Nothing
R/data.R
In YAPSA: Yet Another Package for Signature Analysis
# Copyright © 2014-2019 The YAPSA package contributors
# This file is part of the YAPSA package. The YAPSA package is licenced under
# GPL-3
#' Data for mutational signatures
#'
#' The numerical data of the mutational signatures published initially by
#' Alexandrov et al. (Nature 2013) and Alexandrov et al., (Bioaxiv 2018) is
#' stored in data frames with endings \code{_sig_df}, the associated
#' meta-information is stored in data frames with endings \code{_sigInd_df}.
#' There are several instances of \code{_sig_df} and \code{_sigInd_df},
#' corresponding to results and data obtained at different times and with
#' different raw data. There always is a one-to-one correspondence between
#' a \code{_sig_df} and a \code{_sigInd_df}. The data frames of type
#' \code{_sig_df} have as many rows as there are features, i.e. 96 if
#' analyzing mutational signatures of SNVs in a triplet context, and as
#' many columns as there are signatures.
#' Data frames of type \code{_sigInd_df} have as many rows as there are
#' signatures in the corresponding \code{_sig_df} and several columns:
#' \itemize{
#' \item \code{sig}: signature name
#' \item \code{index}: corresponding to the row index of the signature
#' \item \code{colour}: colour for visualization in stacked barplots
#' \item \code{process}: asserted biological process
#' \item \code{cat.coarse}: categorization of the signatures according
#' to the asserted biological processes at low level of detail
#' \item \code{cat.medium}: categorization of the signatures according
#' to the asserted biological processes at intermediate level of detail
#' \item \code{cat.high}: categorization of the signatures according
#' to the asserted biological processes at high level of detail
#' \item \code{cat.putative}: categorization of the signatures according
#' to the asserted biological processes based on clustering and inference
#' }
#' Please note, that categorization columns are only present for the data
#' frames corrosponding to the data from Alexandorv et al. (Nature 2013).
#'
#' @docType data
#' @name sigs
#' @usage data(sigs)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @references Alexandrov et al. (Nature 2013)
#'
NULL
#' @docType data
#' @name sigs_pcawg
#' @usage data(sigs_pcawg)
#' @author Lea Jopp-Saile \email{huebschmann.daniel@@googlemail.com}
#' @references Alexandrov et al. (Biorxiv 2018)
#'
NULL
#' Data for initial sigs, including artifacts
#'
#' \code{AlexInitialArtif_sig_df}: Data frame of the signatures published
#' initially by Alexandrov et al.
#' (Nature 2013). There are 27 signatures which constitute the columns, 22 of
#' which were validated by an orhtogonal sequencing technology. These 22 are in
#' the first 22 columns of the data frame. The column names are \emph{A} pasted
#' to the number of the signature, e.g. \emph{A5}. The nonvalidated signatures
#' have an additional letter in their naming convention: either
#' \emph{AR1} - \emph{AR3} or \emph{AU1} - \emph{AU2}. The rownames are the
#' features, i.e. an encoding of the nucleotide exchanges in their
#' trinucleotide context, e.g. \emph{C>A ACA}. In total there are 96 different
#' features and therefore 96 rows when dealing with a trinucleotide context.
#'
#' @source \code{AlexInitial}: \url{ftp://ftp.sanger.ac.uk/pub/cancer/
#' AlexandrovEtAl/signatures.txt}
#' @name AlexInitialArtif_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for initial sigs, including artifacts
#'
#' \code{AlexInitialArtif_sigInd_df}: Meta-information for
#' \code{AlexInitialArtif_sig_df}
#'
#' @name AlexInitialArtif_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for initial sigs, only validated
#'
#' \code{AlexInitialValid_sig_df}: Data frame of only the validated signatures
#' published initially by Alexandrov et al. (Nature 2013), corresponding to the
#' first 22 columns of \code{AlexInitialArtif_sig_df}
#'
#' @name AlexInitialValid_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for initial sigs, only validated
#'
#' \code{AlexInitialValid_sigInd_df}: Meta-information for
#' \code{AlexInitialValid_sig_df}
#'
#' @name AlexInitialValid_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for Cosmic sigs, only validated
#'
#' \code{AlexCosmicValid_sig_df}: Data frame of the updated signatures list
#' maintained by Ludmil Alexandrov at
#' \url{http://cancer.sanger.ac.uk/cosmic/signatures}. The column names are
#' \emph{AC} pasted to the number of the signature, e.g. \emph{AC5}. The naming
#' convention for the rows is as described for
#' \code{\link{AlexInitialArtif_sig_df}}.
