Package: YAPSA 1.31.0

Zuguang Gu

YAPSA: Yet Another Package for Signature Analysis

This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata.

Authors:Daniel Huebschmann [aut], Lea Jopp-Saile [aut], Carolin Andresen [aut], Zuguang Gu [aut, cre], Matthias Schlesner [aut]

YAPSA_1.31.0.tar.gz
YAPSA_1.31.0.zip(r-4.5)YAPSA_1.31.0.zip(r-4.4)YAPSA_1.31.0.zip(r-4.3)
YAPSA_1.31.0.tgz(r-4.4-any)YAPSA_1.31.0.tgz(r-4.3-any)
YAPSA_1.31.0.tar.gz(r-4.5-noble)YAPSA_1.31.0.tar.gz(r-4.4-noble)
YAPSA_1.31.0.tgz(r-4.4-emscripten)YAPSA_1.31.0.tgz(r-4.3-emscripten)
YAPSA.pdf |YAPSA.html✨
YAPSA/json (API)
NEWS

# Install 'YAPSA' in R:
install.packages('YAPSA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • AlexCosmicArtif_sigInd_df - Data for mutational signatures
  • AlexCosmicArtif_sig_df - Data for mutational signatures
  • AlexCosmicValid_sigInd_df - Data for mutational signatures
  • AlexCosmicValid_sig_df - Data for mutational signatures
  • AlexCosmicValid_sig_df - Data for mutational signatures
  • AlexInitialArtif_sigInd_df - Data for mutational signatures
  • AlexInitialArtif_sig_df - Data for mutational signatures
  • AlexInitialValid_sigInd_df - Data for mutational signatures
  • AlexInitialValid_sig_df - Data for mutational signatures
  • COSMIC_subgroups_df - Test and example data
  • GenomeOfNl_raw - Example data for the Indel vignette
  • MutCat_indel_df - Example mutational catalog for the Indel vignette
  • PCAWG_SP_ID_sigInd_df - 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.
  • PCAWG_SP_ID_sigs_df - 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.
  • PCAWG_SP_SBS_sigInd_Artif_df - 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.
  • PCAWG_SP_SBS_sigInd_Real_df - 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.
  • PCAWG_SP_SBS_sigs_Artif_df - 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.
  • PCAWG_SP_SBS_sigs_Real_df - 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.
  • chosen_signatures_indices_df - Test and example data
  • cutoffCosmicArtif_abs_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffCosmicArtif_rel_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffCosmicValid_abs_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffCosmicValid_rel_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffInitialArtif_abs_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffInitialArtif_rel_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffInitialValid_abs_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffInitialValid_rel_df - Cutoffs for a supervised analysis of mutational signatures.
  • cutoffPCAWG_ID_WGS_Pid_df - Opt. cutoffs, PCAWG SNV signatures, including artifacts
  • cutoffPCAWG_SBS_WGSWES_artifPid_df - Opt. cutoffs, PCAWG SNV signatures, including artifacts
  • cutoffPCAWG_SBS_WGSWES_realPid_df - Opt. cutoffs, PCAWG SNV signatures, including artifacts
  • exchange_colour_vector - Colours codes for displaying SNVs
  • exome_mutCatRaw_df - Example mutational catalog for the exome vignette
  • lymphomaNature2013_mutCat_df - Example mutational catalog for the SNV vignette
  • lymphoma_Nature2013_COSMIC_cutoff_exposures_df - Test and example data
  • lymphoma_Nature2013_raw_df - Test and example data
  • lymphoma_PID_df - Test and example data
  • lymphoma_test_df - Test and example data
  • rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df - Test and example data
  • targetCapture_cor_factors - Correction factors for different target capture kits

On BioConductor:YAPSA-1.31.0(bioc 3.20)YAPSA-1.30.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

