{ "_id": "6a1053acacfb0bcc41ca1d9f", "Package": "YAPSA", "Type": "Package", "Title": "Yet Another Package for Signature Analysis", "Version": "1.39.0", "Date": "2025-11-28", "Authors@R": "c(person(\"Daniel\", \"Huebschmann\", role = c(\"aut\", \"cre\"), email = \"huebschmann.daniel@googlemail.com\"),\nperson(\"Lea\", \"Jopp-Saile\", role = \"aut\"),\nperson(\"Carolin\", \"Andresen\", role = \"aut\"),\nperson(\"Zuguang\", \"Gu\", role = \"aut\"),\nperson(\"Matthias\", \"Schlesner\", role = \"aut\"))", "Description": "This package provides functions and routines for\nsupervised analyses of mutational signatures (i.e., the\nsignatures have to be known, cf. L. Alexandrov et al., Nature\n2013 and L. Alexandrov et al., Bioaxiv 2018). In particular,\nthe family of functions LCD (LCD = linear combination\ndecomposition) can use optimal signature-specific cutoffs which\ntakes care of different detectability of the different\nsignatures. Moreover, the package provides different sets of\nmutational signatures, including the COSMIC and PCAWG SNV\nsignatures and the PCAWG Indel signatures; the latter infering\nthat with YAPSA, the concept of supervised analysis of\nmutational signatures is extended to Indel signatures. YAPSA\nalso provides confidence intervals as computed by profile\nlikelihoods and can perform signature analysis on a stratified\nmutational catalogue (SMC = stratify mutational catalogue) in\norder to analyze enrichment and depletion patterns for the\nsignatures in different strata.", "License": "GPL-3", "VignetteBuilder": "knitr", "LazyLoad": "yes", "biocViews": "Sequencing, DNASeq, SomaticMutation, Visualization,\nClustering, GenomicVariation, StatisticalMethod,\nBiologicalQuestion", "RoxygenNote": "7.2.3", "Encoding": "UTF-8", "Config/pak/sysreqs": "cmake libgmp3-dev make libbz2-dev libicu-dev\nliblzma-dev libpng-dev libuv1-dev libxml2-dev libmpfr-dev\nlibssl-dev perl xz-utils zlib1g-dev", "Repository": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:44:05 UTC", "RemoteUrl": "https://github.com/bioc/YAPSA", "RemoteRef": "HEAD", "RemoteSha": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-09 09:00:51 UTC", "User": "root" }, "Author": "Daniel Huebschmann [aut, cre],\nLea Jopp-Saile [aut],\nCarolin Andresen [aut],\nZuguang Gu [aut],\nMatthias Schlesner [aut]", "Maintainer": "Daniel Huebschmann ", "MD5sum": "dcb01665435230cc0f5780dfc897643e", "_user": "bioc", "_type": "src", "_file": "YAPSA_1.39.0.tar.gz", "_fileid": "ef2076199ed8f7b3a1a3796884b6746fbe052c52d83ce3439acec2ce37c8cd15", "_filesize": 3541951, "_sha256": "ef2076199ed8f7b3a1a3796884b6746fbe052c52d83ce3439acec2ce37c8cd15", "_created": "2026-05-09T09:00:51.000Z", "_published": "2026-05-22T13:01:32.036Z", "_jobs": [ { "job": 77384075250, "time": 331, "config": "bioc-checks", "r": "4.6.0", "check": "WARNING", "artifact": "6894535921" }, { "job": 77384075137, "time": 683, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "6894566989" }, { "job": 77384075646, "time": 649, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "6894563832" }, { "job": 77384075802, "time": 357, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "NOTE", "artifact": "6894537637" }, { "job": 77384075267, "time": 499, "config": "macos-release-arm64", "r": "4.6.0", "check": "NOTE", "artifact": "6894550212" }, { "job": 77384075052, "time": 570, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "6894504098" }, { "job": 77384074629, "time": 253, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7161088695" }, { "job": 77384075145, "time": 531, "config": "windows-devel", "r": "4.7.0", "check": "NOTE", "artifact": "6894553117" }, { "job": 77384075918, "time": 532, "config": "windows-oldrel", "r": "4.5.3", "check": "NOTE", "artifact": "6894553208" }, { "job": 77384075860, "time": 482, "config": "windows-release", "r": "4.6.0", "check": "NOTE", "artifact": "6894548560" } ], "_bioccheck": { "error": 0, "warning": 1, "note": 18 }, "_buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/bioc/YAPSA", "_commit": { "id": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "author": "A Wokaty ", "committer": "A Wokaty ", "message": "bump x.y.z version to odd y following creation of RELEASE_3_23 branch\n", "time": 1777380245 }, "_maintainer": { "name": "Daniel Huebschmann", "email": "huebschmann.daniel@googlemail.com", "login": "huebschm", "uuid": 20400643 }, "_distro": "noble", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.0.0", "role": "Depends" }, { "package": "GenomicRanges", "role": "Depends" }, { "package": "ggplot2", "role": "Depends" }, { "package": "grid", "role": "Depends" }, { "package": "limSolve", "role": "Imports" }, { "package": "SomaticSignatures", "role": "Imports" }, { "package": "VariantAnnotation", "role": "Imports" }, { "package": "Seqinfo", "role": "Imports" }, { "package": "reshape2", "role": "Imports" }, { "package": "gridExtra", "role": "Imports" }, { "package": "corrplot", "role": "Imports" }, { "package": "dendextend", "role": "Imports" }, { "package": "GetoptLong", "role": "Imports" }, { "package": "circlize", "role": "Imports" }, { "package": "gtrellis", "role": "Imports" }, { "package": "doParallel", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "PMCMRplus", "role": "Imports" }, { "package": "ggbeeswarm", "role": "Imports" }, { "package": "ComplexHeatmap", "role": "Imports" }, { "package": "KEGGREST", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "Biostrings", "role": "Imports" }, { "package": "BSgenome.Hsapiens.UCSC.hg19", "role": "Imports" }, { "package": "magrittr", "role": "Imports" }, { "package": "pracma", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "testthat", "role": "Suggests" }, { "package": "BiocStyle", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" } ], "_owner": "bioc", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-30", "n": 1 }, { "week": "2025-44", "n": 2 }, { "week": "2025-48", "n": 2 }, { "week": "2026-18", "n": 2 } ], "_tags": [], "_bioc": [ { "branch": "devel", "version": "1.39.0", "bioc": "3.24" }, { "branch": "release", "version": "1.38.0", "bioc": "3.23" } ], "_topics": [ "sequencing", "dnaseq", "somaticmutation", "visualization", "clustering", "genomicvariation", "statisticalmethod", "biologicalquestion" ], "_userbio": { "uuid": 2286807, "type": "organization", "name": "Bioconductor", "description": "Software for the analysis and comprehension of high-throughput genomic data" }, "_downloads": { "count": 634, "source": "https://www.bioconductor.org/packages/stats/bioc/YAPSA" }, "_mentions": 5, "_searchresults": 73, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/YAPSA.html", "manual.pdf" ], "_realowner": "bioc", "_cranurl": false, "_exports": [ "add_annotation", "add_as_fist_to_list", "aggregate_exposures_by_category", "annotate_intermut_dist_cohort", "annotate_intermut_dist_PID", "annotation_exposures_barplot", "annotation_exposures_list_barplot", "annotation_heatmap_exposures", "attribute_nucleotide_exchanges", "attribute_sequence_contex_indel", "attribution_of_indels", "average_over_present", "build_gene_list_for_pathway", "classify_indels", "compare_exposures", "compare_expousre_sets", "compare_sets", "compare_SMCs", "compare_to_catalogues", "complex_heatmap_exposures", "compute_comparison_stat_df", "computeLogLik", "confidence_indel_calulation", "confidence_indel_only_calulation", "confIntExp", "correct_rounded", "cosineDist", "cosineMatchDist", "create_indel_mut_cat_from_df", "create_indel_mutation_catalogue_from_df", "create_mutation_catalogue_from_df", "create_mutation_catalogue_from_VR", "cut_breaks_as_intervals", "deriveSigInd_df", "disambiguateVector", "enrichSigs", "exposures_barplot", "extract_names_from_gene_list", "find_affected_PIDs", "get_extreme_PIDs", "getSequenceContext", "hclust_exposures", "LCD", "LCD_complex_cutoff", "LCD_complex_cutoff_combined", "LCD_complex_cutoff_consensus", "LCD_complex_cutoff_perPID", "LCD_extractCohort_callPerPID", "logLikelihood", "make_catalogue_strata_df", "make_comparison_matrix", "make_strata_df", "make_subgroups_df", "makeVRangesFromDataFrame", "melt_exposures", "merge_exposures", "normalize_df_per_dim", "normalizeMotifs_otherRownames", "plot_exposures", "plot_relative_exposures", "plot_SMC", "plot_strata", "plotExchangeSpectra", "plotExchangeSpectra_indel", "plotExposuresConfidence", "plotExposuresConfidence_indel", "read_entry", "read_list", "relateSigs", "repeat_df", "round_precision", "run_annotate_vcf_pl", "run_comparison_catalogues", "run_comparison_general", "run_kmer_frequency_correction", "run_kmer_frequency_normalization", "run_plot_strata_general", "run_SMC", "sd_over_present", "shapiro_if_possible", "SMC", "SMC_perPID", "split_exposures_by_subgroups", "stat_plot_subgroups", "stat_test_SMC", "stat_test_subgroups", "stderrmean", "stderrmean_over_present", "sum_over_list_of_df", "test_exposureAffected", "test_gene_list_in_exposures", "testSigs", "transform_rownames_deconstructSigs_to_YAPSA", "transform_rownames_MATLAB_to_R", "transform_rownames_nature_to_R", "transform_rownames_R_to_MATLAB", "transform_rownames_YAPSA_to_deconstructSigs", "translate_to_1kG", "translate_to_hg19", "trellis_rainfall_plot", "variateExp", "variateExpSingle" ], "_datasets": [ { "name": "AlexCosmicArtif_sig_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "AC1", "AC2", "AC3", "AC4", "AC5", "AC6", "AC7", "AC8", "AC9", "AC10", "AC11", "AC12", "AC13", "AC14", "AC15", "AC16", "AC17", "AC18", "AC19", "AC20", "AC21", "AC22", "AC23", "AC24", "AC25", "AC26", "AC27", "AC28", "AC29", "AC30", "AR1", "AR2", "AR3", "AU1", "AU2" ], "rows": 96, "table": true, "tojson": true }, { "name": "AlexCosmicArtif_sigInd_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process", "cat.coarse", "cat.medium", "cat.fine", "cat.putative" ], "rows": 36, "table": true, "tojson": true }, { "name": "AlexCosmicValid_sig_df", "title": "Data for mutational signatures", "object": "AlexCosmicValid_sig_df", "file": "AlexCosmicValid_sig_df.rda", "class": [ "data.frame" ], "fields": [ "Substitution.Type", "Trinucleotide", "Somatic.Mutation.Type", "Signature.1", "Signature.2", "Signature.3", "Signature.4", "Signature.5", "Signature.6", "Signature.7", "Signature.8", "Signature.9", "Signature.10", "Signature.11", "Signature.12", "Signature.13", "Signature.14", "Signature.15", "Signature.16", "Signature.17", "Signature.18", "Signature.19", "Signature.20", "Signature.21", "Signature.22", "Signature.23", "Signature.24", "Signature.25", "Signature.26", "Signature.27", "Signature.28", "Signature.29", "Signature.30", "X", "X.1", "X.2", "X.3", "X.4", "X.5", "X.6" ], "rows": 96, "table": true, "tojson": true }, { "name": "AlexCosmicValid_sig_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "AC1", "AC2", "AC3", "AC4", "AC5", "AC6", "AC7", "AC8", "AC9", "AC10", "AC11", "AC12", "AC13", "AC14", "AC15", "AC16", "AC17", "AC18", "AC19", "AC20", "AC21", "AC22", "AC23", "AC24", "AC25", "AC26", "AC27", "AC28", "AC29", "AC30" ], "rows": 96, "table": true, "tojson": true }, { "name": "AlexCosmicValid_sigInd_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process", "cat.coarse", "cat.medium", "cat.fine", "cat.putative" ], "rows": 31, "table": true, "tojson": true }, { "name": "AlexInitialArtif_sig_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "A1A", "A1B", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "AR1", "AR2", "AR3", "AU1", "AU2" ], "rows": 96, "table": true, "tojson": true }, { "name": "AlexInitialArtif_sigInd_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process", "cat.coarse", "cat.medium", "cat.fine", "cat.putative" ], "rows": 28, "table": true, "tojson": true }, { "name": "AlexInitialValid_sig_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "A1A", "A1B", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21" ], "rows": 96, "table": true, "tojson": true }, { "name": "AlexInitialValid_sigInd_df", "title": "Data for mutational signatures", "object": "sigs", "file": "sigs.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process", "cat.coarse", "cat.medium", "cat.fine", "cat.putative" ], "rows": 23, "table": true, "tojson": true }, { "name": "chosen_signatures_indices_df", "title": "Test and example data", "object": "lymphoma_cohort_LCD_results", "file": "lymphoma_cohort_LCD_results.