{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graph-neural-networks-for-multimodal-single","title":"Graph Neural Networks for Multimodal Single-Cell Data Integration","arxiv_id":"2203.01884","date":"2022-03-03","proceeding":null,"authors":["Hongzhi Wen","Jiayuan Ding","Wei Jin","Yiqi Wang","Yuying Xie","Jiliang Tang"],"abstract":"Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics. However, it is challenging to learn the joint representations from the multimodal data, model the relationship between modalities, and, more importantly, incorporate the vast amount of single-modality datasets into the downstream analyses. To address these challenges and correspondingly facilitate multimodal single-cell data analyses, three key tasks have been introduced: $\\textit{modality prediction}$, $\\textit{modality matching}$ and $\\textit{joint embedding}$. In this work, we present a general Graph Neural Network framework $\\textit{scMoGNN}$ to tackle these three tasks and show that $\\textit{scMoGNN}$ demonstrates superior results in all three tasks compared with the state-of-the-art and conventional approaches. Our method is an official winner in the overall ranking of $\\textit{Modality prediction}$ from NeurIPS 2021 Competition, and all implementations of our methods have been integrated into DANCE package~\\url{https://github.com/OmicsML/dance}.","url_abs":"https://arxiv.org/abs/2203.01884v3","url_pdf":"https://arxiv.org/pdf/2203.01884v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"graph-neural-networks-for-multimodal-single","repo_url":"https://github.com/omicsml/dance","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-integration","task_name":"Data Integration"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"}],"methods":[{"method_slug":"dance","method_name":"DANCE"},{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2203.01884","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01884"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/openproblems-bio/neurips2021_multimodal_","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/omicsml/dance","reach":null}],"summary":{"ran":1,"ran_fixture":1,"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"b0a963dc284b953a","entry":"ScMoGCN","repo":"omicsml/dance","repo_kind":"official","path":"dance/modules/multi_modality/joint_embedding/scmogcn.py","file_url":"https://github.com/omicsml/dance/blob/HEAD/dance/modules/multi_modality/joint_embedding/scmogcn.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"b0a963dc284b953a"}},{"code_sha256_prefix":"fde51da806b4cf18","entry":"propagation_layer_combination","repo":"omicsml/dance","repo_kind":"official","path":"dance/modules/multi_modality/joint_embedding/scmogcn.py","file_url":"https://github.com/omicsml/dance/blob/HEAD/dance/modules/multi_modality/joint_embedding/scmogcn.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"fde51da806b4cf18"}},{"code_sha256_prefix":"a4783d0331a8594d","entry":"read_gmt","repo":"omicsml/dance","repo_kind":"official","path":"dance/transforms/graph/scmogcn_graph.py","file_url":"https://github.com/omicsml/dance/blob/HEAD/dance/transforms/graph/scmogcn_graph.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"BSD-2-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"a4783d0331a8594d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}