#'
#' @source \code{AlexCosmic}: \url{http://cancer.sanger.ac.uk/cancergenome/
#' assets/signatures_probabilities.txt}
#' @name AlexCosmicValid_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for Cosmic sigs, only validated
#'
#' \code{AlexCosmicValid_sigInd_df}: Meta-information for
#' \code{AlexCosmicValid_sig_df}
#'
#' @name AlexCosmicValid_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for Cosmic sigs, including artifacts
#'
#' \code{AlexCosmicArtif_sig_df}: Data frame of the updated signatures list
#' maintained by Ludmil Alexandrov at
#' \url{http://cancer.sanger.ac.uk/cosmic/signatures} and complemented by the
#' artifact signatures from the initial publication, i.e. the last 5 columns of
#' \code{\link{AlexInitialArtif_sig_df}}. The column names are \emph{AC} pasted
#' to the number of the signature, e.g. \emph{AC5}. The naming convention for
#' the rows is as described for \code{\link{AlexInitialArtif_sig_df}}.
#'
#' @name AlexCosmicArtif_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for Cosmic sigs, including artifacts
#'
#' \code{AlexCosmicArtif_sigInd_df}: Meta-information for
#' \code{AlexCosmicArtif_sig_df}
#'
#' @name AlexCosmicArtif_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for PCAWG SNV signatures (COSMIC v3), including artifacts
#'
#' \code{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.
#'
#' @source \code{PCAWG_SNV}: \url{https://www.synapse.org/#!Synapse:syn11738319}
#' @name PCAWG_SP_SBS_sigs_Artif_df
#' @rdname sigs_pcawg
#'
NULL
#' Meta-info for PCAWG SNV signatures, including artifacts
#'
#' \code{PCAWG_SP_SBS_sigInd_Artif_df}: Meta-information for
#' \code{PCAWG_SP_SBS_sigs_Artif_df}
#'
#' @name PCAWG_SP_SBS_sigInd_Artif_df
#' @rdname sigs_pcawg
#'
NULL
#' Data for PCAWG SNV signatures (COSMIC v3), only validated
#'
#' \code{PCAWG_SP_SBS_sigs_Real_df}: Data frame of only the validated
#' signatures published by Alexandrov et al. (Biorxiv 2018), corresponding
#' to the column 1-26, 28-42 and 44 of the \code{PCAWG_SP_SBS_sigs_Artif_df}
#' data frame
#'
#' @name PCAWG_SP_SBS_sigs_Real_df
#' @rdname sigs_pcawg
#'
NULL
#' Meta-info for PCAWG SNV signatures, only validated
#'
#' \code{PCAWG_SP_SBS_sigInd_Real_df}: Meta-information for
#' \code{PCAWG_SP_SBS_sigs_Real_df}
#'
#' @name PCAWG_SP_SBS_sigInd_Real_df
#' @rdname sigs_pcawg
#'
NULL
#' Data for PCAWG Indel signatures (COSMIC v3)
#'
#' \code{PCAWG_SP_ID_sigs_df}: Data frame with Indel signatures published by
#' Alexandrov et al. (Biorxiv 2018) which were decomposed with the method
#' SigProfiler. There are 17 Sigantures reported but as supervised signatures
#' are only valid for whole genome sequencing data analysis. In whole genome
#' sequencing data the Indel signature ID15 was not discribed and thus is not
#' part of this data set. In total 83 features are described. The categorization
#' consideres the size of the insertion and delition, the motif, and the
#' sequence context. Hereby the number of repetition or patial repetition of the
#' motif is determined.
#'
#' @source \code{PCAWG_INDEL}: \url{https://cancer.sanger.ac.uk/cosmic/
#' signatures/ID}
#' @name PCAWG_SP_ID_sigs_df
#' @rdname sigs_pcawg
#'
NULL
#' Meta-info for PCAWG Indel signatures
#'
#' \code{PCAWG_SP_ID_sigInd_df}: Meta-information for
#' \code{PCAWG_SP_ID_sigs_df}
#'
#' @name PCAWG_SP_ID_sigInd_df
#' @rdname sigs_pcawg
#'
NULL
#' Test and example data
#'
#' Data structures used in examples, SNV tests and the SNV signature vignette
#' of the YAPSA package.
#'
#' @docType data
#' @name exampleYAPSA
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @references \url{http://www.ncbi.nlm.nih.gov/pubmed/23945592}
#'
NULL
#' Data structures used in examples, Indel tests and the Indel signature
#' vignette of the YAPSA package.
#'
#' @docType data
#' @name exampleINDEL_YAPSA
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @references \url{http://www.ncbi.nlm.nih.gov/pubmed/23945592}
#'
NULL
#' Subgroup information for some samples in the vignette
#'
#' \code{lymphoma_PID_df}: A data frame carrying subgroup information for a
#' subcohort of samples used in the vignette. Data in the vignette is
#' downloaded from
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in. In the vignette we want to restrict the
#' analysis to only a fraction of these involved samples. The data frame
#' \code{lymphoma_PID_df} carries the sample identifiers (PID) as rownames and
#' the attributed subgroup in a column called \code{subgroup}.