102 exports 1.38 score 196 dependencies 5 mentions

Last updated 2 months agofrom:02bb798fe5

Exports:add_annotationadd_as_fist_to_listaggregate_exposures_by_categoryannotate_intermut_dist_cohortannotate_intermut_dist_PIDannotation_exposures_barplotannotation_exposures_list_barplotannotation_heatmap_exposuresattribute_nucleotide_exchangesattribute_sequence_contex_indelattribution_of_indelsaverage_over_presentbuild_gene_list_for_pathwayclassify_indelscompare_exposurescompare_expousre_setscompare_setscompare_SMCscompare_to_cataloguescomplex_heatmap_exposurescompute_comparison_stat_dfcomputeLogLikconfidence_indel_calulationconfidence_indel_only_calulationconfIntExpcorrect_roundedcosineDistcosineMatchDistcreate_indel_mut_cat_from_dfcreate_indel_mutation_catalogue_from_dfcreate_mutation_catalogue_from_dfcreate_mutation_catalogue_from_VRcut_breaks_as_intervalsderiveSigInd_dfdisambiguateVectorenrichSigsexposures_barplotextract_names_from_gene_listfind_affected_PIDsget_extreme_PIDsgetSequenceContexthclust_exposuresLCDLCD_complex_cutoffLCD_complex_cutoff_combinedLCD_complex_cutoff_consensusLCD_complex_cutoff_perPIDLCD_extractCohort_callPerPIDlogLikelihoodmake_catalogue_strata_dfmake_comparison_matrixmake_strata_dfmake_subgroups_dfmakeVRangesFromDataFramemelt_exposuresmerge_exposuresnormalize_df_per_dimnormalizeMotifs_otherRownamesplot_exposuresplot_relative_exposuresplot_SMCplot_strataplotExchangeSpectraplotExchangeSpectra_indelplotExposuresConfidenceplotExposuresConfidence_indelread_entryread_listrelateSigsrepeat_dfround_precisionrun_annotate_vcf_plrun_comparison_cataloguesrun_comparison_generalrun_kmer_frequency_correctionrun_kmer_frequency_normalizationrun_plot_strata_generalrun_SMCsd_over_presentshapiro_if_possibleSMCSMC_perPIDsplit_exposures_by_subgroupsstat_plot_subgroupsstat_test_SMCstat_test_subgroupsstderrmeanstderrmean_over_presentsum_over_list_of_dftest_exposureAffectedtest_gene_list_in_exposurestestSigstransform_rownames_deconstructSigs_to_YAPSAtransform_rownames_MATLAB_to_Rtransform_rownames_nature_to_Rtransform_rownames_R_to_MATLABtransform_rownames_YAPSA_to_deconstructSigstranslate_to_1kGtranslate_to_hg19trellis_rainfall_plotvariateExpvariateExpSingle

Dependencies:abindAnnotationDbiAnnotationFilteraskpassbackportsbase64encbeeswarmBHBiobaseBiocFileCacheBiocGenericsBiocIOBiocManagerBiocParallelbiomaRtBiostringsbiovizBasebitbit64bitopsblobbroombroom.helpersBSgenomeBSgenome.Hsapiens.UCSC.hg19bslibBWStestcachemcheckmatecirclizeclicliprclueclustercodetoolscolorspaceComplexHeatmapcorrplotcpp11crayoncurldata.tableDBIdbplyrDelayedArraydendextenddichromatdigestdoParalleldplyrensembldbevaluatefansifarverfastmapfilelockfontawesomeforcatsforeachforeignformatRFormulafsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicAlignmentsGenomicFeaturesGenomicRangesGetoptLongGGallyggbeeswarmggbioggplot2ggstatsGlobalOptionsgluegmpgraphgridBasegridExtragtablegtrellishavenhighrHmischmshtmlTablehtmltoolshtmlwidgetshttrhttr2IRangesisobanditeratorsjquerylibjsonliteKEGGRESTknitrkSampleslabelinglabelledlambda.rlatticelazyevallifecyclelimSolvelpSolvemagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemultcompViewmunsellmvtnormnlmeNMFnnetopensslOrganismDbipatchworkpcaMethodspillarpkgconfigplogrplyrPMCMRpluspngpracmaprettyunitsprogressProtGenericsproxypurrrquadprogR6rappdirsRBGLRColorBrewerRcppRCurlreadrregistryreshape2restfulrRhtslibrjsonrlangrmarkdownRmpfrrngtoolsrpartRsamtoolsRSQLiterstudioapirtracklayerS4ArraysS4VectorssassscalesshapesnowSomaticSignaturesSparseArraystringistringrSummarizedExperimentSuppDistssystibbletidyrtidyselecttinytextxdbmakertzdbUCSC.utilsutf8VariantAnnotationvctrsviporviridisviridisLitevroomwithrxfunXMLxml2XVectoryamlzlibbioc