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process" ], "rows": 7, "table": true, "tojson": true }, { "name": "COSMIC_subgroups_df", "title": "Test and example data", "object": "lymphoma_cohort_LCD_results", "file": "lymphoma_cohort_LCD_results.rda", "class": [ "data.frame" ], "fields": [ "PID", "subgroup", "sum", "compl_sum", "index", "col", "cluster", "cluster_col" ], "rows": 23, "table": true, "tojson": true }, { "name": "cutoffCosmicArtif_abs_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "AC1", "AC2", "AC3", "AC4", "AC5", "AC6", "AC7", "AC8", "AC9", "AC10", "AC11", "AC12", "AC13", "AC14", "AC15", "AC16", "AC17", "AC18", "AC19", "AC20", "AC21", "AC22", "AC23", "AC24", "AC25", "AC26", "AC27", "AC28", "AC29", "AC30", "AR1", "AR2", "AR3", "AU1", "AU2" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffCosmicArtif_rel_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "AC1", "AC2", "AC3", "AC4", "AC5", "AC6", "AC7", "AC8", "AC9", "AC10", "AC11", "AC12", "AC13", "AC14", "AC15", "AC16", "AC17", "AC18", "AC19", "AC20", "AC21", "AC22", "AC23", "AC24", "AC25", "AC26", "AC27", "AC28", "AC29", "AC30", "AR1", "AR2", "AR3", "AU1", "AU2" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffCosmicValid_abs_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "AC1", "AC2", "AC3", "AC4", "AC5", "AC6", "AC7", "AC8", "AC9", "AC10", "AC11", "AC12", "AC13", "AC14", "AC15", "AC16", "AC17", "AC18", "AC19", "AC20", "AC21", "AC22", "AC23", "AC24", "AC25", "AC26", "AC27", "AC28", "AC29", "AC30" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffCosmicValid_rel_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "AC1", "AC2", "AC3", "AC4", "AC5", "AC6", "AC7", "AC8", "AC9", "AC10", "AC11", "AC12", "AC13", "AC14", "AC15", "AC16", "AC17", "AC18", "AC19", "AC20", "AC21", "AC22", "AC23", "AC24", "AC25", "AC26", "AC27", "AC28", "AC29", "AC30" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffInitialArtif_abs_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "A1A", "A1B", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "AR1", "AR2", "AR3", "AU1", "AU2" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffInitialArtif_rel_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "A1A", "A1B", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "AR1", "AR2", "AR3", "AU1", "AU2" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffInitialValid_abs_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "A1A", "A1B", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffInitialValid_rel_df", "title": "Cutoffs for a supervised analysis of mutational signatures.", "object": "cutoffs", "file": "cutoffs.rda", "class": [ "data.frame" ], "fields": [ "A1A", "A1B", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffPCAWG_ID_WGS_Pid_df", "title": "Opt. cutoffs, PCAWG SNV signatures, including artifacts", "object": "cutoffs_pcawg", "file": "cutoffs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "ID1", "ID2", "ID3", "ID4", "ID5", "ID6", "ID7", "ID8", "ID9", "ID10", "ID11", "ID12", "ID13", "ID14", "ID16", "ID17" ], "rows": 10, "table": true, "tojson": true }, { "name": "cutoffPCAWG_SBS_WGSWES_artifPid_df", "title": "Opt. cutoffs, PCAWG SNV signatures, including artifacts", "object": "cutoffs_pcawg", "file": "cutoffs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "SBS1", "SBS2", "SBS3", "SBS4", "SBS5", "SBS6", "SBS7a", "SBS7b", "SBS7c", "SBS7d", "SBS8", "SBS9", "SBS10a", "SBS10b", "SBS11", "SBS12", "SBS13", "SBS14", "SBS15", "SBS16", "SBS17a", "SBS17b", "SBS18", "SBS19", "SBS20", "SBS21", "SBS22", "SBS23", "SBS24", "SBS25", "SBS26", "SBS27", "SBS28", "SBS29", "SBS30", "SBS31", "SBS32", "SBS33", "SBS34", "SBS35", "SBS36", "SBS37", "SBS38", "SBS39", "SBS40", "SBS41", "SBS42", "SBS43", "SBS44", "SBS45", "SBS46", "SBS47", "SBS48", "SBS49", "SBS50", "SBS51", "SBS52", "SBS53", "SBS54", "SBS55", "SBS56", "SBS57", "SBS58", "SBS59", "SBS60" ], "rows": 16, "table": true, "tojson": true }, { "name": "cutoffPCAWG_SBS_WGSWES_realPid_df", "title": "Opt. cutoffs, PCAWG SNV signatures, including artifacts", "object": "cutoffs_pcawg", "file": "cutoffs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "SBS1", "SBS2", "SBS3", "SBS4", "SBS5", "SBS6", "SBS7a", "SBS7b", "SBS7c", "SBS7d", "SBS8", "SBS9", "SBS10a", "SBS10b", "SBS11", "SBS12", "SBS13", "SBS14", "SBS15", "SBS16", "SBS17a", "SBS17b", "SBS18", "SBS19", "SBS20", "SBS21", "SBS22", "SBS23", "SBS24", "SBS25", "SBS26", "SBS28", "SBS29", "SBS30", "SBS31", "SBS32", "SBS33", "SBS34", "SBS35", "SBS36", "SBS37", "SBS38", "SBS39", "SBS40", "SBS41", "SBS42", "SBS44" ], "rows": 15, "table": true, "tojson": true }, { "name": "exchange_colour_vector", "title": "Colours codes for displaying SNVs", "object": "exchange_colour_vector", "file": "exchange_colour_vector.rda", "class": [ "character" ], "fields": [], "table": false, "tojson": true }, { "name": "exome_mutCatRaw_df", "title": "Example mutational catalog for the exome vignette", "object": "smallCellLungCancerMutCat_NatureGenetics2012", "file": "smallCellLungCancerMutCat_NatureGenetics2012.rda", "class": [ "data.frame" ], "fields": [ "113368", "134398", "134413", "134417", "134421", "134426", "134427", "134430", "2334187", "2334188", "2334189", "2334191", "2334193", "2334195", "2334196", "2334199", "2334201", "2334202", "585203", "585205", "585208", "585210", "585223", "585258", "585260", "585265", "585267", "585270", "585272", "585276", "631052", "631056", "631060", "631064", "631076", "631084", "631092", "98687", "98711", "98735", "H1672", "H2171", "NCI-H209", "S00022", "S00050", "S00356", "S00472", "S00501", "S00539", "S00827", "S00830", "S00833", "S00836", "S00837", "S00841", "S00932", "S00933", "S00935", "S00936", "S00943", "S00944", "S00945", "S00946", "S00947", "S01366", "S01453", "S01494", "S01512", "S01563", "S01728" ], "rows": 96, "table": true, "tojson": true }, { "name": "GenomeOfNl_raw", "title": "Example data for the Indel vignette", "object": "GenomeOfNl_raw", "file": "GenomeOfNl_raw.rda", "class": [ "data.frame" ], "fields": [ "CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO" ], "rows": 591, "table": true, "tojson": true }, { "name": "lymphoma_Nature2013_COSMIC_cutoff_exposures_df", "title": "Test and example data", "object": "lymphoma_cohort_LCD_results", "file": "lymphoma_cohort_LCD_results.rda", "class": [ "data.frame" ], "fields": [ "4101316", "4105105", "4108101", "4112512", "4116738", "4119027", "4121361", "4125240", "4133511", "4135350", "4142267", "4158726", "4159170", "4163639", "4175837", "4177856", "4182393", "4189200", "4189998", "4190495", "4193278", "4194218", "4194891" ], "rows": 7, "table": true, "tojson": true }, { "name": "lymphoma_Nature2013_raw_df", "title": "Test and example data", "object": "lymphoma_Nature2013_raw", "file": "lymphoma_Nature2013_raw.rda", "class": [ "data.frame" ], "fields": [ "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8" ], "rows": 128639, "table": true, "tojson": true }, { "name": "lymphoma_PID_df", "title": "Test and example data", "object": "lymphoma_PID", "file": "lymphoma_PID.rda", "class": [ "data.frame" ], "fields": [ "subgroup" ], "rows": 23, "table": true, "tojson": true }, { "name": "lymphoma_test_df", "title": "Test and example data", "object": "lymphoma_test", "file": "lymphoma_test.rda", "class": [ "data.frame" ], "fields": [ "CHROM", "POS", "REF", "ALT", "PID", "SUBGROUP", "change", "dist", "col", "random_norm" ], "rows": 10113, "table": true, "tojson": true }, { "name": "lymphomaNature2013_mutCat_df", "title": "Example