{"id":28436071,"url":"https://github.com/immunogenomics/ra_atlas_citeseq","last_synced_at":"2025-10-12T08:15:19.944Z","repository":{"id":179286466,"uuid":"367384023","full_name":"immunogenomics/RA_Atlas_CITEseq","owner":"immunogenomics","description":"RA Synovial Single-cell Multimodal Cell Atlas","archived":false,"fork":false,"pushed_at":"2024-03-06T21:15:02.000Z","size":17868,"stargazers_count":18,"open_issues_count":1,"forks_count":3,"subscribers_count":10,"default_branch":"master","last_synced_at":"2025-10-06T13:37:43.371Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://immunogenomics.io/ampra2/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/immunogenomics.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-05-14T14:14:26.000Z","updated_at":"2025-09-30T09:04:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"e05f1467-322c-44c3-ad83-1516db1e2de3","html_url":"https://github.com/immunogenomics/RA_Atlas_CITEseq","commit_stats":null,"previous_names":["immunogenomics/ra_atlas_citeseq"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/immunogenomics/RA_Atlas_CITEseq","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/immunogenomics%2FRA_Atlas_CITEseq","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/immunogenomics%2FRA_Atlas_CITEseq/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/immunogenomics%2FRA_Atlas_CITEseq/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/immunogenomics%2FRA_Atlas_CITEseq/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/immunogenomics","download_url":"https://codeload.github.com/immunogenomics/RA_Atlas_CITEseq/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/immunogenomics%2FRA_Atlas_CITEseq/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279010802,"owners_count":26084807,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-12T02:00:06.719Z","response_time":53,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-06-05T21:10:01.609Z","updated_at":"2025-10-12T08:15:19.926Z","avatar_url":"https://github.com/immunogenomics.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RA_Atlas_CITEseq\n\n[![DOI](https://zenodo.org/badge/367384023.svg)](https://zenodo.org/badge/latestdoi/367384023)\n![](https://komarev.com/ghpvc/?username=immunogenomics\u0026style=flat-square\u0026color=blueviolet)\n\n## Overview\nThis repo provides the data, code, and website for our work on \"Cellular deconstruction of inflamed synovium defines diverse inflammatory phenotypes in rheumatoid arthritis\". The data is generated through a collaborative effort with NIH funded [AMP RA/SLE (Accelerating Medicines Partnership Rheumatoid Arthritis and Systemic Lupus Erythematosus)](https://www.niams.nih.gov/grants-funding/funded-research/accelerating-medicines/RA-SLE).\n\n\n```\n- RA Synovial Single-cell Multimodal (transcriptomics and Proteomics) Cell Atlas\n- Inflammatory tissue stratification: RA Synovial Cell Type Abundance Phenotypes (CTAPs)\n- Associations of certain CTAPs with disease-relevant cytokines, histology, serology metrics\n- Reveal significant associations between RA causal genes and cell type-specific synovial CTAPs\n- Infer inflammatory phenotypes from clinical trial bulk RNA-seq of RA synovial tissue\n- Functional cell-cell interaction and immune mediator assays\n- Microscopy