Usage of YAPSA

Daniel Huebschmann

Rendered fromYAPSA.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2024-03-29
Started: 2016-08-26

Signature-specific cutoffs

Lea Jopp-Saile and Daniel Hübschmann

Rendered fromvignette_signature_specific_cutoffs.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2023-09-16
Started: 2019-11-28

Confidence Intervals

Daniel Huebschmann

Rendered fromvignette_confidenceIntervals.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2023-09-16
Started: 2020-01-01

Stratified Analysis of Mutational Signatures

Daniel Huebschmann

Rendered fromvignette_stratifiedAnalysis.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2023-09-16
Started: 2020-01-01

Indel signature analysis

Lea Jopp-Saile and Daniel Huebschmann

Rendered fromvignettes_Indel.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2023-09-16
Started: 2019-11-26

Usage of YAPSA for Whole Exome Sequencing (WES) data

Daniel Huebschmann

Rendered fromvignette_exomes.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2023-09-16
Started: 2020-03-17

Readme and manuals

Help Manual

Help pageTopics
Add information to an annotation data structureadd_annotation
Add an element as first entry to a listadd_as_fist_to_list
Aggregate exposures by categoryaggregate_exposures_by_category
Annotate the intermutation distance of variants cohort-wideannotate_intermut_dist_cohort
Annotate the intermutation distance of variants per PIDannotate_intermut_dist_PID
Plot the exposures of a cohort with different layers of annotationannotation_exposures_barplot
Plot the exposures of a cohort with different layers of annotation for SNV and INDEL signaturesannotation_exposures_list_barplot
Heatmap to cluster the PIDs on their signature exposures (ComplexHeatmap)annotation_heatmap_exposures
Attribute the nucleotide exchange for an SNVattribute_nucleotide_exchanges
Attribution of sequence context and size for an INDELattribute_sequence_contex_indel
Attribution of variant into one onf the 83 INDEL categoriesattribution_of_indels
Build a gene list for a given pathway namebuild_gene_list_for_pathway
INDEL function V1 - not compartible with AlexandrovSignaturesclassify_indels
Compares alternative exposurescompare_exposures
Compare two sets of exposures by cosine distancecompare_expousre_sets
Compare two sets of signatures by cosine distancecompare_sets
Compare all strata from different stratificationscompare_SMCs
Compare one mutational catalogue to reference mutational cataloguescompare_to_catalogues
Heatmap to cluster the PIDs on their signature exposures (ComplexHeatmap)complex_heatmap_exposures
Extract statistical measures for entity comparisoncompute_comparison_stat_df
Compute the loglikelihoodcomputeLogLik
Wrapper to compute confidence intervals for SNV and INDEL signatures of a cohort or single-sampleconfidence_indel_calulation
Wrapper to compute confidence intervals for only INDEL signatures.confidence_indel_only_calulation
Compute confidence intervalsconfIntExp
Readjust the vector to it's original norm after roundingcorrect_rounded
Compute the cosine distance of two vectorscosineDist
Compute an altered cosine distance of two vectorscosineMatchDist
Create a Mutational catalog from a data framecreate_indel_mut_cat_from_df
Wrapper to create an INDEL mutational catalog from a vlf-like data framecreate_indel_mutation_catalogue_from_df
Create a Mutational Catalogue from a data framecreate_mutation_catalogue_from_df
Create a Mutational Catalogue from a VRanges Objectcreate_mutation_catalogue_from_VR
Wrapper for cutcut_breaks_as_intervals
Cutoffs for a supervised analysis of mutational signatures.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
Opt. cutoffs, PCAWG SNV signatures, including artifactscutoffPCAWG_ID_WGS_Pid_df cutoffPCAWG_SBS_WGSWES_artifPid_df cutoffPCAWG_SBS_WGSWES_realPid_df cutoffs_pcawg
Derive a signature_indices_df objectderiveSigInd_df
Disambiguate a vectordisambiguateVector
Compare to background distributionenrichSigs
Data structures used in examples, Indel tests and the Indel signature vignette of the YAPSA package.exampleINDEL_YAPSA
Test and example datachosen_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
Colours codes for displaying SNVsexchange_colour_vector
Example mutational catalog for the exome vignetteexome_mutCatRaw_df
Wrapper for enhanced_barplotexposures_barplot