#'
#' @name lymphoma_PID_df
#' @usage data(lymphoma_PID)
#' @rdname exampleYAPSA
#'
NULL
#' Test data for complex functions
#'
#' \code{lymphoma_test_df}: A data frame carrying point mutation calls. It
#' represents a subset of the data stored in
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in. The data frame \code{lymphoma_test_df} has
#' only the variants occuring in the sample identifiers (PIDs) 4112512, 4194218
#' and 4121361.
#'
#' @name lymphoma_test_df
#' @usage data(lymphoma_test)
#' @rdname exampleYAPSA
#'
#' @examples
#' data(lymphoma_test)
#' head(lymphoma_test_df)
#' dim(lymphoma_test_df)
#' table(lymphoma_test_df$PID)
#'
NULL
#' Example data for the vignette
#'
#' \code{lymphoma_Nature2013_raw_df}: A data frame carrying point mutation
#' calls. It represents a subset of the data stored in
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in.
#'
#' @name lymphoma_Nature2013_raw_df
#' @usage data(lymphoma_Nature2013_raw)
#' @rdname exampleYAPSA
#'
#' @examples
#' data(lymphoma_Nature2013_raw)
#' head(lymphoma_Nature2013_raw_df)
#' dim(lymphoma_Nature2013_raw_df)
#'
NULL
#' Example data for the Indel vignette
#'
#' \code{GenomeOfNl_raw}: A data frame contains the gemiline varinats of
#' the dutch population. carrying point mutation
#' calls. It represents a subset of the data stored in
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/
#' somatic_mutation_data/Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in.
#'
#' @name GenomeOfNl_raw
#' @usage data(GenomeOfNl_raw)
#' @references release version 5 \url{http://www.nlgenome.nl/?page_id=9}
#' @return A data frame in a vcf-like format
#'
#' @examples
#' data(GenomeOfNl_raw)
#' head(GenomeOfNl_raw)
#' dim(GenomeOfNl_raw)
#'
NULL
#' Example mutational catalog for the SNV vignette
#'
#' \code{lymphomaNature2013_mutCat_df}: A data frame in the format of a SNV mutation catalog.
#' The mutational catalog contains SNV variants from the
#' \code{lymphoma_Nature2013_raw_df} data. Mutational catalog was created with
#' \code{create_mutation_catalogue_from_df} function.
#'
#' @name lymphomaNature2013_mutCat_df
#' @usage data(lymphomaNature2013_mutCat_df)
#' @references paste0("ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/",
#' "somatic_mutation_data/Lymphoma B-cell/",
#' "Lymphoma B-cell_clean_somatic_mutations_",
#' "for_signature_analysis.txt")
#' @return A data fame in the layout of a SNV mutational catalog
#'
#' @examples
#' data(lymphomaNature2013_mutCat_df)
#' head(lymphomaNature2013_mutCat_df)
#' dim(lymphomaNature2013_mutCat_df)
#'
NULL
#' Example mutational catalog for the exome vignette
#'
#' \code{exome_mutCatRaw_df}: A data frame in the format of a SNV mutation
#' catalog. The mutational catalog contains SNV variants from a cohort of
#' small-cell lung cancer published by Rudin et al. (Nature Genetics 2012)
#' which was later used in the de novo discovery analysis of mutational
#' signatures in human cancer by Alexandrov et al. (Nature 2013).
#'
#' @name exome_mutCatRaw_df
#' @usage data(smallCellLungCancerMutCat_NatureGenetics2012)
#' @references \url{https://www.nature.com/articles/ng.2405}
#' @return A data fame in the layout of a SNV mutational catalog
#'
#' @examples
#' data(smallCellLungCancerMutCat_NatureGenetics2012)
#' head(exome_mutCatRaw_df)
#' dim(exome_mutCatRaw_df)
#'
NULL
#' Example mutational catalog for the Indel vignette
#'
#' \code{MutCat_indel_df}: A data frame in the format of a mutation catalog.
#' The mutational catalog contains Indel variants from the
#' \code{GenomeOfNl_raw} data. Variants were random sampled for 15 artificial
#' patient for the purpose to have a Indel mutational catalog and have to
#' show the functionality of the package. The results of the mutational
#' catalog should not be interpreted fot they biological relevance.
#' Mutational catalog was created with
#' \code{create_indel_mutation_catalogue_from_df} function.