mutational catalog for the SNV vignette", "object": "lymphomaNature2013_mutCat_df", "file": "lymphomaNature2013_mutCat_df.rda", "class": [ "data.frame" ], "fields": [ "4101316", "4105105", "4108101", "4112512", "4116738", "4119027", "4121361", "4125240", "4133511", "4135350", "4142267", "4158726", "4159170", "4163639", "4175837", "4177856", "4182393", "4189200", "4189998", "4190495", "4193278", "4194218", "4194891" ], "rows": 96, "table": true, "tojson": true }, { "name": "MutCat_indel_df", "title": "Example mutational catalog for the Indel vignette", "object": "GenomeOfNl_MutCat", "file": "GenomeOfNl_MutCat.rda", "class": [ "data.frame" ], "fields": [ "PID_1", "PID_2", "PID_3", "PID_4", "PID_5", "PID_6", "PID_7", "PID_8", "PID_9", "PID_10", "PID_11", "PID_12", "PID_13", "PID_14", "PID_15" ], "rows": 83, "table": true, "tojson": true }, { "name": "PCAWG_SP_ID_sigInd_df", "title": "Data for PCAWG SNV signatures (COSMIC v3), including artifacts 'PCAWG_SP_SBS_sigs_Artif_df': Data frame of the signatures published by Alexandrov et al. (Biorxiv 2013) which were decomposed with the method SigProfiler. SNV signatures are labeled with SBS, single base signature. There are 67 signatures which constitute the columns, 47 of which were validated by a bayesian NFM mehtod, SignatureAnayzer. Validated signatures are SBS1-SBS26,SBS28-SBS42 and SBS44. SBS7 is split up into 7 a/b/c and d. SBS10 ans SBS17 are both split up into a and b. Resulting in a 47 validated sigantures. Please note, unlike the paper by Alexandrov et al. (Biorxiv 2018) the data sets do not contain a SBS84 and SBS85 as not all were availiablt to perfom supervised signature analysis. In total there are 96 different features and therefore 96 rows when dealing with a trinucleotide context.", "object": "sigs_pcawg", "file": "sigs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process" ], "rows": 16, "table": true, "tojson": true }, { "name": "PCAWG_SP_ID_sigs_df", "title": "Data for PCAWG SNV signatures (COSMIC v3), including artifacts 'PCAWG_SP_SBS_sigs_Artif_df': Data frame of the signatures published by Alexandrov et al. (Biorxiv 2013) which were decomposed with the method SigProfiler. SNV signatures are labeled with SBS, single base signature. There are 67 signatures which constitute the columns, 47 of which were validated by a bayesian NFM mehtod, SignatureAnayzer. Validated signatures are SBS1-SBS26,SBS28-SBS42 and SBS44. SBS7 is split up into 7 a/b/c and d. SBS10 ans SBS17 are both split up into a and b. Resulting in a 47 validated sigantures. Please note, unlike the paper by Alexandrov et al. (Biorxiv 2018) the data sets do not contain a SBS84 and SBS85 as not all were availiablt to perfom supervised signature analysis. In total there are 96 different features and therefore 96 rows when dealing with a trinucleotide context.", "object": "sigs_pcawg", "file": "sigs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "ID1", "ID2", "ID3", "ID4", "ID5", "ID6", "ID7", "ID8", "ID9", "ID10", "ID11", "ID12", "ID13", "ID14", "ID16", "ID17" ], "rows": 83, "table": true, "tojson": true }, { "name": "PCAWG_SP_SBS_sigInd_Artif_df", "title": "Data for PCAWG SNV signatures (COSMIC v3), including artifacts 'PCAWG_SP_SBS_sigs_Artif_df': Data frame of the signatures published by Alexandrov et al. (Biorxiv 2013) which were decomposed with the method SigProfiler. SNV signatures are labeled with SBS, single base signature. There are 67 signatures which constitute the columns, 47 of which were validated by a bayesian NFM mehtod, SignatureAnayzer. Validated signatures are SBS1-SBS26,SBS28-SBS42 and SBS44. SBS7 is split up into 7 a/b/c and d. SBS10 ans SBS17 are both split up into a and b. Resulting in a 47 validated sigantures. Please note, unlike the paper by Alexandrov et al. (Biorxiv 2018) the data sets do not contain a SBS84 and SBS85 as not all were availiablt to perfom supervised signature analysis. In total there are 96 different features and therefore 96 rows when dealing with a trinucleotide context.", "object": "sigs_pcawg", "file": "sigs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process" ], "rows": 67, "table": true, "tojson": true }, { "name": "PCAWG_SP_SBS_sigInd_Real_df", "title": "Data for PCAWG SNV signatures (COSMIC v3), including artifacts 'PCAWG_SP_SBS_sigs_Artif_df': Data frame of the signatures published by Alexandrov et al. (Biorxiv 2013) which were decomposed with the method SigProfiler. SNV signatures are labeled with SBS, single base signature. There are 67 signatures which constitute the columns, 47 of which were validated by a bayesian NFM mehtod, SignatureAnayzer. Validated signatures are SBS1-SBS26,SBS28-SBS42 and SBS44. SBS7 is split up into 7 a/b/c and d. SBS10 ans SBS17 are both split up into a and b. Resulting in a 47 validated sigantures. Please note, unlike the paper by Alexandrov et al. (Biorxiv 2018) the data sets do not contain a SBS84 and SBS85 as not all were availiablt to perfom supervised signature analysis. In total there are 96 different features and therefore 96 rows when dealing with a trinucleotide context.", "object": "sigs_pcawg", "file": "sigs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "sig", "index", "colour", "process" ], "rows": 47, "table": true, "tojson": true }, { "name": "PCAWG_SP_SBS_sigs_Artif_df", "title": "Data for PCAWG SNV signatures (COSMIC v3), including artifacts 'PCAWG_SP_SBS_sigs_Artif_df': Data frame of the signatures published by Alexandrov et al. (Biorxiv 2013) which were decomposed with the method SigProfiler. SNV signatures are labeled with SBS, single base signature. There are 67 signatures which constitute the columns, 47 of which were validated by a bayesian NFM mehtod, SignatureAnayzer. Validated signatures are SBS1-SBS26,SBS28-SBS42 and SBS44. SBS7 is split up into 7 a/b/c and d. SBS10 ans SBS17 are both split up into a and b. Resulting in a 47 validated sigantures. Please note, unlike the paper by Alexandrov et al. (Biorxiv 2018) the data sets do not contain a SBS84 and SBS85 as not all were availiablt to perfom supervised signature analysis. In total there are 96 different features and therefore 96 rows when dealing with a trinucleotide context.", "object": "sigs_pcawg", "file": "sigs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "SBS1", "SBS2", "SBS3", "SBS4", "SBS5", "SBS6", "SBS7a", "SBS7b", "SBS7c", "SBS7d", "SBS8", "SBS9", "SBS10a", "SBS10b", "SBS11", "SBS12", "SBS13", "SBS14", "SBS15", "SBS16", "SBS17a", "SBS17b", "SBS18", "SBS19", "SBS20", "SBS21", "SBS22", "SBS23", "SBS24", "SBS25", "SBS26", "SBS27", "SBS28", "SBS29", "SBS30", "SBS31", "SBS32", "SBS33", "SBS34", "SBS35", "SBS36", "SBS37", "SBS38", "SBS39", "SBS40", "SBS41", "SBS42", "SBS43", "SBS44", "SBS45", "SBS46", "SBS47", "SBS48", "SBS49", "SBS50", "SBS51", "SBS52", "SBS53", "SBS54", "SBS55", "SBS56", "SBS57", "SBS58", "SBS59", "SBS60", "SBS84", "SBS85" ], "rows": 