{ "_id": "6ad32f242c5c76d3dd0c9d21", "Package": "metasnf", "Title": "Meta Clustering with Similarity Network Fusion", "Version": "2.3.1", "Authors@R": "c(\nperson(\n\"Prashanth S\",\n\"Velayudhan\",\nemail = \"psvelayu@gmail.com\",\nrole = c(\"aut\", \"cre\")\n),\nperson(\"Xiaoqiao\", \"Xu\", role = c(\"aut\")),\nperson(\"Prajkta\", \"Kallurkar\", role = c(\"aut\")),\nperson(\"Ana Patricia\", \"Balbon\", role = c(\"aut\")),\nperson(\"Maria T\", \"Secara\", role = c(\"aut\")),\nperson(\"Adam\", \"Taback\", role = c(\"aut\")),\nperson(\"Denise\", \"Sabac\", role = c(\"aut\")),\nperson(\"Nicholas\", \"Chan\", role = c(\"aut\")),\nperson(\"Shihao\", \"Ma\", role = c(\"aut\")),\nperson(\"Bo\", \"Wang\", role = c(\"aut\")),\nperson(\"Daniel\", \"Felsky\", role = c(\"aut\")),\nperson(\"Stephanie H\", \"Ameis\", role = c(\"aut\")),\nperson(\"Brian\", \"Cox\", role = c(\"aut\")),\nperson(\"Colin\", \"Hawco\", role = c(\"aut\")),\nperson(given = \"Lauren\", family = \"Erdman\", role = c(\"aut\")),\nperson(\ngiven = \"Anne L\",\nfamily = \"Wheeler\",\nemail = \"anne.wheeler@sickkids.ca\",\nrole = c(\"aut\", \"ths\")\n)\n)", "Description": "Framework to facilitate patient subtyping with similarity\nnetwork fusion and meta clustering. The similarity network\nfusion (SNF) algorithm was introduced by Wang et al. (2014) in\n. SNF is a data integration approach\nthat can transform high-dimensional and diverse data types into\na single similarity network suitable for clustering with\nminimal loss of information from each initial data source. The\nmeta clustering approach was introduced by Caruana et al.\n(2006) in . Meta clustering involves\ngenerating a wide range of cluster solutions by adjusting\nclustering hyperparameters, then clustering the solutions\nthemselves into a manageable number of qualitatively similar\nsolutions, and finally characterizing representative solutions\nto find ones that are best for the user's specific context.\nThis package provides a framework to easily transform\nmulti-modal data into a wide range of similarity network\nfusion-derived cluster solutions as well as to visualize,\ncharacterize, and validate those solutions. Core package\nfunctionality includes easy customization of distance metrics,\nclustering algorithms, and SNF hyperparameters to generate\ndiverse clustering solutions; calculation and plotting of\nassociations between features, between patients, and between\ncluster solutions; and standard cluster validation approaches\nincluding resampled measures of cluster stability, standard\nmetrics of cluster quality, and label propagation to evaluate\ngeneralizability in unseen data. Associated vignettes guide the\nuser through using the package to identify patient subtypes\nwhile adhering to best practices for unsupervised learning.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "Config/testthat/edition": "3", "LazyData": "true", "VignetteBuilder": "knitr", "URL": "https://branchlab.github.io/metasnf/,\nhttps://github.com/BRANCHlab/metasnf/", "BugReports": "https://github.com/BRANCHlab/metasnf/issues", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://branchlab.r-universe.dev", "Date/Publication": "2026-06-19 21:08:41 UTC", "RemoteUrl": "https://github.com/branchlab/metasnf", "RemoteRef": "HEAD", "RemoteSha": "07a0e486d3f77566818b58e9bc89960012117b7e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-17 08:13:23 UTC", "User": "root" }, "Author": "Prashanth S Velayudhan [aut, cre],\nXiaoqiao Xu [aut],\nPrajkta Kallurkar [aut],\nAna Patricia Balbon [aut],\nMaria T Secara [aut],\nAdam Taback [aut],\nDenise Sabac [aut],\nNicholas Chan [aut],\nShihao Ma [aut],\nBo Wang [aut],\nDaniel Felsky [aut],\nStephanie H Ameis [aut],\nBrian Cox [aut],\nColin Hawco [aut],\nLauren Erdman [aut],\nAnne L Wheeler [aut, ths]", "Maintainer": "Prashanth S Velayudhan ", "_user": "branchlab", "_type": "src", "_file": "metasnf_2.3.1.tar.gz", "_fileid": "https://r2.ropensci.org/306a73b62b4f30ee0a73493987cfc321ae718e3d9a3c26eed6c70b6d5723999d", "_filesize": 6186739, "_sha256": "306a73b62b4f30ee0a73493987cfc321ae718e3d9a3c26eed6c70b6d5723999d", "_expires": "2027-01-25T08:17:38.000Z", "_created": "2026-10-17T08:13:23.000Z", "_published": "2026-10-17T08:17:40.490Z", "_buildurl": "https://github.com/r-universe/branchlab/actions/runs/39016279761", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 117349830447, "time": 208, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12094224685" }, { "job": 117349830411, "time": 169, "config": 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"purrr", "role": "Imports" }, { "package": "RColorBrewer", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "SNFtool", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "tidyr", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "circlize", "role": "Suggests" }, { "package": "ComplexHeatmap", "role": "Suggests" }, { "package": "InteractiveComplexHeatmap", "role": "Suggests" }, { "package": "future", "role": "Suggests" }, { "package": "future.apply", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "ggalluvial", "role": "Suggests" }, { "package": "lifecycle", "role": "Suggests" }, { "package": "dbscan", "role": "Suggests" } ], "_owner": "branchlab", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-24", 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"extra/NEWS.txt", "extra/readme.html", "extra/readme.md" ], "_homeurl": "https://github.com/branchlab/metasnf", "_realowner": "branchlab", "_cranurl": true, "_releases": [ { "version": "1.1.1", "date": "2024-11-08" }, { "version": "1.1.2", "date": "2024-11-08" }, { "version": "2.0.0", "date": "2025-02-04" }, { "version": "2.0.6", "date": "2025-03-25" }, { "version": "2.1.1", "date": "2025-04-25" }, { "version": "2.1.2", "date": "2025-04-28" }, { "version": "2.3.0", "date": "2026-06-19" } ], "_exports": [ "add_settings_df_rows", "adjusted_rand_index_heatmap", "alluvial_cluster_plot", "as_ari_matrix", "as_data_list", "as_settings_df", "as_sim_mats_list", "as_snf_config", "as_weights_matrix", "assemble_data", "assoc_pval_heatmap", "auto_plot", "bar_plot", "batch_snf", "batch_snf_subsamples", "calc_aris", "calc_assoc_pval_matrix", "calc_nmis", "calculate_coclustering", "calculate_db_indices", "calculate_dunn_indices", "calculate_silhouettes", "cell_significance_fn", 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"abcd_colour", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "patient", "colour" ], "rows": 275, "table": true, "tojson": true }, { "name": "abcd_cort_sa", "title": "Mock ABCD cortical surface area data", "object": "abcd_cort_sa", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "patient", "mrisdp_303", "mrisdp_304", "mrisdp_305", "mrisdp_306", "mrisdp_307", "mrisdp_308", "mrisdp_309", "mrisdp_310", "mrisdp_311", "mrisdp_312", "mrisdp_313", "mrisdp_314", "mrisdp_315", "mrisdp_316", "mrisdp_317", "mrisdp_318", "mrisdp_319", "mrisdp_320", "mrisdp_321", "mrisdp_322", "mrisdp_323", "mrisdp_324", "mrisdp_325", "mrisdp_326", "mrisdp_327", "mrisdp_328", "mrisdp_329", "mrisdp_330", "mrisdp_331", "mrisdp_332", "mrisdp_333", "mrisdp_334", "mrisdp_335", "mrisdp_336", "mrisdp_337", "mrisdp_338", "mrisdp_339", "mrisdp_340", "mrisdp_341", "mrisdp_342", "mrisdp_343", "mrisdp_344", "mrisdp_345", "mrisdp_346", "mrisdp_347", "mrisdp_348", "mrisdp_349", "mrisdp_350", "mrisdp_351", 