analysis for different synovial phenotypes \n```\n\n\u003cimg src=\"https://github.com/immunogenomics/RA_Atlas_CITEseq/blob/master/figure/overview.png\" width=\"800\" align=\"center\"\u003e\n\n\n\n## Citation\nOur paper can be cited: Zhang*, Jonsson*, Nathan*, Wei*, Millard*, *et al*, ***Nature***, 2023, https://www.nature.com/articles/s41586-023-06708-y\n\n\n\n## Website\nFeel free to check out our [Website](https://immunogenomics.io/ampra2/) regarding the single-cell data and results.\n\n\n## Source code\nSource code for reproducing the results are available at our [Github repo](https://github.com/immunogenomics/RA_Atlas_CITEseq).\n\n\n## Data availability\nCITE-seq single-cell expression matrices and sequencing and bulk expression matrices are available on Synapse (https://doi.org/10.7303/syn52297840). \n\nAssociated genotype and clinical data are available through the Arthritis and Autoimmune and Related Diseases Knowledge Portal (ARK Portal, https://arkportal.synapse.org/Explore/Datasets/DetailsPage?id=syn52297840).\n\n#### Data Description: ####\n\n- Each cell type-specific reference is provided based on one cell type-focused analysis. This could support reference mapping analysis based on the anchor genes, a downstream analysis to query and annotate cells from other disease contexts.\n\n```\nBcell_reference.rds\nBcell_uwot_model\nNK_reference.rds\nNK_uwot_model\nT_reference.rds\nT_uwot_model\nendothelial_reference.rds\nendothelial_uwot_model\nfibroblast_reference.rds\nfibroblast_uwot_model\nmyeloid_reference.rds\nmyeloid_uwot_model\n```\n\nEvery reference data has the same data structure format. Taking one cell type reference as an example, you will see the substructures as follows.\n```\n\u003e ref \u003c- readRDS(\"myeloid_reference_2023-03-12.rds\")\n\u003e str(ref)\n\n $ meta_data     :'data.frame':\t76181 obs. of  7 variables:\n  ..$ cell          : chr [1:76181] \"BRI-399_AAACCCAGTAGGAGGG\" \"BRI-399_AAACGCTGTTCAAGTC\" \"BRI-399_AAAGGATTCTGTACAG\" \"BRI-399_AAAGGTAAGCTGGCTC\" ...\n  ..$ sample        : chr [1:76181] \"BRI-399\" \"BRI-399\" \"BRI-399\" \"BRI-399\" ...\n  ..$ cluster_number: chr [1:76181] \"M-10\" \"M-10\" \"M-0\" \"M-7\" ...\n  ..$ cluster_name  : chr [1:76181] \"M-10: DC2\" \"M-10: DC2\" \"M-0: MERTK+ SELENOP+ LYVE1+\" \"M-7: IL1B+ FCN1+ HBEGF+\" ...\n  ..$ nUMI          : num [1:76181] 33644 17084 4999 16721 11235 ...\n  ..$ nGene         : int [1:76181] 5515 3806 1770 3767 3101 4788 3460 4028 3463 3152 ...\n  ..$ percent_mito  : num [1:76181] 0.0847 0.0446 0.1658 0.1576 0.0129 ...\n $ vargenes      : tibble [3,547 × 3] (S3: tbl_df/tbl/data.frame)\n  ..$ symbol: chr [1:3547] \"CXCL13\" \"CCL17\" \"CHI3L1\" \"SPP1\" ...\n  ..$ mean  : Named num [1:3547] 0.0256 0.0208 0.0429 1.0576 0.2033 ...\n  .. ..- attr(*, \"names\")= chr [1:3547] \"CXCL13\" \"CCL17\" \"CHI3L1\" \"SPP1\" ...\n  ..$ stddev: Named num [1:3547] 0.227 0.226 0.192 1.682 0.452 ...\n  .. ..- attr(*, \"names\")= chr [1:3547] \"CXCL13\" \"CCL17\" \"CHI3L1\" \"SPP1\" ...\n $ loadings      : num [1:3547, 1:20] -0.00166 -0.00647 0.00709 0.00111 0.04714 ...\n $ R             : num [1:100, 1:76181] 2.21e-14 1.81e-06 4.35e-08 3.66e-08 1.70e-14 ...\n $ Z_orig        : num [1:20, 1:76181] -7.567 0.538 -8.644 -5.862 -2.298 ...