96, "table": true, "tojson": true }, { "name": "PCAWG_SP_SBS_sigs_Real_df", "title": "Data for PCAWG SNV signatures (COSMIC v3), including artifacts 'PCAWG_SP_SBS_sigs_Artif_df': Data frame of the signatures published by Alexandrov et al. (Biorxiv 2013) which were decomposed with the method SigProfiler. SNV signatures are labeled with SBS, single base signature. There are 67 signatures which constitute the columns, 47 of which were validated by a bayesian NFM mehtod, SignatureAnayzer. Validated signatures are SBS1-SBS26,SBS28-SBS42 and SBS44. SBS7 is split up into 7 a/b/c and d. SBS10 ans SBS17 are both split up into a and b. Resulting in a 47 validated sigantures. Please note, unlike the paper by Alexandrov et al. (Biorxiv 2018) the data sets do not contain a SBS84 and SBS85 as not all were availiablt to perfom supervised signature analysis. In total there are 96 different features and therefore 96 rows when dealing with a trinucleotide context.", "object": "sigs_pcawg", "file": "sigs_pcawg.rda", "class": [ "data.frame" ], "fields": [ "SBS1", "SBS2", "SBS3", "SBS4", "SBS5", "SBS6", "SBS7a", "SBS7b", "SBS7c", "SBS7d", "SBS8", "SBS9", "SBS10a", "SBS10b", "SBS11", "SBS12", "SBS13", "SBS14", "SBS15", "SBS16", "SBS17a", "SBS17b", "SBS18", "SBS19", "SBS20", "SBS21", "SBS22", "SBS23", "SBS24", "SBS25", "SBS26", "SBS28", "SBS29", "SBS30", "SBS31", "SBS32", "SBS33", "SBS34", "SBS35", "SBS36", "SBS37", "SBS38", "SBS39", "SBS40", "SBS41", "SBS42", "SBS44" ], "rows": 96, "table": true, "tojson": true }, { "name": "rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df", "title": "Test and example data", "object": "lymphoma_cohort_LCD_results", "file": "lymphoma_cohort_LCD_results.rda", "class": [ "data.frame" ], "fields": [ "4101316", "4105105", "4108101", "4112512", "4116738", "4119027", "4121361", "4125240", "4133511", "4135350", "4142267", "4158726", "4159170", "4163639", "4175837", "4177856", "4182393", "4189200", "4189998", "4190495", "4193278", "4194218", "4194891" ], "rows": 7, "table": true, "tojson": true }, { "name": "targetCapture_cor_factors", "title": "Correction factors for different target capture kits", "object": "targetCapture_cor_factors", "file": "targetCapture_cor_factors.rda", "class": [ "list" ], "fields": [], "table": true, "tojson": false } ], "_help": [ { "page": "add_annotation", "title": "Add information to an annotation data structure", "topics": [ "add_annotation" ] }, { "page": "add_as_fist_to_list", "title": "Add an element as first entry to a list", "topics": [ "add_as_fist_to_list" ] }, { "page": "aggregate_exposures_by_category", "title": "Aggregate exposures by category", "topics": [ "aggregate_exposures_by_category" ] }, { "page": "annotate_intermut_dist_cohort", "title": "Annotate the intermutation distance of variants cohort-wide", "topics": [ "annotate_intermut_dist_cohort" ] }, { "page": "annotate_intermut_dist_PID", "title": "Annotate the intermutation distance of variants per PID", "topics": [ "annotate_intermut_dist_PID" ] }, { "page": "annotation_exposures_barplot", "title": "Plot the exposures of a cohort with different layers of annotation", "topics": [ "annotation_exposures_barplot" ] }, { "page": "annotation_exposures_list_barplot", "title": "Plot the exposures of a cohort with different layers of annotation for SNV and INDEL signatures", "topics": [ "annotation_exposures_list_barplot" ] }, { "page": "annotation_heatmap_exposures", "title": "Heatmap to cluster the PIDs on their signature exposures (ComplexHeatmap)", "topics": [ "annotation_heatmap_exposures" ] }, { "page": "attribute_nucleotide_exchanges", "title": "Attribute the nucleotide exchange for an SNV", "topics": [ "attribute_nucleotide_exchanges" ] }, { "page": "attribute_sequence_contex_indel", "title": "Attribution of sequence context and size for an INDEL", "topics": [ "attribute_sequence_contex_indel" ] }, { "page": "attribution_of_indels", "title": "Attribution of variant into one onf the 83 INDEL categories", "topics": [ "attribution_of_indels" ] }, { "page": "build_gene_list_for_pathway", "title": "Build a gene list for a given pathway name", "topics": [ "build_gene_list_for_pathway" ] }, { "page": "classify_indels", "title": "INDEL function V1 - not compartible with AlexandrovSignatures", "topics": [ "classify_indels" ] }, { "page": "compare_exposures", "title": "Compares alternative exposures", "topics": [ "compare_exposures" ] }, { "page": "compare_expousre_sets", "title": "Compare two sets of exposures by cosine distance", "topics": [ "compare_expousre_sets" ] }, { "page": "compare_sets", "title": "Compare two sets of signatures by cosine distance", "topics": [ "compare_sets" ] }, { "page": "compare_SMCs", "title": "Compare all strata from different stratifications", "topics": [ "compare_SMCs" ] }, { "page": "compare_to_catalogues", "title": "Compare one mutational catalogue to reference mutational catalogues", "topics": [ "compare_to_catalogues" ] }, { "page": "complex_heatmap_exposures", "title": "Heatmap to cluster the PIDs on their signature exposures (ComplexHeatmap)", "topics": [ "complex_heatmap_exposures" ] }, { "page": "compute_comparison_stat_df", "title": "Extract statistical measures for entity comparison", "topics": [ "compute_comparison_stat_df" ] }, { "page": "computeLogLik", "title": "Compute the loglikelihood", "topics": [ "computeLogLik" ] }, { "page": "confidence_indel_calulation", "title": "Wrapper to compute confidence intervals for SNV and INDEL signatures of a cohort or single-sample", "topics": [ "confidence_indel_calulation" ] }, { "page": "confidence_indel_only_calulation", "title": "Wrapper to compute confidence intervals for only INDEL signatures.", "topics": [ "confidence_indel_only_calulation" ] }, { "page": "confIntExp", "title": "Compute confidence intervals", "topics": [ "confIntExp" ] }, { "page": "correct_rounded", "title": "Readjust the vector to it's original norm after rounding", "topics": [ "correct_rounded" ] }, { "page": "cosineDist", "title": "Compute the cosine distance of two vectors", "topics": [ "cosineDist" ] }, { "page": "cosineMatchDist", "title": "Compute an altered cosine distance of two vectors", "topics": [ "cosineMatchDist" ] }, { "page": "create_indel_mut_cat_from_df", "title": "Create