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"smri_vol_scs_pallidumlh", "smri_vol_scs_3rdventricle", "smri_vol_scs_4thventricle", "smri_vol_scs_bstem", "smri_vol_scs_hpuslh", "smri_vol_scs_amygdalalh", "smri_vol_scs_csf", "smri_vol_scs_aal", "smri_vol_scs_vedclh", "smri_vol_scs_cbwmatterrh", "smri_vol_scs_ltventriclerh", "smri_vol_scs_inflatventrh", "smri_vol_scs_crbwmatterrh", "smri_vol_scs_crbcortexrh", "smri_vol_scs_tprh", "smri_vol_scs_caudaterh", "smri_vol_scs_putamenrh", "smri_vol_scs_pallidumrh", "smri_vol_scs_hpusrh", "smri_vol_scs_amygdalarh", "smri_vol_scs_aar", "smri_vol_scs_vedcrh" ], "rows": 174, "table": true, "tojson": true }, { "name": "age_df", "title": "Mock age data", "object": "age_df", "class": [ "data.frame" ], "fields": [ "patient_id", "age" ], "rows": 200, "table": true, "tojson": true }, { "name": "anxiety", "title": "Mock ABCD anxiety data", "object": "anxiety", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "cbcl_anxiety_r" ], "rows": 275, "table": true, "tojson": true }, { "name": "cache_a_complete_example_ext_sol_df", "title": "Cached example extended solutions data frame", "object": "cache_a_complete_example_ext_sol_df", "class": [ "ext_solutions_df", "data.frame" ], "fields": [], "rows": 20, "table": true, "tojson": true }, { "name": "cache_a_complete_example_lp_ext_sol_df", "title": "Cached example extended solutions data frame", "object": "cache_a_complete_example_lp_ext_sol_df", "class": [ "ext_solutions_df", "data.frame" ], "fields": [ "solution", "nclust", "mc", "min_pval", "mean_pval", "max_pval", "uid_NDAR_INV0567T2Y9", "uid_NDAR_INV0J4PYA5F", "uid_NDAR_INV10OMKVLE", "uid_NDAR_INV15FPCW4O", "uid_NDAR_INV19NB4RJK", "uid_NDAR_INV1HLGR738", "uid_NDAR_INV1KR0EZFU", "uid_NDAR_INV1L3Y9EOP", "uid_NDAR_INV1ZHRDJ6B", "uid_NDAR_INV2PK6C85M", "uid_NDAR_INV2XO1PHCT", "uid_NDAR_INV3CU5Y9BZ", "uid_NDAR_INV3MBSY16V", "uid_NDAR_INV3N0QFDLO", "uid_NDAR_INV3Y027GVK", "uid_NDAR_INV40Z7GVYJ", "uid_NDAR_INV49UPOXHJ", "uid_NDAR_INV4N5XGZE8", "uid_NDAR_INV4OWRB536", "uid_NDAR_INV4X80QUZY", "uid_NDAR_INV50JL2RXP", "uid_NDAR_INV5BRNFYQC", "uid_NDAR_INV6RVH5KZS", "uid_NDAR_INV6WBQCY2I", "uid_NDAR_INV752EFAQ0", "uid_NDAR_INV7QO93CJH", "uid_NDAR_INV84G9ONXP", "uid_NDAR_INV8EHP6W1U", "uid_NDAR_INV8MJFUKIW", "uid_NDAR_INV8WGK6ECZ", "uid_NDAR_INV94AKNGMJ", "uid_NDAR_INV9GAZYV8Q", "uid_NDAR_INV9IREH05N", "uid_NDAR_INV9KC3GVMU", "uid_NDAR_INV9NFKZ82A", "uid_NDAR_INV9S1BMDE5", "uid_NDAR_INVA68OU0YK", "uid_NDAR_INVADCYZ38B", "uid_NDAR_INVAYM8WTIN", "uid_NDAR_INVB8O4LAQV", "uid_NDAR_INVBAP80W1R", "uid_NDAR_INVBTRW1NUK", "uid_NDAR_INVCIXE0496", "uid_NDAR_INVCYBSZD0N", "uid_NDAR_INVD37Z9N61", "uid_NDAR_INVD61ZUBC7", "uid_NDAR_INVEQ1OBNSM", "uid_NDAR_INVEVBDLSTM", "uid_NDAR_INVEY0FMJDI", "uid_NDAR_INVFLU0YINE", "uid_NDAR_INVFNZPWMSI", "uid_NDAR_INVFY76P8AJ", "uid_NDAR_INVG3T0PXW6", "uid_NDAR_INVG8BRLSO9", "uid_NDAR_INVH1KV76BQ", "uid_NDAR_INVH3P4T8C2", "uid_NDAR_INVH4FZC2XB", "uid_NDAR_INVH8QN7WLT", "uid_NDAR_INVHERPS382", "uid_NDAR_INVHM3XS68O", "uid_NDAR_INVI1RKT9MX", "uid_NDAR_INVIZFV08RU", "uid_NDAR_INVJ574KX6A", "uid_NDAR_INVK3FL5CP2", "uid_NDAR_INVKB0CYO1H", "uid_NDAR_INVKHWS26UN", "uid_NDAR_INVKTUMPLXY", "uid_NDAR_INVL4NIUZYF", "uid_NDAR_INVLF3TNDUZ", "uid_NDAR_INVLI58ERQC", "uid_NDAR_INVLIQRM8KC", "uid_NDAR_INVLXDP1SWT", "uid_NDAR_INVMBOZVEA4", "uid_NDAR_INVMIWOSHJN", "cbcl_anxiety_r_pval", "cbcl_depress_r_pval" ], "rows": 5, "table": true, "tojson": true }, { "name": "cache_a_complete_example_sol_df", "title": "Cached example solutions data frame", "object": "cache_a_complete_example_sol_df", "class": [ "solutions_df", "data.frame" ], "fields": [ "solution", "nclust", "mc", "uid_NDAR_INV0567T2Y9", "uid_NDAR_INV0J4PYA5F", "uid_NDAR_INV10OMKVLE", "uid_NDAR_INV15FPCW4O", "uid_NDAR_INV19NB4RJK", "uid_NDAR_INV1HLGR738", "uid_NDAR_INV1KR0EZFU", "uid_NDAR_INV1L3Y9EOP", "uid_NDAR_INV1TCP5GNM", "uid_NDAR_INV1ZHRDJ6B", "uid_NDAR_INV2EJ41YSZ", "uid_NDAR_INV2PK6C85M", "uid_NDAR_INV2XO1PHCT", "uid_NDAR_INV3CU5Y9BZ", "uid_NDAR_INV3MBSY16V", "uid_NDAR_INV3N0QFDLO", "uid_NDAR_INV3N1476QE", "uid_NDAR_INV3Y027GVK", "uid_NDAR_INV40Z7GVYJ", "uid_NDAR_INV49UPOXHJ", "uid_NDAR_INV4N5XGZE8", "uid_NDAR_INV4OWRB536", "uid_NDAR_INV4X80QUZY", "uid_NDAR_INV50JL2RXP", "uid_NDAR_INV5BRNFYQC", "uid_NDAR_INV64F9GH0V", "uid_NDAR_INV6RVH5KZS", "uid_NDAR_INV6WBQCY2I", "uid_NDAR_INV752EFAQ0", "uid_NDAR_INV7O30HFV6", "uid_NDAR_INV7QO93CJH", "uid_NDAR_INV84G9ONXP", "uid_NDAR_INV8EHP6W1U", "uid_NDAR_INV8MJFUKIW", "uid_NDAR_INV8WGK6ECZ", "uid_NDAR_INV94AKNGMJ", "uid_NDAR_INV9GAZYV8Q", "uid_NDAR_INV9IREH05N", "uid_NDAR_INV9KC3GVMU", "uid_NDAR_INV9NFKZ82A", "uid_NDAR_INV9S1BMDE5", "uid_NDAR_INVA68OU0YK", "uid_NDAR_INVADCYZ38B", "uid_NDAR_INVAYM8WTIN", "uid_NDAR_INVB8O4LAQV", "uid_NDAR_INVBAP80W1R", "uid_NDAR_INVBTRW1NUK", "uid_NDAR_INVCIXE0496", "uid_NDAR_INVCYBSZD0N", "uid_NDAR_INVD37Z9N61", "uid_NDAR_INVD61ZUBC7", "uid_NDAR_INVDXKG2UBF", "uid_NDAR_INVEQ1OBNSM", "uid_NDAR_INVEQ4D2M8P", "uid_NDAR_INVEVBDLSTM", "uid_NDAR_INVEY0FMJDI", "uid_NDAR_INVFLU0YINE", "uid_NDAR_INVFNZPWMSI", "uid_NDAR_INVFY76P8AJ", "uid_NDAR_INVG3T0PXW6", "uid_NDAR_INVG5CI7XK4", "uid_NDAR_INVG8BRLSO9", "uid_NDAR_INVGDBYXWV4", "uid_NDAR_INVH1KV76BQ", "uid_NDAR_INVH3P4T8C2", "uid_NDAR_INVH4FZC2XB", "uid_NDAR_INVH8QN7WLT", "uid_NDAR_INVHERPS382", "uid_NDAR_INVHEUWA52I", "uid_NDAR_INVHM3XS68O", "uid_NDAR_INVI1RKT9MX", "uid_NDAR_INVIZFV08RU", "uid_NDAR_INVJ574KX6A", "uid_NDAR_INVK3FL5CP2", "uid_NDAR_INVK9ULDQA2", "uid_NDAR_INVKB0CYO1H", "uid_NDAR_INVKHWS26UN", "uid_NDAR_INVKTUMPLXY", "uid_NDAR_INVKYH529RD", "uid_NDAR_INVL4NIUZYF", "uid_NDAR_INVLDQH8ATK", "uid_NDAR_INVLF3TNDUZ", "uid_NDAR_INVLI58ERQC", "uid_NDAR_INVLIQRM8KC", "uid_NDAR_INVLXDP1SWT", "uid_NDAR_INVMBOZVEA4", "uid_NDAR_INVMIWOSHJN" ], "rows": 20, "table": true, "tojson": true }, { "name": "cancer_diagnosis_df", "title": "Mock diagnosis data", "object": "cancer_diagnosis_df", "class": [ "data.frame" ], "fields": [ "patient_id", "diagnosis" ], "rows": 200, "table": true, "tojson": true }, { "name": "cort_sa", "title": "Mock ABCD cortical surface area