{"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/a-coupled-autoencoder-approach-for-multi-1","title":"A coupled autoencoder approach for multi-modal analysis of cell types","arxiv_id":"1911.05663","date":"2019-11-06","proceeding":"NeurIPS 2019 12","authors":["Rohan Gala","Nathan Gouwens","Zizhen Yao","Agata Budzillo","Osnat Penn","Bosiljka Tasic","Gabe Murphy","Hongkui Zeng","Uygar Sümbül"],"abstract":"Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types. While clustering of neuron populations based on a particular data modality can be used to define cell types, such definitions are often inconsistent across different characterization modalities. We pose this issue of cross-modal alignment as an optimization problem and develop an approach based on coupled training of autoencoders as a framework for such analyses. We apply this framework to a Patch-seq dataset consisting of transcriptomic and electrophysiological profiles for the same set of neurons to study consistency of representations across modalities, and evaluate cross-modal data prediction ability. We explore the problem where only a subset of neurons is characterized with more than one modality, and demonstrate that representations learned by coupled autoencoders can be used to identify types sampled only by a single modality.","url_abs":"https://arxiv.org/abs/1911.05663v1","url_pdf":"https://arxiv.org/pdf/1911.05663v1.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":"a-coupled-autoencoder-approach-for-multi-1","repo_url":"https://github.com/AllenInstitute/coupledAE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"cross-modal-alignment","task_name":"cross-modal alignment"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.05663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.05663"}},"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":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/AllenInstitute/coupledAE","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":8},"by_repo_kind":{"official":{"samples":8,"ran":0,"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":"5a7c2b62be452097","entry":"compute_cov","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"coupling_functions.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/coupling_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5a7c2b62be452097"}},{"code_sha256_prefix":"48872f6c4a164043","entry":"dataset_50fold","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"datagen.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/datagen.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"48872f6c4a164043"}},{"code_sha256_prefix":"925b6e5af7644ec7","entry":"dataset_50foldSF","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"datagen.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/datagen.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"925b6e5af7644ec7"}},{"code_sha256_prefix":"ecc2eb0bcfa252d7","entry":"do_merges","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"analysis_clustering_helpers.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/analysis_clustering_helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ecc2eb0bcfa252d7"}},{"code_sha256_prefix":"897e7dc55aad2830","entry":"get_descendants","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"analysis_clustering_helpers.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/analysis_clustering_helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"897e7dc55aad2830"}},{"code_sha256_prefix":"14f6d53fab336bf0","entry":"get_merge_sequence","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"analysis_clustering_helpers.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/analysis_clustering_helpers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"14f6d53fab336bf0"}},{"code_sha256_prefix":"5f83597c211ea13d","entry":"mean_squared_error","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"coupling_functions.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/coupling_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5f83597c211ea13d"}},{"code_sha256_prefix":"931c8a84f9b0de00","entry":"mse","repo":"AllenInstitute/coupledAE","repo_kind":"official","path":"coupling_functions.py","file_url":"https://github.com/AllenInstitute/coupledAE/blob/HEAD/coupling_functions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"931c8a84f9b0de00"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}