1f5e039f6cba6bb08138bc","last_reissued_at":"2026-07-05T10:50:14.698759Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:14.698759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.12353","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AY6jBOoJ5c+gyb2Z68OgIAQUL3ju1/n8/Ljke9meJ7gmO5LIPCWPz7hDrOYC1RE4P7r/b8U66GmG8KzHlzq2CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:03:10.006074Z"},"content_sha256":"012ce8b7c710ec9b5959d7b9155f6a133e54f68ce07c3098985b02e056a58b30","schema_version":"1.0","event_id":"sha256:012ce8b7c710ec9b5959d7b9155f6a133e54f68ce07c3098985b02e056a58b30"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:IIAPGRTCNMEFYSDPIZVHAGPDBN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.ML"],"primary_cat":"q-bio.GN","authors_text":"Anil K. Rustgi, Jianhua Hu, Ming Yuan, Shikun Wang, Shuo Shuo Liu, Yuxuan Chen","submitted_at":"2025-04-15T22:03:38Z","abstract_excerpt":"Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of the technology such as the relatively low resolution and comparatively insufficient sequencing depth make it difficult to reliably extract real biological signals from these data. To alleviate this challenge, we propose a novel transfer learning framework, referred to as TransST, to adaptively leverage the cell-labeled information from external so"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12353","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/2504.12353/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SFTnJ9ejNyY6gE1pW3ox7FDeAgurT1BdujDRqJ4Q5aHA/MmrhEHkEYKUj/kzV4HXnjbASuZ1l1p716ynfk8VCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:03:10.006472Z"},"content_sha256":"88935b40285c694b9f3d8d9c2ae884aac7488be77f10466110207140120b79f1","schema_version":"1.0","event_id":"sha256:88935b40285c694b9f3d8d9c2ae884aac7488be77f10466110207140120b79f1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IIAPGRTCNMEFYSDPIZVHAGPDBN/bundle.json","state_url":"https://pith.science/pith/IIAPGRTCNMEFYSDPIZVHAGPDBN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IIAPGRTCNMEFYSDPIZVHAGPDBN/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T19:03:10Z","links":{"resolver":"https://pith.science/pith/IIAPGRTCNMEFYSDPIZVHAGPDBN","bundle":"https://pith.science/pith/IIAPGRTCNMEFYSDPIZVHAGPDBN/bundle.json","state":"https://pith.science/pith/IIAPGRTCNMEFYSDPIZVHAGPDBN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IIAPGRTCNMEFYSDPIZVHAGPDBN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IIAPGRTCNMEFYSDPIZVHAGPDBN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c1b5c5612419ea679cc9a4d8b03f116ab2d9e208c4f2dc78b02acf39ba939f36","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.GN","submitted_at":"2025-04-15T22:03:38Z","title_canon_sha256":"d0be595bbe7fb931a0c30475157e3c6f9fd74f86b48dabc1c414470b5d379708"},"schema_version":"1.0","source":{"id":"2504.12353","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12353","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12353v1","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12353","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_12","alias_value":"IIAPGRTCNMEF","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_16","alias_value":"IIAPGRTCNMEFYSDP","created_at":"2026-07-05T10:50:14Z"},{"alias_kind":"pith_short_8","alias_value":"IIAPGRTC","created_at":"2026-07-05T10:50:14Z"}],"graph_snapshots":[{"event_id":"sha256:88935b40285c694b9f3d8d9c2ae884aac7488be77f10466110207140120b79f1","target":"graph","created_at":"2026-07-05T10:50:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.12353/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of the technology such as the relatively low resolution and comparatively insufficient sequencing depth make it difficult to reliably extract real biological signals from these data. To alleviate this challenge, we propose a novel transfer learning framework, referred to as TransST, to adaptively leverage the cell-labeled information from external so","authors_text":"Anil K. Rustgi, Jianhua Hu, Ming Yuan, Shikun Wang, Shuo Shuo Liu, Yuxuan Chen","cross_cats":["cs.LG","stat.AP","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.GN","submitted_at":"2025-04-15T22:03:38Z","title":"TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12353","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:012ce8b7c710ec9b5959d7b9155f6a133e54f68ce07c3098985b02e056a58b30","target":"record","created_at":"2026-07-05T10:50:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c1b5c5612419ea679cc9a4d8b03f116ab2d9e208c4f2dc78b02acf39ba939f36","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.GN","submitted_at":"2025-04-15T22:03:38Z","title_canon_sha256":"d0be595bbe7fb931a0c30475157e3c6f9fd74f86b48dabc1c414470b5d379708"},"schema_version":"1.0","source":{"id":"2504.12353","kind":"arxiv","version":1}},"canonical_sha256":"4200f346626b085c486f466a7019e30b4537fff14a1f5e039f6cba6bb08138bc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4200f346626b085c486f466a7019e30b4537fff14a1f5e039f6cba6bb08138bc","first_computed_at":"2026-07-05T10:50:14.698759Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:14.698759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r++4+aQsdABBLokG1qnymWikjgzxAu9Qs/Xe7/maCmZQx8OytIwRmOTCXk88kKdshuTLr5T9tkyxipYyyXFFAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:14.699281Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.12353","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:012ce8b7c710ec9b5959d7b9155f6a133e54f68ce07c3098985b02e056a58b30","sha256:88935b40285c694b9f3d8d9c2ae884aac7488be77f10466110207140120b79f1"],"state_sha256":"f7ca913b6437a803a3afa193d982129121f77547273b3cd67f6c17191a3024a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1GwB+l+/IJ+ysQTZ4UJEucsPdL2Nvl0wpO5+AUU33ChCa9H1qUU7qftEoU/Jod9mgAMRk7QvzH+MQ9mFXOb6BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:03:10.008469Z","bundle_sha256":"5c4bd63cba08373791327f021bb556540afa9ef24dabec5acc7331d17d6e5a42"}}