data", "object": "cort_sa", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "mrisdp_303", "mrisdp_304", "mrisdp_305", "mrisdp_306", "mrisdp_307", "mrisdp_308", "mrisdp_309", "mrisdp_310", "mrisdp_311", "mrisdp_312", "mrisdp_313", "mrisdp_314", "mrisdp_315", "mrisdp_316", "mrisdp_317", "mrisdp_318", "mrisdp_319", "mrisdp_320", "mrisdp_321", "mrisdp_322", "mrisdp_323", "mrisdp_324", "mrisdp_325", "mrisdp_326", "mrisdp_327", "mrisdp_328", "mrisdp_329", "mrisdp_330", "mrisdp_331", "mrisdp_332", "mrisdp_333", "mrisdp_334", "mrisdp_335", "mrisdp_336", "mrisdp_337", "mrisdp_338", "mrisdp_339", "mrisdp_340", "mrisdp_341", "mrisdp_342", "mrisdp_343", "mrisdp_344", "mrisdp_345", "mrisdp_346", "mrisdp_347", "mrisdp_348", "mrisdp_349", "mrisdp_350", "mrisdp_351", "mrisdp_352", "mrisdp_353", "mrisdp_354", "mrisdp_355", "mrisdp_356", "mrisdp_357", "mrisdp_358", "mrisdp_359", "mrisdp_360", "mrisdp_361", "mrisdp_362", "mrisdp_363", "mrisdp_364", "mrisdp_365", "mrisdp_366", "mrisdp_367", "mrisdp_368", "mrisdp_369", "mrisdp_370", "mrisdp_371", "mrisdp_372", "mrisdp_373", "mrisdp_374", "mrisdp_375", "mrisdp_376", "mrisdp_377", "mrisdp_378", "mrisdp_379", "mrisdp_380", "mrisdp_381", "mrisdp_382", "mrisdp_383", "mrisdp_384", "mrisdp_385", "mrisdp_386", "mrisdp_387", "mrisdp_388", "mrisdp_389", "mrisdp_390", "mrisdp_391", "mrisdp_392", "mrisdp_393", "mrisdp_394", "mrisdp_395", "mrisdp_396", "mrisdp_397", "mrisdp_398", "mrisdp_399", "mrisdp_400", "mrisdp_401", "mrisdp_402", "mrisdp_403", "mrisdp_404", "mrisdp_405", "mrisdp_406", "mrisdp_407", "mrisdp_408", "mrisdp_409", "mrisdp_410", "mrisdp_411", "mrisdp_412", "mrisdp_413", "mrisdp_414", "mrisdp_415", "mrisdp_416", "mrisdp_417", "mrisdp_418", "mrisdp_419", "mrisdp_420", "mrisdp_421", "mrisdp_422", "mrisdp_423", "mrisdp_424", "mrisdp_425", "mrisdp_426", "mrisdp_427", "mrisdp_428", "mrisdp_429", "mrisdp_430", "mrisdp_431", "mrisdp_432", "mrisdp_433", "mrisdp_434", "mrisdp_435", "mrisdp_436", "mrisdp_437", "mrisdp_438", "mrisdp_439", "mrisdp_440", "mrisdp_441", "mrisdp_442", "mrisdp_443", "mrisdp_444", "mrisdp_445", "mrisdp_446", "mrisdp_447", "mrisdp_448", "mrisdp_449", "mrisdp_450", "mrisdp_451", "mrisdp_452", "mrisdp_453" ], "rows": 188, "table": true, "tojson": true }, { "name": "cort_t", "title": "Mock ABCD cortical thickness data", "object": "cort_t", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "mrisdp_1", "mrisdp_2", "mrisdp_3", "mrisdp_4", "mrisdp_5", "mrisdp_6", "mrisdp_7", "mrisdp_8", "mrisdp_9", "mrisdp_10", "mrisdp_11", "mrisdp_12", "mrisdp_13", "mrisdp_14", "mrisdp_15", "mrisdp_16", "mrisdp_17", "mrisdp_18", "mrisdp_19", "mrisdp_20", "mrisdp_21", "mrisdp_22", "mrisdp_23", "mrisdp_24", "mrisdp_25", "mrisdp_26", "mrisdp_27", "mrisdp_28", "mrisdp_29", "mrisdp_30", "mrisdp_31", "mrisdp_32", "mrisdp_33", "mrisdp_34", "mrisdp_35", "mrisdp_36", "mrisdp_37", "mrisdp_38", "mrisdp_39", "mrisdp_40", "mrisdp_41", "mrisdp_42", "mrisdp_43", "mrisdp_44", "mrisdp_45", "mrisdp_46", "mrisdp_47", "mrisdp_48", "mrisdp_49", "mrisdp_50", "mrisdp_51", "mrisdp_52", "mrisdp_53", "mrisdp_54", "mrisdp_55", "mrisdp_56", "mrisdp_57", "mrisdp_58", "mrisdp_59", "mrisdp_60", "mrisdp_61", "mrisdp_62", "mrisdp_63", "mrisdp_64", "mrisdp_65", "mrisdp_66", "mrisdp_67", "mrisdp_68", "mrisdp_69", "mrisdp_70", "mrisdp_71", "mrisdp_72", "mrisdp_73", "mrisdp_74", "mrisdp_75", "mrisdp_76", "mrisdp_77", "mrisdp_78", "mrisdp_79", "mrisdp_80", "mrisdp_81", "mrisdp_82", "mrisdp_83", "mrisdp_84", "mrisdp_85", "mrisdp_86", "mrisdp_87", "mrisdp_88", "mrisdp_89", "mrisdp_90", "mrisdp_91", "mrisdp_92", "mrisdp_93", "mrisdp_94", "mrisdp_95", "mrisdp_96", "mrisdp_97", "mrisdp_98", "mrisdp_99", "mrisdp_100", "mrisdp_101", "mrisdp_102", "mrisdp_103", "mrisdp_104", "mrisdp_105", "mrisdp_106", "mrisdp_107", "mrisdp_108", "mrisdp_109", "mrisdp_110", "mrisdp_111", "mrisdp_112", "mrisdp_113", "mrisdp_114", "mrisdp_115", "mrisdp_116", "mrisdp_117", "mrisdp_118", "mrisdp_119", "mrisdp_120", "mrisdp_121", "mrisdp_122", "mrisdp_123", "mrisdp_124", "mrisdp_125", "mrisdp_126", "mrisdp_127", "mrisdp_128", "mrisdp_129", "mrisdp_130", "mrisdp_131", "mrisdp_132", "mrisdp_133", "mrisdp_134", "mrisdp_135", "mrisdp_136", "mrisdp_137", "mrisdp_138", "mrisdp_139", "mrisdp_140", "mrisdp_141", "mrisdp_142", "mrisdp_143", "mrisdp_144", "mrisdp_145", "mrisdp_146", "mrisdp_147", "mrisdp_148", "mrisdp_149", "mrisdp_150", "mrisdp_151" ], "rows": 188, "table": true, "tojson": true }, { "name": "depress", "title": "Mock ABCD depression data", "object": "depress", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "cbcl_depress_r" ], "rows": 275, "table": true, "tojson": true }, { "name": "diagnosis_df", "title": "Mock diagnosis data", "object": "diagnosis_df", "class": [ "data.frame" ], "fields": [ "patient_id", "diagnosis" ], "rows": 200, "table": true, "tojson": true }, { "name": "expression_df", "title": "Modification of SNFtool mock data frame \"Data1\"", "object": "expression_df", "class": [ "data.frame" ], "fields": [ "gene_1_expression", "gene_2_expression", "patient_id" ], "rows": 200, "table": true, "tojson": true }, { "name": "fav_colour", "title": "Mock ABCD \"colour\" data", "object": "fav_colour", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "colour" ], "rows": 275, "table": true, "tojson": true }, { "name": "gender_df", "title": "Mock gender data", "object": "gender_df", "class": [ "data.frame" ], "fields": [ "patient_id", "gender" ], "rows": 200, "table": true, "tojson": true }, { "name": "income", "title": "Mock ABCD income data", "object": "income", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "household_income" ], "rows": 275, "table": true, "tojson": true }, { "name": "methylation_df", "title": "Modification of SNFtool mock data frame \"Data2\"", "object": "methylation_df", "class": [ "data.frame" ], "fields": [ "gene_1_methylation", "gene_2_methylation", "patient_id" ], "rows": 200, "table": true, "tojson": true }, { "name": "mock_ari_matrix", "title": "Mock example of an 'ari_matrix' metasnf object", "object": "mock_ari_matrix", "class": [ "ari_matrix", "matrix", "array" ], "fields": [ "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20" ], "rows": 20, "table": true, "tojson": true }, { "name": "mock_clust_fns_list", "title": "Mock example of a 'clust_fns_list' metasnf object", "object": "mock_clust_fns_list", "class": [ "clust_fns_list", "list" ], "fields": [], "table": false, "tojson": true }, { "name": "mock_data_list", "title": "Mock example of a 'data_list' metasnf object", "object": "mock_data_list", "class": [ "data_list", "list" ], "fields": [], "table": false, "tojson": true }, { "name": "mock_dist_fns_list", "title": "Mock example of a 'dist_fns_list' metasnf object", "object": "mock_dist_fns_list", "class": [ "dist_fns_list", "list" ], "fields": [], "table": false, "tojson": true }, { "name": "mock_ext_solutions_df", "title": "Mock example of a 'ext_solutions_df' metasnf object", "object": "mock_ext_solutions_df", "class": [ "ext_solutions_df", "data.frame" ], "fields": [], "rows": 4, "table": true, "tojson": true }, { "name": "mock_mc_solutions_df", "title": "Mock example of a 'mc_solutions_df' metasnf object", "object": "mock_mc_solutions_df", "class": [ "solutions_df", "data.frame" ], "fields": [ "solution", "nclust", "mc", "uid_NDAR_INV0567T2Y9", "uid_NDAR_INV0IZ157F8", "uid_NDAR_INV0J4PYA5F", "uid_NDAR_INV10OMKVLE", "uid_NDAR_INV15FPCW4O", "uid_NDAR_INV19NB4RJK", "uid_NDAR_INV1HLGR738", "uid_NDAR_INV1KR0EZFU", "uid_NDAR_INV1L3Y9EOP", "uid_NDAR_INV1TCP5GNM", "uid_NDAR_INV1ZHRDJ6B", "uid_NDAR_INV2EJ41YSZ", "uid_NDAR_INV2PK6C85M", "uid_NDAR_INV2XO1PHCT", "uid_NDAR_INV3CU5Y9BZ", "uid_NDAR_INV3MBSY16V", "uid_NDAR_INV3N0QFDLO", "uid_NDAR_INV3N1476QE", "uid_NDAR_INV3Y027GVK", "uid_NDAR_INV40Z7GVYJ", "uid_NDAR_INV49UPOXHJ", "uid_NDAR_INV4AKIU1WX", "uid_NDAR_INV4G7032E6", "uid_NDAR_INV4KQ3WEFY", "uid_NDAR_INV4N5XGZE8", "uid_NDAR_INV4OWRB536", "uid_NDAR_INV4X80QUZY", "uid_NDAR_INV50JL2RXP", "uid_NDAR_INV5BRNFYQC", "uid_NDAR_INV5Q9YWRCK", "uid_NDAR_INV64F9GH0V", "uid_NDAR_INV6RVH5KZS", "uid_NDAR_INV6WBQCY2I", "uid_NDAR_INV752EFAQ0", "uid_NDAR_INV7O30HFV6", "uid_NDAR_INV7QO93CJH", "uid_NDAR_INV84G9ONXP", "uid_NDAR_INV8EHP6W1U", "uid_NDAR_INV8MJFUKIW", "uid_NDAR_INV8WGK6ECZ", "uid_NDAR_INV915R2Z67", "uid_NDAR_INV94AKNGMJ", "uid_NDAR_INV9GAZYV8Q", "uid_NDAR_INV9IREH05N", "uid_NDAR_INV9KC3GVMU", "uid_NDAR_INV9NFKZ82A", "uid_NDAR_INV9S1BMDE5", "uid_NDAR_INVA68OU0YK", "uid_NDAR_INVADCYZ38B", "uid_NDAR_INVAYM8WTIN", "uid_NDAR_INVB4MU1PDA", "uid_NDAR_INVB8O4LAQV", "uid_NDAR_INVBAP80W1R", "uid_NDAR_INVBTRW1NUK", "uid_NDAR_INVCI0KZWMN", "uid_NDAR_INVCIXE0496", "uid_NDAR_INVCYBSZD0N", "uid_NDAR_INVD37Z9N61", "uid_NDAR_INVD61ZUBC7", "uid_NDAR_INVDXKG2UBF", "uid_NDAR_INVEO3JM5CB", "uid_NDAR_INVEQ1OBNSM", "uid_NDAR_INVEQ4D2M8P", "uid_NDAR_INVEVBDLSTM", "uid_NDAR_INVEY0FMJDI", "uid_NDAR_INVFLU0YINE", "uid_NDAR_INVFNZPWMSI", "uid_NDAR_INVFY76P8AJ", "uid_NDAR_INVG3T0PXW6", "uid_NDAR_INVG5CI7XK4", "uid_NDAR_INVG8BRLSO9", "uid_NDAR_INVGDBYXWV4", "uid_NDAR_INVH1KV76BQ", "uid_NDAR_INVH3P4T8C2", "uid_NDAR_INVH4FZC2XB", "uid_NDAR_INVH8QN7WLT", "uid_NDAR_INVHERPS382", "uid_NDAR_INVHEUWA52I", "uid_NDAR_INVHM3XS68O", "uid_NDAR_INVI1RKT9MX", "uid_NDAR_INVIZFV08RU", "uid_NDAR_INVJ4THFRD2", "uid_NDAR_INVJ574KX6A", "uid_NDAR_INVJEV61XIU", "uid_NDAR_INVJR3S271G", "uid_NDAR_INVK3FL5CP2", "uid_NDAR_INVK9ULDQA2", "uid_NDAR_INVKB0CYO1H", "uid_NDAR_INVKHWS26UN", "uid_NDAR_INVKTUMPLXY", "uid_NDAR_INVKYH529RD", "uid_NDAR_INVL045Z1TY", "uid_NDAR_INVL4NIUZYF", "uid_NDAR_INVLDQH8ATK", "uid_NDAR_INVLF3TNDUZ", "uid_NDAR_INVLI58ERQC", "uid_NDAR_INVLIQRM8KC", "uid_NDAR_INVLXDP1SWT", "uid_NDAR_INVMBOZVEA4", "uid_NDAR_INVMIWOSHJN" ], "rows": 20, "table": true, "tojson": true }, { "name": "mock_rep_solutions_df", "title": "Mock example of a 'rep_solutions_df' metasnf object", "object": "mock_rep_solutions_df", "class": [ "solutions_df", "data.frame" ], "fields": [ "solution", "nclust", "mc", "uid_NDAR_INV0567T2Y9", "uid_NDAR_INV0IZ157F8", "uid_NDAR_INV0J4PYA5F", "uid_NDAR_INV10OMKVLE", "uid_NDAR_INV15FPCW4O", "uid_NDAR_INV19NB4RJK", "uid_NDAR_INV1HLGR738", "uid_NDAR_INV1KR0EZFU", "uid_NDAR_INV1L3Y9EOP", "uid_NDAR_INV1TCP5GNM", "uid_NDAR_INV1ZHRDJ6B", "uid_NDAR_INV2EJ41YSZ", "uid_NDAR_INV2PK6C85M", "uid_NDAR_INV2XO1PHCT", "uid_NDAR_INV3CU5Y9BZ", "uid_NDAR_INV3MBSY16V", "uid_NDAR_INV3N0QFDLO", "uid_NDAR_INV3N1476QE", "uid_NDAR_INV3Y027GVK", "uid_NDAR_INV40Z7GVYJ", "uid_NDAR_INV49UPOXHJ", "uid_NDAR_INV4AKIU1WX", "uid_NDAR_INV4G7032E6", "uid_NDAR_INV4KQ3WEFY", "uid_NDAR_INV4N5XGZE8", "uid_NDAR_INV4OWRB536", "uid_NDAR_INV4X80QUZY", "uid_NDAR_INV50JL2RXP", "uid_NDAR_INV5BRNFYQC", "uid_NDAR_INV5Q9YWRCK", "uid_NDAR_INV64F9GH0V", "uid_NDAR_INV6RVH5KZS", "uid_NDAR_INV6WBQCY2I", "uid_NDAR_INV752EFAQ0", "uid_NDAR_INV7O30HFV6", "uid_NDAR_INV7QO93CJH", "uid_NDAR_INV84G9ONXP", "uid_NDAR_INV8EHP6W1U", "uid_NDAR_INV8MJFUKIW", "uid_NDAR_INV8WGK6ECZ", "uid_NDAR_INV915R2Z67", "uid_NDAR_INV94AKNGMJ", "uid_NDAR_INV9GAZYV8Q", "uid_NDAR_INV9IREH05N", "uid_NDAR_INV9KC3GVMU", "uid_NDAR_INV9NFKZ82A", "uid_NDAR_INV9S1BMDE5", "uid_NDAR_INVA68OU0YK", "uid_NDAR_INVADCYZ38B", "uid_NDAR_INVAYM8WTIN", "uid_NDAR_INVB4MU1PDA", "uid_NDAR_INVB8O4LAQV", "uid_NDAR_INVBAP80W1R", "uid_NDAR_INVBTRW1NUK", "uid_NDAR_INVCI0KZWMN", "uid_NDAR_INVCIXE0496", "uid_NDAR_INVCYBSZD0N", "uid_NDAR_INVD37Z9N61", "uid_NDAR_INVD61ZUBC7", "uid_NDAR_INVDXKG2UBF", "uid_NDAR_INVEO3JM5CB", "uid_NDAR_INVEQ1OBNSM", "uid_NDAR_INVEQ4D2M8P", "uid_NDAR_INVEVBDLSTM", "uid_NDAR_INVEY0FMJDI", "uid_NDAR_INVFLU0YINE", "uid_NDAR_INVFNZPWMSI", "uid_NDAR_INVFY76P8AJ", "uid_NDAR_INVG3T0PXW6", "uid_NDAR_INVG5CI7XK4", "uid_NDAR_INVG8BRLSO9", "uid_NDAR_INVGDBYXWV4", "uid_NDAR_INVH1KV76BQ", "uid_NDAR_INVH3P4T8C2", "uid_NDAR_INVH4FZC2XB", "uid_NDAR_INVH8QN7WLT", "uid_NDAR_INVHERPS382", "uid_NDAR_INVHEUWA52I", "uid_NDAR_INVHM3XS68O", "uid_NDAR_INVI1RKT9MX", "uid_NDAR_INVIZFV08RU", "uid_NDAR_INVJ4THFRD2", "uid_NDAR_INVJ574KX6A", "uid_NDAR_INVJEV61XIU", "uid_NDAR_INVJR3S271G", "uid_NDAR_INVK3FL5CP2", "uid_NDAR_INVK9ULDQA2", "uid_NDAR_INVKB0CYO1H", "uid_NDAR_INVKHWS26UN", "uid_NDAR_INVKTUMPLXY", "uid_NDAR_INVKYH529RD", "uid_NDAR_INVL045Z1TY", "uid_NDAR_INVL4NIUZYF", "uid_NDAR_INVLDQH8ATK", "uid_NDAR_INVLF3TNDUZ", "uid_NDAR_INVLI58ERQC", "uid_NDAR_INVLIQRM8KC", "uid_NDAR_INVLXDP1SWT", "uid_NDAR_INVMBOZVEA4", "uid_NDAR_INVMIWOSHJN" ], "rows": 4, "table": true, "tojson": true }, { "name": "mock_settings_df", "title": "Mock example of a 'settings_df' metasnf object", "object": "mock_settings_df", "class": [ "settings_df", "data.frame" ], "fields": [ "solution", "alpha", "k", "t", "snf_scheme", "clust_alg", "cnt_dist", "dsc_dist", "ord_dist", "cat_dist", "mix_dist", "inc_subcortical_volume", "inc_cortical_sa", "inc_household_income", "inc_pubertal_status" ], "rows": 20, "table": true, "tojson": true }, { "name": "mock_snf_config", "title": "Mock example of a 'snf_config' metasnf object", "object": "mock_snf_config", "class": [ "snf_config", "list" ], "fields": [], "table": false, "tojson": true }, { "name": "mock_solutions_df", "title": "Mock example of a 'solutions_df' metasnf object", "object": "mock_solutions_df", "class": [ "solutions_df", "data.frame" ], "fields": [ "solution", "nclust", "mc", "uid_NDAR_INV0567T2Y9", "uid_NDAR_INV0IZ157F8", "uid_NDAR_INV0J4PYA5F", "uid_NDAR_INV10OMKVLE", "uid_NDAR_INV15FPCW4O", "uid_NDAR_INV19NB4RJK", "uid_NDAR_INV1HLGR738", "uid_NDAR_INV1KR0EZFU", "uid_NDAR_INV1L3Y9EOP", "uid_NDAR_INV1TCP5GNM", "uid_NDAR_INV1ZHRDJ6B", "uid_NDAR_INV2EJ41YSZ", "uid_NDAR_INV2PK6C85M", "uid_NDAR_INV2XO1PHCT", "uid_NDAR_INV3CU5Y9BZ", "uid_NDAR_INV3MBSY16V", "uid_NDAR_INV3N0QFDLO", "uid_NDAR_INV3N1476QE", "uid_NDAR_INV3Y027GVK", "uid_NDAR_INV40Z7GVYJ", "uid_NDAR_INV49UPOXHJ", "uid_NDAR_INV4AKIU1WX", "uid_NDAR_INV4G7032E6", "uid_NDAR_INV4KQ3WEFY", "uid_NDAR_INV4N5XGZE8", "uid_NDAR_INV4OWRB536", "uid_NDAR_INV4X80QUZY", "uid_NDAR_INV50JL2RXP", "uid_NDAR_INV5BRNFYQC", "uid_NDAR_INV5Q9YWRCK", "uid_NDAR_INV64F9GH0V", "uid_NDAR_INV6RVH5KZS", "uid_NDAR_INV6WBQCY2I", "uid_NDAR_INV752EFAQ0", "uid_NDAR_INV7O30HFV6", "uid_NDAR_INV7QO93CJH", "uid_NDAR_INV84G9ONXP", "uid_NDAR_INV8EHP6W1U", "uid_NDAR_INV8MJFUKIW", "uid_NDAR_INV8WGK6ECZ", "uid_NDAR_INV915R2Z67", "uid_NDAR_INV94AKNGMJ", "uid_NDAR_INV9GAZYV8Q", "uid_NDAR_INV9IREH05N", "uid_NDAR_INV9KC3GVMU", "uid_NDAR_INV9NFKZ82A", "uid_NDAR_INV9S1BMDE5", "uid_NDAR_INVA68OU0YK", "uid_NDAR_INVADCYZ38B", "uid_NDAR_INVAYM8WTIN", "uid_NDAR_INVB4MU1PDA", "uid_NDAR_INVB8O4LAQV", "uid_NDAR_INVBAP80W1R", "uid_NDAR_INVBTRW1NUK", "uid_NDAR_INVCI0KZWMN", "uid_NDAR_INVCIXE0496", "uid_NDAR_INVCYBSZD0N", "uid_NDAR_INVD37Z9N61", "uid_NDAR_INVD61ZUBC7", "uid_NDAR_INVDXKG2UBF", "uid_NDAR_INVEO3JM5CB", "uid_NDAR_INVEQ1OBNSM", "uid_NDAR_INVEQ4D2M8P", "uid_NDAR_INVEVBDLSTM", "uid_NDAR_INVEY0FMJDI", "uid_NDAR_INVFLU0YINE", "uid_NDAR_INVFNZPWMSI", "uid_NDAR_INVFY76P8AJ", "uid_NDAR_INVG3T0PXW6", "uid_NDAR_INVG5CI7XK4", "uid_NDAR_INVG8BRLSO9", "uid_NDAR_INVGDBYXWV4", "uid_NDAR_INVH1KV76BQ", "uid_NDAR_INVH3P4T8C2", "uid_NDAR_INVH4FZC2XB", "uid_NDAR_INVH8QN7WLT", "uid_NDAR_INVHERPS382", "uid_NDAR_INVHEUWA52I", "uid_NDAR_INVHM3XS68O", "uid_NDAR_INVI1RKT9MX", "uid_NDAR_INVIZFV08RU", "uid_NDAR_INVJ4THFRD2", "uid_NDAR_INVJ574KX6A", "uid_NDAR_INVJEV61XIU", "uid_NDAR_INVJR3S271G", "uid_NDAR_INVK3FL5CP2", "uid_NDAR_INVK9ULDQA2", "uid_NDAR_INVKB0CYO1H", "uid_NDAR_INVKHWS26UN", "uid_NDAR_INVKTUMPLXY", "uid_NDAR_INVKYH529RD", "uid_NDAR_INVL045Z1TY", "uid_NDAR_INVL4NIUZYF", "uid_NDAR_INVLDQH8ATK", "uid_NDAR_INVLF3TNDUZ", "uid_NDAR_INVLI58ERQC", "uid_NDAR_INVLIQRM8KC", "uid_NDAR_INVLXDP1SWT", "uid_NDAR_INVMBOZVEA4", "uid_NDAR_INVMIWOSHJN" ], "rows": 