a Mutational catalog from a data frame", "topics": [ "create_indel_mut_cat_from_df" ] }, { "page": "create_indel_mutation_catalogue_from_df", "title": "Wrapper to create an INDEL mutational catalog from a vlf-like data frame", "topics": [ "create_indel_mutation_catalogue_from_df" ] }, { "page": "create_mutation_catalogue_from_df", "title": "Create a Mutational Catalogue from a data frame", "topics": [ "create_mutation_catalogue_from_df" ] }, { "page": "create_mutation_catalogue_from_VR", "title": "Create a Mutational Catalogue from a VRanges Object", "topics": [ "create_mutation_catalogue_from_VR" ] }, { "page": "cut_breaks_as_intervals", "title": "Wrapper for cut", "topics": [ "cut_breaks_as_intervals" ] }, { "page": "cutoffs", "title": "Cutoffs for a supervised analysis of mutational signatures.", "topics": [ "cutoffCosmicArtif_abs_df", "cutoffCosmicArtif_rel_df", "cutoffCosmicValid_abs_df", "cutoffCosmicValid_rel_df", "cutoffInitialArtif_abs_df", "cutoffInitialArtif_rel_df", "cutoffInitialValid_abs_df", "cutoffInitialValid_rel_df", "cutoffs" ] }, { "page": "cutoffs_pcawg", "title": "Opt. cutoffs, PCAWG SNV signatures, including artifacts", "topics": [ "cutoffPCAWG_ID_WGS_Pid_df", "cutoffPCAWG_SBS_WGSWES_artifPid_df", "cutoffPCAWG_SBS_WGSWES_realPid_df", "cutoffs_pcawg" ] }, { "page": "deriveSigInd_df", "title": "Derive a signature_indices_df object", "topics": [ "deriveSigInd_df" ] }, { "page": "disambiguateVector", "title": "Disambiguate a vector", "topics": [ "disambiguateVector" ] }, { "page": "enrichSigs", "title": "Compare to background distribution", "topics": [ "enrichSigs" ] }, { "page": "exampleINDEL_YAPSA", "title": "Data structures used in examples, Indel tests and the Indel signature vignette of the YAPSA package.", "topics": [ "exampleINDEL_YAPSA" ] }, { "page": "exampleYAPSA", "title": "Test and example data", "topics": [ "chosen_AlexInitialArtif_sigInd_df", "chosen_signatures_indices_df", "COSMIC_subgroups_df", "exampleYAPSA", "lymphoma_Nature2013_COSMIC_cutoff_exposures_df", "lymphoma_Nature2013_raw_df", "lymphoma_PID_df", "lymphoma_test_df", "rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df" ] }, { "page": "exchange_colour_vector", "title": "Colours codes for displaying SNVs", "topics": [ "exchange_colour_vector" ] }, { "page": "exome_mutCatRaw_df", "title": "Example mutational catalog for the exome vignette", "topics": [ "exome_mutCatRaw_df" ] }, { "page": "exposures_barplot", "title": "Wrapper for enhanced_barplot", "topics": [ "exposures_barplot" ] }, { "page": "extract_names_from_gene_list", "title": "Return gene names from gene lists", "topics": [ "extract_names_from_gene_list" ] }, { "page": "find_affected_PIDs", "title": "Find samples affected", "topics": [ "find_affected_PIDs" ] }, { "page": "GenomeOfNl_raw", "title": "Example data for the Indel vignette", "topics": [ "GenomeOfNl_raw" ] }, { "page": "get_extreme_PIDs", "title": "Return those PIDs which have an extreme pattern for signature exposure", "topics": [ "get_extreme_PIDs" ] }, { "page": "getSequenceContext", "title": "Extracts the sequence context up and downstream of a nucleotide position", "topics": [ "getSequenceContext" ] }, { "page": "hclust_exposures", "title": "Cluster the PIDs according to their signature exposures", "topics": [ "hclust_exposures" ] }, { "page": "LCD", "title": "Linear Combination Decomposition", "topics": [ "LCD" ] }, { "page": "LCD_complex_cutoff", "title": "LCD with a signature-specific cutoff on exposures", "topics": [ "LCD_complex_cutoff", "LCD_complex_cutoff_combined", "LCD_complex_cutoff_consensus", "LCD_complex_cutoff_perPID", "LCD_extractCohort_callPerPID" ] }, { "page": "LCD_SMC", "title": "CD stratification analysis", "topics": [ "LCD_SMC" ] }, { "page": "logLikelihood", "title": "Compute a loglikelihood ratio test", "topics": [ "logLikelihood" ] }, { "page": "lymphomaNature2013_mutCat_df", "title": "Example mutational catalog for the SNV vignette", "topics": [ "lymphomaNature2013_mutCat_df" ] }, { "page": "make_catalogue_strata_df", "title": "Group strata from different stratification axes", "topics": [ "make_catalogue_strata_df" ] }, { "page": "make_comparison_matrix", "title": "Compute a similarity matrix for different strata", "topics": [ "make_comparison_matrix" ] }, { "page": "make_strata_df", "title": "Group strata from different stratification axes", "topics": [ "make_strata_df" ] }, { "page": "make_subgroups_df", "title": "Make a custom data structure for subgroups", "topics": [ "make_subgroups_df" ] }, { "page": "makeVRangesFromDataFrame", "title": "Construct a VRanges Object from a data frame", "topics": [ "makeVRangesFromDataFrame" ] }, { "page": "melt_exposures", "title": "Generically melts exposure data frames", "topics": [ "melt_exposures" ] }, { "page": "merge_exposures", "title": "Merge exposure data frames", "topics": [ "merge_exposures" ] }, { "page": "MutCat_indel_df", "title": "Example mutational catalog for the Indel vignette", "topics": [ "MutCat_indel_df" ] }, { "page": "normalize_df_per_dim", "title": "Useful functions on data frames", "topics": [ "average_over_present", "normalize_df_per_dim", "sd_over_present", "stderrmean_over_present" ] }, { "page": "normalizeMotifs_otherRownames", "title": "Normalize Somatic Motifs with different rownames", "topics": [ "normalizeMotifs_otherRownames" ] }, { "page": "plot_exposures", "title": "Plot the exposures of a cohort", "topics": [ "plot_exposures", "plot_relative_exposures" ] }, { "page": "plot_SMC", "title": "Plot results of the Stratification of a Mutational Catalogue", "topics": [ "plot_SMC" ] }, { "page": "plot_strata", "title": "Plot all strata from different stratification axes together", "topics": [ "plot_strata" ] }, { "page": "plotExchangeSpectra", "title": "Plot the spectra of nucleotide exchanges", "topics": [ "plotExchangeSpectra" ] }, { "page": "plotExchangeSpectra_indel", "title": "Plot the spectra of nucleotide exchanges of INDELs", "topics": [ "plotExchangeSpectra_indel" ] }, { "page": "plotExposuresConfidence", "title": "Plot exposures including confidence intervals", "topics": [ "plotExposuresConfidence" ] }, { "page": "plotExposuresConfidence_indel", "title": "Plot exposures including confidence intervals for exposures of SNVs and INDELs", "topics": [ "plotExposuresConfidence_indel" ] }, { "page": "read_entry", "title": "Read a single vcf-like file into a single data frame", "topics": [ "read_entry", "read_list" ] }, { "page": "relateSigs", "title": "Make unique assignments between sets of signatures", "topics": [ "relateSigs" ] }, { "page": "repeat_df", "title": "Create