Return gene names from gene listsextract_names_from_gene_list
Find samples affectedfind_affected_PIDs
Example data for the Indel vignetteGenomeOfNl_raw
Return those PIDs which have an extreme pattern for signature exposureget_extreme_PIDs
Extracts the sequence context up and downstream of a nucleotide positiongetSequenceContext
Cluster the PIDs according to their signature exposureshclust_exposures
Linear Combination DecompositionLCD
LCD with a signature-specific cutoff on exposuresLCD_complex_cutoff LCD_complex_cutoff_combined LCD_complex_cutoff_consensus LCD_complex_cutoff_perPID LCD_extractCohort_callPerPID
CD stratification analysisLCD_SMC
Compute a loglikelihood ratio testlogLikelihood
Example mutational catalog for the SNV vignettelymphomaNature2013_mutCat_df
Group strata from different stratification axesmake_catalogue_strata_df
Compute a similarity matrix for different stratamake_comparison_matrix
Group strata from different stratification axesmake_strata_df
Make a custom data structure for subgroupsmake_subgroups_df
Construct a VRanges Object from a data framemakeVRangesFromDataFrame
Generically melts exposure data framesmelt_exposures
Merge exposure data framesmerge_exposures
Example mutational catalog for the Indel vignetteMutCat_indel_df
Useful functions on data framesaverage_over_present normalize_df_per_dim sd_over_present stderrmean_over_present
Normalize Somatic Motifs with different rownamesnormalizeMotifs_otherRownames
Plot the exposures of a cohortplot_exposures plot_relative_exposures
Plot results of the Stratification of a Mutational Catalogueplot_SMC
Plot all strata from different stratification axes togetherplot_strata
Plot the spectra of nucleotide exchangesplotExchangeSpectra
Plot the spectra of nucleotide exchanges of INDELsplotExchangeSpectra_indel
Plot exposures including confidence intervalsplotExposuresConfidence
Plot exposures including confidence intervals for exposures of SNVs and INDELsplotExposuresConfidence_indel
Read a single vcf-like file into a single data frameread_entry read_list
Make unique assignments between sets of signaturesrelateSigs
Create a data frame with default valuesrepeat_df
Round to a defined precisionround_precision
Wrapper function to annotate addition informationrun_annotate_vcf_pl
Compare all strata from different stratificationsrun_comparison_catalogues
Compare all strata from different stratificationsrun_comparison_general
Provide comprehensive correction factors for kmer contentrun_kmer_frequency_correction
Provide normalized correction factors for kmer contentrun_kmer_frequency_normalization
Wrapper function for 'plot_strata'run_plot_strata_general
Wrapper function for the Stratification of a Mutational Cataloguerun_SMC
Wrapper for Shapiro test but allow for all identical valuesshapiro_if_possible
Data for mutational signaturesAlexCosmicArtif_sigInd_df AlexCosmicArtif_sig_df AlexCosmicValid_sigInd_df AlexCosmicValid_sig_df AlexInitialArtif_sigInd_df AlexInitialArtif_sig_df AlexInitialValid_sigInd_df AlexInitialValid_sig_df sigs
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.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
Stratification of a Mutational CatalogueSMC
Run SMC at a per sample levelSMC_perPID
Split an exposures data frame by subgroupssplit_exposures_by_subgroups
Plot averaged signature exposures per subgroupstat_plot_subgroups
Apply statistical tests to a stratification (SMC)stat_test_SMC
Test for differences in average signature exposures between subgroupsstat_test_subgroups
Compute the standard error of the meanstderrmean
Elementwise sum over a list of (numerical) data framessum_over_list_of_df
Correction factors for different target capture kitstargetCapture_cor_factors
Test significance of associationtest_exposureAffected
Test if mutated PIDs are enriched in signaturestest_gene_list_in_exposures
Test for significance of alternative models cohort widetestSigs
Change rownames from one naming convention to anothertransform_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 chromosome names to the hg19 naming conventiontranslate_to_1kG translate_to_hg19
Create a rainfall plot in a trellis structuretrellis_rainfall_plot
Wrapper to compute confidence intervals for a cohortvariateExp
Wrapper for the likelihood ratio testvariateExpSingle
Generate R documentation from inline comments.YAPSA