20, "table": true, "tojson": true }, { "name": "mock_t_solutions_df", "title": "Mock example of a 't_solutions_df' metasnf object", "object": "mock_t_solutions_df", "class": [ "t_solutions_df", "data.frame" ], "fields": [ "uid", "s1", "s2", "s3", "s4", "s5", "s6", "s7", "s8", "s9", "s10", "s11", "s12", "s13", "s14", "s15", "s16", "s17", "s18", "s19", "s20" ], "rows": 100, "table": true, "tojson": true }, { "name": "mock_weights_matrix", "title": "Mock example of a 'weights_matrix' metasnf object", "object": "mock_weights_matrix", "class": [ "weights_matrix", "matrix", "array" ], "fields": [ "smri_vol_scs_cbwmatterlh", "smri_vol_scs_ltventriclelh", "smri_vol_scs_inflatventlh", "smri_vol_scs_crbwmatterlh", "smri_vol_scs_crbcortexlh", "smri_vol_scs_tplh", "smri_vol_scs_caudatelh", "smri_vol_scs_putamenlh", "smri_vol_scs_pallidumlh", "smri_vol_scs_3rdventricle", "smri_vol_scs_4thventricle", "smri_vol_scs_bstem", "smri_vol_scs_hpuslh", "smri_vol_scs_amygdalalh", "smri_vol_scs_csf", "smri_vol_scs_aal", "smri_vol_scs_vedclh", "smri_vol_scs_cbwmatterrh", "smri_vol_scs_ltventriclerh", "smri_vol_scs_inflatventrh", "smri_vol_scs_crbwmatterrh", "smri_vol_scs_crbcortexrh", "smri_vol_scs_tprh", "smri_vol_scs_caudaterh", "smri_vol_scs_putamenrh", "smri_vol_scs_pallidumrh", "smri_vol_scs_hpusrh", "smri_vol_scs_amygdalarh", "smri_vol_scs_aar", "smri_vol_scs_vedcrh", "mrisdp_303", "mrisdp_304", "mrisdp_305", "mrisdp_306", "mrisdp_307", "mrisdp_308", "mrisdp_309", "mrisdp_310", "mrisdp_311", "mrisdp_312", "mrisdp_313", "mrisdp_314", "mrisdp_315", "mrisdp_316", "mrisdp_317", "mrisdp_318", "mrisdp_319", "mrisdp_320", "mrisdp_321", "mrisdp_322", "mrisdp_323", "mrisdp_324", "mrisdp_325", "mrisdp_326", "mrisdp_327", "mrisdp_328", "mrisdp_329", "mrisdp_330", "mrisdp_331", "mrisdp_332", "mrisdp_333", "mrisdp_334", "mrisdp_335", "mrisdp_336", "mrisdp_337", "mrisdp_338", "mrisdp_339", "mrisdp_340", "mrisdp_341", "mrisdp_342", "mrisdp_343", "mrisdp_344", "mrisdp_345", "mrisdp_346", "mrisdp_347", "mrisdp_348", "mrisdp_349", "mrisdp_350", "mrisdp_351", "mrisdp_352", "mrisdp_353", "mrisdp_354", "mrisdp_355", "mrisdp_356", "mrisdp_357", "mrisdp_358", "mrisdp_359", "mrisdp_360", "mrisdp_361", "mrisdp_362", "mrisdp_363", "mrisdp_364", "mrisdp_365", "mrisdp_366", "mrisdp_367", "mrisdp_368", "mrisdp_369", "mrisdp_370", "mrisdp_371", "mrisdp_372", "mrisdp_373", "mrisdp_374", "mrisdp_375", "mrisdp_376", "mrisdp_377", "mrisdp_378", "mrisdp_379", "mrisdp_380", "mrisdp_381", "mrisdp_382", "mrisdp_383", "mrisdp_384", "mrisdp_385", "mrisdp_386", "mrisdp_387", "mrisdp_388", "mrisdp_389", "mrisdp_390", "mrisdp_391", "mrisdp_392", "mrisdp_393", "mrisdp_394", "mrisdp_395", "mrisdp_396", "mrisdp_397", "mrisdp_398", "mrisdp_399", "mrisdp_400", "mrisdp_401", "mrisdp_402", "mrisdp_403", "mrisdp_404", "mrisdp_405", "mrisdp_406", "mrisdp_407", "mrisdp_408", "mrisdp_409", "mrisdp_410", "mrisdp_411", "mrisdp_412", "mrisdp_413", "mrisdp_414", "mrisdp_415", "mrisdp_416", "mrisdp_417", "mrisdp_418", "mrisdp_419", "mrisdp_420", "mrisdp_421", "mrisdp_422", "mrisdp_423", "mrisdp_424", "mrisdp_425", "mrisdp_426", "mrisdp_427", "mrisdp_428", "mrisdp_429", "mrisdp_430", "mrisdp_431", "mrisdp_432", "mrisdp_433", "mrisdp_434", "mrisdp_435", "mrisdp_436", "mrisdp_437", "mrisdp_438", "mrisdp_439", "mrisdp_440", "mrisdp_441", "mrisdp_442", "mrisdp_443", "mrisdp_444", "mrisdp_445", "mrisdp_446", "mrisdp_447", "mrisdp_448", "mrisdp_449", "mrisdp_450", "mrisdp_451", "mrisdp_452", "mrisdp_453", "household_income", "pubertal_status" ], "rows": 20, "table": true, "tojson": true }, { "name": "pubertal", "title": "Mock ABCD pubertal status data", "object": "pubertal", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "pubertal_status" ], "rows": 275, "table": true, "tojson": true }, { "name": "subc_v", "title": "Mock ABCD subcortical volumes data", "object": "subc_v", "class": [ "tbl_df", "tbl", "data.frame" ], "fields": [ "unique_id", "smri_vol_scs_cbwmatterlh", "smri_vol_scs_ltventriclelh", "smri_vol_scs_inflatventlh", "smri_vol_scs_crbwmatterlh", "smri_vol_scs_crbcortexlh", "smri_vol_scs_tplh", "smri_vol_scs_caudatelh", "smri_vol_scs_putamenlh", "smri_vol_scs_pallidumlh", "smri_vol_scs_3rdventricle", "smri_vol_scs_4thventricle", "smri_vol_scs_bstem", "smri_vol_scs_hpuslh", "smri_vol_scs_amygdalalh", "smri_vol_scs_csf", "smri_vol_scs_aal", "smri_vol_scs_vedclh", "smri_vol_scs_cbwmatterrh", "smri_vol_scs_ltventriclerh", "smri_vol_scs_inflatventrh", "smri_vol_scs_crbwmatterrh", "smri_vol_scs_crbcortexrh", "smri_vol_scs_tprh", "smri_vol_scs_caudaterh", "smri_vol_scs_putamenrh", "smri_vol_scs_pallidumrh", "smri_vol_scs_hpusrh", "smri_vol_scs_amygdalarh", "smri_vol_scs_aar", "smri_vol_scs_vedcrh" ], "rows": 174, "table": true, "tojson": true } ], "_help": [ { "page": "abcd_anxiety", "title": "Mock ABCD anxiety data", "topics": [ "abcd_anxiety" ] }, { "page": "abcd_colour", "title": "Mock ABCD \"colour\" data", "topics": [ "abcd_colour" ] }, { "page": "abcd_cort_sa", "title": "Mock ABCD cortical surface area data", "topics": [ "abcd_cort_sa" ] }, { "page": "abcd_cort_t", "title": "Mock ABCD cortical thickness data", "topics": [ "abcd_cort_t" ] }, { "page": "abcd_depress", "title": "Mock ABCD depression data", "topics": [ "abcd_depress" ] }, { "page": "abcd_h_income", "title": "Mock ABCD income data", "topics": [ "abcd_h_income" ] }, { "page": "abcd_income", "title": "Mock ABCD income data", "topics": [ "abcd_income" ] }, { "page": "abcd_pubertal", "title": "Mock ABCD pubertal status data", "topics": [ "abcd_pubertal" ] }, { "page": "abcd_subc_v", "title": "Mock ABCD subcortical volumes data", "topics": [ "abcd_subc_v" ] }, { "page": "add_settings_df_rows", "title": "Add rows to a settings_df", "topics": [ "add_settings_df_rows" ] }, { "page": "age_df", "title": "Mock age data", "topics": [ "age_df" ] }, { "page": "alluvial_cluster_plot", "title": "Alluvial plot of patients across cluster counts and important features", "topics": [ "alluvial_cluster_plot" ] }, { "page": "anxiety", "title": "Mock ABCD anxiety data", "topics": [ "anxiety" ] }, { "page": "as_ari_matrix", "title": "Convert an object to an ARI matrix", "topics": [ "as_ari_matrix" ] }, { "page": "as_data_list", "title": "Convert an object to a data list", "topics": [ "as_data_list" ] }, { "page": "as_settings_df", "title": "Convert an object to a settings data frame", "topics": [ "as_settings_df" ] }, { "page": "as_sim_mats_list", "title": "Convert an object to a similarity matrix list", "topics": [ "as_sim_mats_list" ] }, { "page": "as_snf_config", "title": "Convert an object to a snf config", "topics": [ "as_snf_config" ] }, { "page": "as_weights_matrix", "title": "Convert an object to a weights matrix", "topics": [ "as_weights_matrix" ] }, { "page": "as.data.frame.data_list", "title": "Coerce a 'data_list' class object into a 'data.frame' class object", "topics": [ "as.data.frame.data_list" ] }, { "page": "as.data.frame.ext_solutions_df", "title": "Coerce a 'ext_solutions_df' class object into a 'data.frame' class object", "topics": [ "as.data.frame.ext_solutions_df" ] }, { "page": "as.data.frame.settings_df", "title": "Coerce a 'settings_df' class object into a 'data.frame' class object", "topics": [ "as.data.frame.settings_df" ] }, { "page": "as.data.frame.snf_config", "title": "Coerce a 'settings_df' class object into a 'data.frame' class object", "topics": [ "as.data.frame.snf_config" ] }, { "page": "as.data.frame.solutions_df", "title": "Coerce a 'solutions_df' class object into a 'data.frame' class object", "topics": [ "as.data.frame.solutions_df" ] }, { "page": "as.data.frame.t_ext_solutions_df", "title": "Coerce a 't_ext_solutions_df' class object into a 'data.frame' class object", "topics": [ "as.data.frame.t_ext_solutions_df" ] }, { "page": "as.data.frame.t_solutions_df", "title": "Coerce a 't_solutions_df' class object into a 'data.frame' class object", "topics": [ "as.data.frame.t_solutions_df" ] }, { "page": "as.data.frame.weights_matrix", "title": "Coerce a 'weights_matrix' class object into a 'data.frame' class object", "topics": [ "as.data.frame.weights_matrix" ] }, { "page": "as.list.clust_fns_list", "title": "Coerce a 'clust_fns_list' class object into a 'list' class object", "topics": [ "as.list.clust_fns_list" ] }, { "page": "as.list.data_list", "title": "Coerce a 'data_list' class object into a 'list' class object", "topics": [ "as.list.data_list" ] }, { "page": "as.list.dist_fns_list", "title": "Coerce a 'dist_fns_list' class object into a 'list' class object", "topics": [ "as.list.dist_fns_list" ] }, { "page": "as.list.sim_mats_list", "title": "Coerce a 'sim_mats_list' class object into a 'list' class object", "topics": [ "as.list.sim_mats_list" ] }, { "page": "as.list.snf_config", "title": "Coerce a 'snf_config' class object into a 'list' class object", "topics": [ "as.list.snf_config" ] }, { "page": "as.matrix.ari_matrix", "title": "Coerce a 'ari_matrix' class object into a 'matrix' class object", "topics": [ "as.matrix.ari_matrix" ] }, { "page": "as.matrix.weights_matrix", "title": "Coerce a 'weights_matrix' class object into a 'matrix' class object", "topics": [ "as.matrix.weights_matrix" ] }, { "page": "assemble_data", "title": "Collapse a data frame and/or a data list into a single data frame", "topics": [ "assemble_data" ] }, { "page": "assoc_pval_heatmap", "title": "Heatmap of pairwise associations between features", "topics": [ "assoc_pval_heatmap" ] }, { "page": "auto_plot", "title": "Automatically plot features across clusters", "topics": [ "auto_plot" ] }, { "page": "bar_plot", "title": "Bar plot separating a feature by cluster", "topics": [ "bar_plot" ] }, { "page": "batch_snf", "title": "Run variations of SNF", "topics": [ "batch_snf" ] }, { "page": "batch_snf_subsamples", "title": "Run SNF clustering pipeline on a list of subsampled data lists", "topics": [ "batch_snf_subsamples" ] }, { "page": "cache_a_complete_example_ext_sol_df", "title": "Cached example extended solutions data frame", "topics": [ "cache_a_complete_example_ext_sol_df" ] }, { "page": "cache_a_complete_example_lp_ext_sol_df", "title": "Cached example extended solutions data frame", "topics": [ "cache_a_complete_example_lp_ext_sol_df" ] }, { "page": "cache_a_complete_example_sol_df", "title": "Cached example solutions data frame", "topics": [ "cache_a_complete_example_sol_df" ] }, { "page": "calc_aris", "title": "Construct an ARI matrix storing inter-solution similarities", "topics": [ "calc_aris" ] }, { "page": "calc_assoc_pval_matrix", "title": "Calculate p-values for all pairwise associations of features in a data list", "topics": [ "calc_assoc_pval_matrix" ] }, { "page": "calc_nmis", "title": "Calculate feature NMIs for a data list and a solutions data frame", "topics": [ "calc_nmis" ] }, { "page": "calculate_coclustering", "title": "Calculate co-clustering data", "topics": [ "calculate_coclustering" ] }, { "page": "cancer_diagnosis_df", "title": "Mock diagnosis data", "topics": [ "cancer_diagnosis_df" ] }, { "page": "cell_significance_fn", "title": "Place significance stars on ComplexHeatmap cells", "topics": [ "cell_significance_fn" ] }, { "page": "check_dataless_annotations", "title": "Helper function to stop annotation building when no data was provided", "topics": [ "check_dataless_annotations" ] }, { "page": "check_hm_dependencies", "title": "Check for ComplexHeatmap and circlize dependencies", "topics": [ "check_hm_dependencies" ] }, { "page": "check_similarity_matrices", "title": "Check validity of similarity matrices", "topics": [ "check_similarity_matrices" ] }, { "page": "clust_fns", "title": "Built-in clustering algorithms", "topics": [ "clust_fns", "spectral_eigen", "spectral_eigen_classic", "spectral_eight", "spectral_five", "spectral_four", "spectral_nine", "spectral_rot", "spectral_rot_classic", "spectral_seven", "spectral_six", "spectral_ten", "spectral_three", "spectral_two" ] }, { "page": "clust_fns_list", "title": "Build a clustering algorithms list", "topics": [ "clust_fns_list" ] }, { "page": "cocluster_density", "title": "Density plot of co-clustering stability across subsampled data", "topics": [ "cocluster_density" ] }, { "page": "cocluster_heatmap", "title": "Heatmap of observation co-clustering across resampled data", "topics": [ "cocluster_heatmap" ] }, { "page": "colour_scale", "title": "Return a colour ramp for a given vector", "topics": [ "colour_scale" ] }, { "page": "cort_sa", "title": "Mock ABCD cortical surface area data", "topics": [ "cort_sa" ] }, { "page": "cort_t", "title": "Mock ABCD cortical thickness data", "topics": [ "cort_t" ] }, { "page": "data_list", "title": "Build a 'data_list' class object", "topics": [ "data_list" ] }, { "page": "depress", "title": "Mock ABCD depression data", "topics": [ "depress" ] }, { "page": "diagnosis_df", "title": "Mock diagnosis data", "topics": [ "diagnosis_df" ] }, { "page": "dist_fns", "title": "Built-in distance functions", "topics": [ "dist_fns", "euclidean_distance", "gower_distance", "hamming_distance", "sew_euclidean_distance", "sn_euclidean_distance" ] }, { "page": "dist_fns_list", "title": "Build a distance metrics list", "topics": [ "dist_fns_list" ] }, { "page": "dlapply", "title": "Apply-like function for data list objects", "topics": [ "dlapply" ] }, { "page": "dplyr_row_slice.ext_solutions_df", "title": "Function to extend dplyr to extended solutions data frame objects", "topics": [ "dplyr_row_slice.ext_solutions_df" ] }, { "page": "dplyr_row_slice.solutions_df", "title": "Function to extend dplyr to solutions data frame objects", "topics": [ "dplyr_row_slice.solutions_df" ] }, { "page": "esm_manhattan_plot", "title": "Manhattan plot of feature-cluster association p-values", "topics": [ "esm_manhattan_plot" ] }, { "page": "estimate_nclust_given_graph", "title": "Estimate number of clusters for a similarity matrix", "topics": [ "estimate_nclust_given_graph" ] }, { "page": "expression_df", "title": "Modification of SNFtool mock data frame \"Data1\"", "topics": [ "expression_df" ] }, { "page": "extend_solutions", "title": "Extend a solutions data frame to include outcome evaluations", "topics": [ "extend_solutions" ] }, { "page": "fav_colour", "title": "Mock ABCD \"colour\" data", "topics": [ "fav_colour" ] }, { "page": "gender_df", "title": "Mock gender data", "topics": [ "gender_df" ] }, { "page": "get_complete_uids", "title": "Pull complete-data UIDs from a list of data frames", "topics": [ "get_complete_uids" ] }, { "page": "get_heatmap_order", "title": "Return