a data frame with default values", "topics": [ "repeat_df" ] }, { "page": "round_precision", "title": "Round to a defined precision", "topics": [ "round_precision" ] }, { "page": "run_annotate_vcf_pl", "title": "Wrapper function to annotate addition information", "topics": [ "run_annotate_vcf_pl" ] }, { "page": "run_comparison_catalogues", "title": "Compare all strata from different stratifications", "topics": [ "run_comparison_catalogues" ] }, { "page": "run_comparison_general", "title": "Compare all strata from different stratifications", "topics": [ "run_comparison_general" ] }, { "page": "run_kmer_frequency_correction", "title": "Provide comprehensive correction factors for kmer content", "topics": [ "run_kmer_frequency_correction" ] }, { "page": "run_kmer_frequency_normalization", "title": "Provide normalized correction factors for kmer content", "topics": [ "run_kmer_frequency_normalization" ] }, { "page": "run_plot_strata_general", "title": "Wrapper function for 'plot_strata'", "topics": [ "run_plot_strata_general" ] }, { "page": "run_SMC", "title": "Wrapper function for the Stratification of a Mutational Catalogue", "topics": [ "run_SMC" ] }, { "page": "shapiro_if_possible", "title": "Wrapper for Shapiro test but allow for all identical values", "topics": [ "shapiro_if_possible" ] }, { "page": "sigs", "title": "Data for mutational signatures", "topics": [ "AlexCosmicArtif_sigInd_df", "AlexCosmicArtif_sig_df", "AlexCosmicValid_sigInd_df", "AlexCosmicValid_sig_df", "AlexInitialArtif_sigInd_df", "AlexInitialArtif_sig_df", "AlexInitialValid_sigInd_df", "AlexInitialValid_sig_df", "sigs" ] }, { "page": "sigs_pcawg", "title": "Data for PCAWG SNV signatures (COSMIC v3), including artifacts 'PCAWG_SP_SBS_sigs_Artif_df': Data frame of the signatures published by Alexandrov et al. (Biorxiv 2013) which were decomposed with the method SigProfiler. SNV signatures are labeled with SBS, single base signature. There are 67 signatures which constitute the columns, 47 of which were validated by a bayesian NFM mehtod, SignatureAnayzer. Validated signatures are SBS1-SBS26,SBS28-SBS42 and SBS44. SBS7 is split up into 7 a/b/c and d. SBS10 ans SBS17 are both split up into a and b. Resulting in a 47 validated sigantures. Please note, unlike the paper by Alexandrov et al. (Biorxiv 2018) the data sets do not contain a SBS84 and SBS85 as not all were availiablt to perfom supervised signature analysis. In total there are 96 different features and therefore 96 rows when dealing with a trinucleotide context.", "topics": [ "PCAWG_SP_ID_sigInd_df", "PCAWG_SP_ID_sigs_df", "PCAWG_SP_SBS_sigInd_Artif_df", "PCAWG_SP_SBS_sigInd_Real_df", "PCAWG_SP_SBS_sigs_Artif_df", "PCAWG_SP_SBS_sigs_Real_df", "sigs_pcawg" ] }, { "page": "SMC", "title": "Stratification of a Mutational Catalogue", "topics": [ "SMC" ] }, { "page": "SMC_perPID", "title": "Run SMC at a per sample level", "topics": [ "SMC_perPID" ] }, { "page": "split_exposures_by_subgroups", "title": "Split an exposures data frame by subgroups", "topics": [ "split_exposures_by_subgroups" ] }, { "page": "stat_plot_subgroups", "title": "Plot averaged signature exposures per subgroup", "topics": [ "stat_plot_subgroups" ] }, { "page": "stat_test_SMC", "title": "Apply statistical tests to a stratification (SMC)", "topics": [ "stat_test_SMC" ] }, { "page": "stat_test_subgroups", "title": "Test for differences in average signature exposures between subgroups", "topics": [ "stat_test_subgroups" ] }, { "page": "stderrmean", "title": "Compute the standard error of the mean", "topics": [ "stderrmean" ] }, { "page": "sum_over_list_of_df", "title": "Elementwise sum over a list of (numerical) data frames", "topics": [ "sum_over_list_of_df" ] }, { "page": "targetCapture_cor_factors", "title": "Correction factors for different target capture kits", "topics": [ "targetCapture_cor_factors" ] }, { "page": "test_exposureAffected", "title": "Test significance of association", "topics": [ "test_exposureAffected" ] }, { "page": "test_gene_list_in_exposures", "title": "Test if mutated PIDs are enriched in signatures", "topics": [ "test_gene_list_in_exposures" ] }, { "page": "testSigs", "title": "Test for significance of alternative models cohort wide", "topics": [ "testSigs" ] }, { "page": "transform_rownames_R_to_MATLAB", "title": "Change rownames from one naming convention to another", "topics": [ "transform_rownames_deconstructSigs_to_YAPSA", "transform_rownames_MATLAB_to_R", "transform_rownames_nature_to_R", "transform_rownames_R_to_MATLAB", "transform_rownames_YAPSA_to_deconstructSigs" ] }, { "page": "translate_to_hg19", "title": "Translate chromosome names to the hg19 naming convention", "topics": [ "translate_to_1kG", "translate_to_hg19" ] }, { "page": "trellis_rainfall_plot", "title": "Create a rainfall plot in a trellis structure", "topics": [ "trellis_rainfall_plot" ] }, { "page": "variateExp", "title": "Wrapper to compute confidence intervals for a cohort", "topics": [ "variateExp" ] }, { "page": "variateExpSingle", "title": "Wrapper for the likelihood ratio test", "topics": [ "variateExpSingle" ] }, { "page": "YAPSA", "title": "Generate R documentation from inline comments.", "topics": [ "YAPSA" ] } ], "_readme": "https://github.com/bioc/YAPSA/raw/HEAD/README.md", "_rundeps": [ "abind", "AnnotationDbi", "AnnotationFilter", "askpass", "backports", "base64enc", "beeswarm", "BH", "Biobase", "BiocBaseUtils", "BiocGenerics", "BiocIO", "BiocManager", "BiocParallel", "Biostrings", "biovizBase", "bit", "bit64", "bitops", "blob", "BSgenome", "BSgenome.Hsapiens.UCSC.hg19", "bslib", "BWStest", "cachem", "checkmate", "cigarillo", "circlize", "cli", "clue", "cluster", "codetools", "colorspace", "ComplexHeatmap", "corrplot", "cpp11", "crayon", "curl", "data.table", "DBI", "DelayedArray", "dendextend", "dichromat", "digest", "doParallel", "dplyr", "ensembldb", "evaluate", "farver", "fastmap", "fontawesome", "foreach", "foreign", "formatR", "Formula", "fs", "futile.logger", "futile.options", "generics", "GenomeInfoDb", "GenomicAlignments", "GenomicFeatures", "GenomicRanges", "GetoptLong", "ggbeeswarm", "ggbio", "ggplot2", "GlobalOptions", "glue", "gmp", "graph", "gridBase", "gridExtra", "gtable", "gtrellis", "highr", "Hmisc", "htmlTable", "htmltools", "htmlwidgets", "httr", "IRanges", "isoband", "iterators", "jquerylib", "jsonlite", "KEGGREST", "knitr", "kSamples", "labeling", "lambda.r", "lattice", "lazyeval", "lifecycle", "limSolve", "lpSolve", "magrittr", "MASS", "Matrix", "MatrixGenerics", "matrixStats", "memoise", "mime", "multcompView", "mvtnorm", "NMF", "nnet", "openssl", "OrganismDbi", "pcaMethods", "pillar", "pkgconfig", "plyr", "PMCMRplus", "png", "pracma", "ProtGenerics", "proxy", "quadprog", "R6", "rappdirs", "RBGL", "RColorBrewer", "Rcpp", "RCurl", "registry", "reshape2", "restfulr", "Rhtslib", "rjson", "rlang", "rmarkdown", "Rmpfr", "rngtools", "rpart", "Rsamtools", "RSQLite", "rstudioapi", "rtracklayer", "S4Arrays", "S4Vectors", "S7", "sass", "scales", "Seqinfo", "shape", "snow", "SomaticSignatures", "SparseArray", "stringi", "stringr", "SummarizedExperiment", "SuppDists", "sys", "tibble", "tidyselect", "tinytex", "UCSC.utils", "utf8", "VariantAnnotation", "vctrs", "vipor", "viridis", "viridisLite", "withr", "xfun", "XML", "XVector", "yaml" ], "_vignettes": [ { "source": "YAPSA.Rmd", "filename": "YAPSA.html", "title": "Usage of YAPSA ", "author": "Daniel Huebschmann", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "The YAPSA package", "Linear Combination Decomposition (LCD)", "Signature-specific cutoffs", "Confidence intervals for mutational signature exposures", "Stratification of the Mutational Catalogue (SMC)", "Indel signatures", "Example: a cohort of B-cell lymphomas", "Example data", "Loading example data", "Displaying example data", "Loading the signature information", "Loading the initial set of signatures", "Loading the updated set of mutational signatures", "Preparation for later analysis", "Performing an LCD analysis", "Building a mutational catalogue", "LCD analysis without any cutoff", "LCD analysis with a generalized cutoff", "LCD analysis with signature-specific cutoffs", "Cluster samples based on their signature exposures", "References" ], "created": "2016-08-26 19:47:20", "modified": "2024-03-29 09:41:22", "commits": 12 }, { "source": "vignette_signature_specific_cutoffs.Rmd", "filename": "vignette_signature_specific_cutoffs.html", "title": "Signature-specific cutoffs ", "author": "Lea Jopp-Saile and Daniel Hübschmann", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Overview of the data", "Signatures", "Optimal cutoffs", "Application example", "Comparison: analysis without any cutoffs", "Making use of optimal signature-specific cutoffs", "Reference" ], "created": "2019-11-28 19:09:02", "modified": "2023-09-16 06:12:20", "commits": 5 }, { "source": "vignette_confidenceIntervals.Rmd", "filename": "vignette_confidenceIntervals.html", "title": "Confidence Intervals ", "author": "Daniel Huebschmann", "engine": "knitr::rmarkdown", "headings": [ "Introduction: Confidence Intervals", "Recap: compute signature exposures", "Compute the confidence intervals", "References" ], "created": "2020-01-01 14:51:55", "modified": "2023-09-16 06:12:20", "commits": 4 }, { "source": "vignette_stratifiedAnalysis.Rmd", "filename": "vignette_stratifiedAnalysis.html", "title": "Stratified Analysis of Mutational Signatures ", "author": "Daniel Huebschmann", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Recap: compute signature exposures (without stratification)", "Stratified analysis of mutational signatures", "Preparation", "Performing a stratification based on mutation density", "Testing for statistical significance of enrichment and depletion patterns" ], "created": "2020-01-01 14:51:55", "modified": "2023-09-16 06:12:20", "commits": 5 }, { "source": "vignettes_Indel.Rmd", "filename": "vignettes_Indel.html", "title": "Indel signature analysis ", "author": "Lea Jopp-Saile and Daniel Huebschmann", "engine": "knitr::rmarkdown", "headings": [ "Introduction: PCAWG signatures", "Classification of Indels", "PCAWG Indel signatures", "Example data: Genome of the Netherlands", "Supervised Indel signature analysis", "Loading example data", "Data Preprocessing", "LCD Analysis", "Visualization", "Confidence Intervals", "References" ], "created": "2019-11-26 17:36:11", "modified": "2023-09-16 06:12:20", "commits": 7 }, { "source": "vignette_exomes.Rmd", "filename": "vignette_exomes.html", "title": "Usage of YAPSA for Whole Exome Sequencing (WES) data ", "author": "Daniel Huebschmann", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Loading the data", "Correcting for the target capture", "Performing the analysis of mutational signatures", "References" ], "created": "2020-03-17 15:38:15", "modified": "2023-09-16 06:12:20", "commits": 3 } ], "_score": 6.6414741105041, "_indexed": true, "_nocasepkg": "yapsa", "_universes": [ "bioc", "huebschm" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.39.0", "date": "2026-05-09T09:06:46.000Z", "distro": "noble", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "9edfa23799eae915d27df7d31c2bcc40b1fc5c6987739a4b99acce32626516bb", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" }, { "r": "4.6.0", "os": "linux", "version": "1.39.0", "date": "2026-05-09T09:06:37.000Z", "distro": "noble", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "b3e6794d4ee78eb5df65f62ab5c85b4545c9ded12d17b1be59a5820237e43083", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" }, { "r": "4.5.3", "os": "mac", "version": "1.39.0", "date": "2026-05-09T09:04:01.000Z", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "f1e5f8204619ae84d845896cc990918d415b660bc8d239b295b9df6cfed7859d", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" }, { "r": "4.6.0", "os": "mac", "version": "1.39.0", "date": "2026-05-09T09:04:43.000Z", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "6f7b31986bb5ed57c83b8f3896444c041ed20617a73c841f4940d63f05e5809e", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" }, { "r": "4.7.0", "os": "win", "version": "1.39.0", "date": "2026-05-09T09:04:24.000Z", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "5f312f252a966c67c54ccac1750cea4c1093fa010941c4350e45996d3ebf77a3", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" }, { "r": "4.5.3", "os": "win", "version": "1.39.0", "date": "2026-05-09T09:04:33.000Z", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "b29b6a3f1be042fe3bfecc8a3c9343a793c5f074cbc5734663f308183626afd6", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" }, { "r": "4.6.0", "os": "win", "version": "1.39.0", "date": "2026-05-09T09:04:24.000Z", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "4cccdbee9464b0b688026ba5b2fc73423ea24226699c828efc32ecfdd8ae9127", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" }, { "r": "4.6.0", "os": "wasm", "version": "1.39.0", "date": "2026-05-22T13:00:07.000Z", "commit": "fdcc08cc2cc025200ebd108e1483f1490f9cc348", "fileid": "a63f41bf52b9a2a0ca442a05e2e90700231369cda566042fd321bbc424cdee61", "status": "success", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25596988701" } ] }