\n $ Z_corr        : num [1:20, 1:76181] -7.559 -1.46 -5.743 -3.853 0.223 ...\n  ..- attr(*, \"dimnames\")=List of 2\n  .. ..$ : chr [1:20] \"harmony_1\" \"harmony_2\" \"harmony_3\" \"harmony_4\" ...\n  .. ..$ : chr [1:76181] \"4\" \"28\" \"38\" \"47\" ...\n $ centroids     : num [1:20, 1:100] 0.92797 0.29046 -0.05318 -0.00418 -0.00714 ...\n $ cache         :List of 2\n  ..$ : num [1:100, 1] 768 865 845 756 775 ...\n  ..$ : num [1:100, 1:20] 7880 -11077 -6132 -1873 6180 ...\n $ umap          :List of 1\n  ..$ embedding: num [1:76181, 1:2] -5.02 -5.62 1.78 -4.26 -4.35 ...\n  .. ..- attr(*, \"scaled:center\")= num [1:2] 0.19 0.343\n  .. ..- attr(*, \"dimnames\")=List of 2\n  .. .. ..$ : NULL\n  .. .. ..$ : chr [1:2] \"UMAP1\" \"UMAP2\"\n $ save_uwot_path: chr \"./myeloid_uwot_model_2021-04-29\"\n ```\n\nAs you can see above, the `cluster_number` and `cluster_name` are given under `ref$meta_data`. Further, some key outpus are explained as follows:\n\n``` \n# vargenes: variable genes, means, and standard deviations used for scaling\n# loadings: gene loadings for projection into pre-Harmony PC space\n# R: Soft cluster assignments\n# Z_orig: Pre-Harmony PC embedding\n# Z_corr: Harmonized PC embedding\n# centroids: locations of final Harmony soft cluster centroids\n# cache: pre-calculated reference-dependent portions of the mixture model\n# umap: UMAP coordinates\n# save_uwot_path: path to saved uwot model (for query UMAP projection into reference UMAP coordinates)\n```\n\n- CTAP assignment to donor ID: `CTAP_donor_mapping.xlsx`\n\n```\n\u003e map \u003c- read_excel(\"CTAP_donor_mapping.xlsx\")\n\u003e head(map)\n  subject_id   donor      CTAP\n1   300-0310 BRI-405 E + F + M\n2   300-0309 BRI-411 E + F + M\n3   300-0174 BRI-479 E + F + M\n4   300-0175 BRI-525 E + F + M\n5   300-0529 BRI-554 E + F + M\n6   300-0145 BRI-589 E + F + M\n```\n\n- Reference for all cell type integrative analysis:\n```\nall_cells_reference.rds\nall_cells_uwot_model\n```\n\n- Cluster annotations for fine-grained cell states: `fine_cluster_all_314011cells_82samples.rds`\n```\n'data.frame':\t314011 obs. of  5 variables:\n $ sample        : chr  \"BRI-399\" \"BRI-399\" \"BRI-399\" \"BRI-399\" ...\n $ cell          : chr  \"BRI-399_AAACGAACAGTCTGGC\" \"BRI-399_AAAGGATGTCTCAAGT\" \"BRI-399_AAAGTGACATCGAACT\" \"BRI-399_AAAGTGAGTGCACAAG\" ...\n $ cluster_number: chr  \"B-2\" \"B-1\" \"B-2\" \"B-1\" ...\n $ cluster_name  : Factor w/ 77 levels \"B-0: CD24+CD27+CD11b+ switched memory\",..: 2 4 2 4 1 4 3 3 2 8 ...\n $ cell_type     : chr  \"B cell\" \"B cell\" \"B cell\" \"B cell\" ...\n```\n\n- Raw and processed matrices:\n\n```\n raw_mRNA_count_matrix.rds: Raw mRNA count\n raw_protein_count_matrix.rds: Raw protein count\n qc_mRNA_314011cells_log_normalized_matrix.rds: QCed mRNA normalized data matrix\n qc_protein_CLR_normalized_filtered_matrix.rds: QCed protein normalized data matrix\n```\n\n\n## Contact\nPlease email Fan Zhang (fanzhanglab@gmail.com) and Helena Jonsson (a.helena.jonsson@gmail.com) for any questions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimmunogenomics%2Fra_atlas_citeseq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fimmunogenomics%2Fra_atlas_citeseq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fimmunogenomics%2Fra_atlas_citeseq/lists"}