the row or column ordering present in a heatmap", "topics": [ "get_heatmap_order" ] }, { "page": "get_matrix_order", "title": "Return the hierarchical clustering order of a matrix", "topics": [ "get_matrix_order" ] }, { "page": "get_pvals", "title": "Get p-values from an extended solutions data frame", "topics": [ "get_pvals" ] }, { "page": "get_representative_solutions", "title": "Extract representative solutions from a matrix of ARIs", "topics": [ "get_representative_solutions" ] }, { "page": "income", "title": "Mock ABCD income data", "topics": [ "income" ] }, { "page": "is_data_list", "title": "Test if the object is a data list", "topics": [ "is_data_list" ] }, { "page": "jitter_plot", "title": "Jitter plot separating a feature by cluster", "topics": [ "jitter_plot" ] }, { "page": "label_meta_clusters", "title": "Assign meta cluster labels to rows of a solutions data frame or extended solutions data frame", "topics": [ "label_meta_clusters" ] }, { "page": "label_propagate", "title": "Label propagate cluster solutions to non-clustered observations", "topics": [ "label_propagate" ] }, { "page": "linear_adjust", "title": "Linearly correct data list by features with unwanted signal", "topics": [ "linear_adjust" ] }, { "page": "mc_manhattan_plot", "title": "Manhattan plot of feature-meta cluster association p-values", "topics": [ "mc_manhattan_plot" ] }, { "page": "merge_df_list", "title": "Merge list of data frames into a single data frame", "topics": [ "merge_df_list" ] }, { "page": "merge.clust_fns_list", "title": "Merge 'clust_fns_list' objects", "topics": [ "merge.clust_fns_list" ] }, { "page": "merge.data_list", "title": "Merge observations between two compatible data lists", "topics": [ "merge.data_list" ] }, { "page": "merge.dist_fns_list", "title": "Merge 'dist_fns_list' objects", "topics": [ "merge.dist_fns_list" ] }, { "page": "merge.ext_solutions_df", "title": "Merge 'ext_solutions_df' objects", "topics": [ "merge.ext_solutions_df" ] }, { "page": "merge.settings_df", "title": "Merge 'settings_df' objects", "topics": [ "merge.settings_df" ] }, { "page": "merge.sim_mats_list", "title": "Merge 'sim_mats_list' objects", "topics": [ "merge.sim_mats_list" ] }, { "page": "merge.snf_config", "title": "Merge method for SNF config objects", "topics": [ "merge.snf_config" ] }, { "page": "merge.solutions_df", "title": "Merge 'solutions_df' objects", "topics": [ "merge.solutions_df" ] }, { "page": "merge.t_ext_solutions_df", "title": "Merge 't_ext_solutions_df' objects", "topics": [ "merge.t_ext_solutions_df" ] }, { "page": "merge.t_solutions_df", "title": "Merge 't_solutions_df' objects", "topics": [ "merge.t_solutions_df" ] }, { "page": "merge.weights_matrix", "title": "Merge 'weights_matrix' objects", "topics": [ "merge.weights_matrix" ] }, { "page": "methylation_df", "title": "Modification of SNFtool mock data frame \"Data2\"", "topics": [ "methylation_df" ] }, { "page": "mock_ari_matrix", "title": "Mock example of an 'ari_matrix' metasnf object", "topics": [ "mock_ari_matrix" ] }, { "page": "mock_clust_fns_list", "title": "Mock example of a 'clust_fns_list' metasnf object", "topics": [ "mock_clust_fns_list" ] }, { "page": "mock_data_list", "title": "Mock example of a 'data_list' metasnf object", "topics": [ "mock_data_list" ] }, { "page": "mock_dist_fns_list", "title": "Mock example of a 'dist_fns_list' metasnf object", "topics": [ "mock_dist_fns_list" ] }, { "page": "mock_ext_solutions_df", "title": "Mock example of a 'ext_solutions_df' metasnf object", "topics": [ "mock_ext_solutions_df" ] }, { "page": "mock_mc_solutions_df", "title": "Mock example of a 'mc_solutions_df' metasnf object", "topics": [ "mock_mc_solutions_df" ] }, { "page": "mock_rep_solutions_df", "title": "Mock example of a 'rep_solutions_df' metasnf object", "topics": [ "mock_rep_solutions_df" ] }, { "page": "mock_settings_df", "title": "Mock example of a 'settings_df' metasnf object", "topics": [ "mock_settings_df" ] }, { "page": "mock_snf_config", "title": "Mock example of a 'snf_config' metasnf object", "topics": [ "mock_snf_config" ] }, { "page": "mock_solutions_df", "title": "Mock example of a 'solutions_df' metasnf object", "topics": [ "mock_solutions_df" ] }, { "page": "mock_t_solutions_df", "title": "Mock example of a 't_solutions_df' metasnf object", "topics": [ "mock_t_solutions_df" ] }, { "page": "mock_weights_matrix", "title": "Mock example of a 'weights_matrix' metasnf object", "topics": [ "mock_weights_matrix" ] }, { "page": "new_solutions_df", "title": "Constructor for 'solutions_df' class object", "topics": [ "new_solutions_df" ] }, { "page": "plot.ari_matrix", "title": "Heatmap of pairwise adjusted rand indices between solutions", "topics": [ "meta_cluster_heatmap", "plot.ari_matrix" ] }, { "page": "plot.data_list", "title": "Plot of feature values in a data list", "topics": [ "plot.data_list" ] }, { "page": "plot.ext_solutions_df", "title": "Plot of cluster assignments in an extended solutions data frame", "topics": [ "plot.ext_solutions_df", "plot.t_ext_solutions_df" ] }, { "page": "plot.snf_config", "title": "Heatmap for visualizing an SNF config", "topics": [ "config_heatmap", "plot.settings_df", "plot.snf_config", "plot.weights_matrix" ] }, { "page": "plot.solutions_df", "title": "Plot of cluster assignments in a solutions data frame", "topics": [ "plot.solutions_df", "plot.t_solutions_df" ] }, { "page": "print.ari_matrix", "title": "Print method for class 'ari_matrix'", "topics": [ "print.ari_matrix" ] }, { "page": "print.clust_fns_list", "title": "Print method for class 'clust_fns_list'", "topics": [ "print.clust_fns_list" ] }, { "page": "print.data_list", "title": "Print method for class 'data_list'", "topics": [ "print.data_list" ] }, { "page": "print.dist_fns_list", "title": "Print method for class 'dist_fns_list'", "topics": [ "print.dist_fns_list" ] }, { "page": "print.ext_solutions_df", "title": "Print method for class 'ext_solutions_df'", "topics": [ "print.ext_solutions_df" ] }, { "page": "print.settings_df", "title": "Print method for class 'settings_df'", "topics": [ "print.settings_df" ] }, { "page": "print.sim_mats_list", "title": "Print method for class 'sim_mats_list'", "topics": [ "print.sim_mats_list" ] }, { "page": "print.snf_config", "title": "Print method for class 'snf_config'", "topics": [ "print.snf_config" ] }, { "page": "print.solutions_df", "title": "Print method for class 'solutions_df'", "topics": [ "print.solutions_df" ] }, { "page": "print.t_ext_solutions_df", "title": "Print method for class 't_ext_solutions_df'", "topics": [ "print.t_ext_solutions_df" ] }, { "page": "print.t_solutions_df", "title": "Print method for class 't_solutions_df'", "topics": [ "print.t_solutions_df" ] }, { "page": "print.weights_matrix", "title": "Print method for class 'weights_matrix'", "topics": [ "print.weights_matrix" ] }, { "page": "pubertal", "title": "Mock ABCD pubertal status data", "topics": [ "pubertal" ] }, { "page": "pval_heatmap", "title": "Heatmap of p-values", "topics": [ "pval_heatmap" ] }, { "page": "quality_measures", "title": "Quality metrics", "topics": [ "calculate_db_indices", "calculate_dunn_indices", "calculate_silhouettes", "quality_measures" ] }, { "page": "rbind.ext_solutions_df", "title": "Row-binding of solutions data frame class objects", "topics": [ "rbind.ext_solutions_df" ] }, { "page": "rbind.solutions_df", "title": "Row-binding of solutions data frame class objects", "topics": [ "rbind.solutions_df" ] }, { "page": "rbind.t_solutions_df", "title": "Row-binding of t_solutions_df class objects", "topics": [ "rbind.t_solutions_df" ] }, { "page": "rbind.weights_matrix", "title": "Row-bind weights matrices", "topics": [ "rbind.weights_matrix" ] }, { "page": "rename_dl", "title": "Rename features in a data list", "topics": [ "rename_dl" ] }, { "page": "resample", "title": "Helper resampling function found in ?sample", "topics": [ "resample" ] }, { "page": "save_heatmap", "title": "Save a heatmap object to a file", "topics": [ "save_heatmap" ] }, { "page": "settings_df", "title": "Build a settings data frame", "topics": [ "settings_df" ] }, { "page": "shiny_annotator", "title": "Launch a shiny app to identify meta cluster boundaries", "topics": [ "shiny_annotator" ] }, { "page": "sim_mats_list", "title": "Create or extract a 'sim_mats_list' class object", "topics": [ "sim_mats_list" ] }, { "page": "similarity_matrix_heatmap", "title": "Plot heatmap of similarity matrix", "topics": [ "similarity_matrix_heatmap" ] }, { "page": "siw_euclidean_distance", "title": "Squared (including weights) Euclidean distance", "topics": [ "siw_euclidean_distance" ] }, { "page": "snf_config", "title": "Define configuration for generating a set of SNF-based cluster solutions", "topics": [ "snf_config" ] }, { "page": "split_parser", "title": "Helper function to determine which row and columns to split on", "topics": [ "split_parser" ] }, { "page": "str.ari_matrix", "title": "Structure of a 'ari_matrix' object", "topics": [ "str.ari_matrix" ] }, { "page": "str.clust_fns_list", "title": "Structure of a 'clust_fns_list' object", "topics": [ "str.clust_fns_list" ] }, { "page": "str.data_list", "title": "Structure of a 'data_list' object", "topics": [ "str.data_list" ] }, { "page": "str.dist_fns_list", "title": "Structure of a 'dist_fns_list' object", "topics": [ "str.dist_fns_list" ] }, { "page": "str.ext_solutions_df", "title": "Structure of a 'ext_solutions_df' object", "topics": [ "str.ext_solutions_df" ] }, { "page": "str.settings_df", "title": "Structure of a 'settings_df' object", "topics": [ "str.settings_df" ] }, { "page": "str.sim_mats_list", "title": "Structure of a 'sim_mats_list' object", "topics": [ "str.sim_mats_list" ] }, { "page": "str.snf_config", "title": "Structure of a 'snf_config' object", "topics": [ "str.snf_config" ] }, { "page": "str.solutions_df", "title": "Structure of a 'solutions_df' object", "topics": [ "str.solutions_df" ] }, { "page": "str.t_ext_solutions_df", "title": "Structure of a 't_ext_solutions_df' object", "topics": [ "str.t_ext_solutions_df" ] }, { "page": "str.t_solutions_df", "title": "Structure of a 't_solutions_df' object", "topics": [ "str.t_solutions_df" ] }, { "page": "str.weights_matrix", "title": "Structure of a 'weights_matrix' object", "topics": [ "str.weights_matrix" ] }, { "page": "subc_v", "title": "Mock ABCD subcortical volumes data", "topics": [ "subc_v" ] }, { "page": "subsample_dl", "title": "Create subsamples of a data list", "topics": [ "subsample_dl" ] }, { "page": "subsample_pairwise_aris", "title": "Calculate pairwise adjusted Rand indices across subsamples of data", "topics": [ "subsample_pairwise_aris" ] }, { "page": "summary.ari_matrix", "title": "Summary method for class 'ari_matrix'", "topics": [ "summary.ari_matrix" ] }, { "page": "summary.clust_fns_list", "title": "Summary method for class 'clust_fns_list'", "topics": [ "summary.clust_fns_list" ] }, { "page": "summary.data_list", "title": "Summary method for class 'data_list'", "topics": [ "summary.data_list" ] }, { "page": "summary.dist_fns_list", "title": "Summary method for class 'dist_fns_list'", "topics": [ "summary.dist_fns_list" ] }, { "page": "summary.ext_solutions_df", "title": "Summary method for class 'ext_solutions_df'", "topics": [ "summary.ext_solutions_df" ] }, { "page": "summary.settings_df", "title": "Summary method for class 'settings_df'", "topics": [ "summary.settings_df" ] }, { "page": "summary.sim_mats_list", "title": "Summary method for class 'sim_mats_list'", "topics": [ "summary.sim_mats_list" ] }, { "page": "summary.snf_config", "title": "Summary method for class 'snf_config'", "topics": [ "summary.snf_config" ] }, { "page": "summary.solutions_df", "title": "Summary method for class 'solutions_df'", "topics": [ "summary.solutions_df" ] }, { "page": "summary.t_ext_solutions_df", "title": "Summary method for class 't_ext_solutions_df'", "topics": [ "summary.t_ext_solutions_df" ] }, { "page": "summary.t_solutions_df", "title": "Summary method for class 't_solutions_df'", "topics": [ "summary.t_solutions_df" ] }, { "page": "summary.weights_matrix", "title": "Summary method for class 'weights_matrix'", "topics": [ "summary.weights_matrix" ] }, { "page": "train_test_assign", "title": "Training and testing split", "topics": [ "train_test_assign" ] }, { "page": "uids", "title": "Pull UIDs from an object", "topics": [ "uids" ] }, { "page": "validate_solutions_df", "title": "Validator for 'solutions_df' class object", "topics": [ "validate_solutions_df" ] }, { "page": "var_manhattan_plot", "title": "Manhattan plot of feature-feature association p-values", "topics": [ "var_manhattan_plot" ] }, { "page": "weights_matrix", "title": "Generate a matrix to store feature weights", "topics": [ "weights_matrix" ] } ], "_readme": "https://github.com/branchlab/metasnf/raw/HEAD/README.md", "_rundeps": [ "alluvial", "cli", "cluster", "cpp11", "data.table", "digest", "dplyr", "ExPosition", "farver", "generics", "ggplot2", "glue", "gtable", "isoband", "labeling", "lifecycle", "magrittr", "MASS", "mclust", "pillar", "pkgconfig", "prettyGraphs", "progressr", "purrr", "R6", "RColorBrewer", "rlang", "S7", "scales", "SNFtool", "stringi", "stringr", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "viridisLite", "withr" ], "_vignettes": [ { "source": "a_complete_example.Rmd", "filename": "a_complete_example.html", "title": "A Complete Example", "engine": "knitr::rmarkdown", "headings": [ "Data Set-up", "Pre-processing", "Generating the data list", "Defining sets of hyperparameters to use for SNF and clustering", "The settings data frame", "Other parts of the SNF config", "Running SNF and clustering", "Identifying and visualizing meta clusters", "Characterizing cluster solutions", "Calculating associations between cluster solutions and initial data", "Visualizing feature associations with meta clustering results", "Characterizing individual solutions representative of each meta cluster", "Relating results to metasnf hyperparameters", "Quality measures", "Stability measures", "Evaluating separation across \"target features\" of importance", "Validating results with label propagation", "References" ], "created": "2024-05-17 20:15:15", "modified": "2026-06-17 18:56:07", "commits": 20 }, { "source": "a_simple_example.Rmd", "filename": "a_simple_example.html", "title": "A Simple Example", "engine": "knitr::rmarkdown", "headings": [ "The original SNF example", "1. 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