#'
#' @name MutCat_indel_df
#' @usage data(GenomeOfNl_MutCat)
#' @references Mutational catalog created form release version 5 of the Genome
#' of NL
#' \url{http://www.nlgenome.nl/?page_id=9}
#' @return A data fame in the layout of a Indel mutational catalog
#'
#' @examples
#' data(GenomeOfNl_MutCat)
#' head(MutCat_indel_df)
#' dim(MutCat_indel_df)
#'
NULL
#' Test exposures for plot functions
#'
#' \code{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}: Data frame with
#' exposures for testing the plot functions. Data taken from
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#'
#' @name lymphoma_Nature2013_COSMIC_cutoff_exposures_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Test normalized exposures for plot functions
#'
#' \code{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}: Data frame with
#' normalized or relative exposures for testing the plot functions. Data taken
#' from
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#'
#' @name rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Subgroup information for test data for plot functions
#'
#' \code{COSMIC_subgroups_df}: Subgroup information for the data stored in
#' \code{\link{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}} and
#' \code{\link{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}}.
#'
#' @name COSMIC_subgroups_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Sigs info (initial, including artifacts) for test data for plot functions
#'
#' \code{chosen_AlexInitialArtif_sigInd_df}: Signature information for the
#' data stored in
#' \code{\link{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}} and
#' \code{\link{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}}.
#'
#' @name chosen_AlexInitialArtif_sigInd_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Sigs info (Cosmic, only validated) for test data for plot functions
#'
#' \code{chosen_signatures_indices_df}: Signature information for the data
#' stored in
#' \code{\link{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}} and
#' \code{\link{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}}.
#'
#' @name chosen_signatures_indices_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Cutoffs for a supervised analysis of mutational signatures.
#'
#' Series of data frames with signature-specific cutoffs. All values represent
#' optimal cutoffs. The optimal cutoffs were determined for different choices
#' of parameters in the cost function of the optimization. The row index is
#' equivalent to the ratio between costs for false negative attribution and
#' false positive attribution. The columns correspond to the different
#' signatures. To be used with \code{\link{LCD_complex_cutoff}}.
#' There are two different sets of cutoffs one for the signatures described by
#' Alexandrov et al.(Natue 2013) and one for the signatures dokumented in
#' Alexandriv et al. (biorxiv 2018). The calculation of the PCAWG signature
#' specific cutoffs was perfomed in a single-sample resolution which are both
#' valid for whole genome and whole exome sequencing data analysis.
#'
#' @docType data
#' @name cutoffs
#' @usage data(cutoffs)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#'
NULL
#' @docType data
#' @name cutoffs_pcawg
#' @usage data(cutoffs_pcawg)
#' @author Lea Jopp-Saile \email{huebschmann.daniel@@googlemail.com}
#'
NULL
#' Opt. cutoffs, rel exposures for the COSMIC sigs, only validated
#'
#' \code{cutoffCosmicValid_rel_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicValid_sig_df}}, i.e. COSMIC signatures, only
#' validated, trained on relative exposures.
#'
#' @name cutoffCosmicValid_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, rel exposures for the COSMIC sigs, including artifacts
#'
#' \code{cutoffCosmicArtif_rel_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicArtif_sig_df}}, i.e. COSMIC signatures, including
#' artifact signatures, trained on relative exposures.
#'
#' @name cutoffCosmicArtif_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the COSMIC sigs, only validated
#'
#' \code{cutoffCosmicValid_abs_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicValid_sig_df}}, i.e. COSMIC signatures, only
#' validated, trained on absolute exposures.
#'
#' @name cutoffCosmicValid_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the COSMIC sigs, including artifacts
#'
#' \code{cutoffCosmicArtif_abs_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicArtif_sig_df}}, i.e. COSMIC signatures, including
#' artifact signatures, trained on absolute exposures.
#'
#' @name cutoffCosmicArtif_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, rel exposures for the initial sigs, only validated
#'
#' \code{cutoffInitialValid_rel_df}: Optimal cutoffs for
#' \code{\link{AlexInitialValid_sig_df}}, i.e. initially published signatures,
#' only validated signatures, trained on relative exposures.
#'
#' @name cutoffInitialValid_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, rel exposures for the initial sigs, including artifacts
#'
#' \code{cutoffInitialArtif_rel_df}: Optimal cutoffs for
#' \code{\link{AlexInitialArtif_sig_df}}, i.e. initially published signatures,
#' including artifact signatures, trained on relative exposures.
#'
#' @name cutoffInitialArtif_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the initial sigs, only validated
#'
#' \code{cutoffInitialValid_abs_df}: Optimal cutoffs for
#' \code{\link{AlexInitialValid_sig_df}}, i.e. initially published signatures,
#' only validated signatures, trained on absolute exposures.
#'
#' @name cutoffInitialValid_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the initial sigs, including artifacts
#'
#' \code{cutoffInitialArtif_abs_df}: Optimal cutoffs for
#' \code{\link{AlexInitialArtif_sig_df}}, i.e. initially published signatures,
#' including artifact signatures, trained on absolute exposures.
#'
#' @name cutoffInitialArtif_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, PCAWG SNV signatures, including artifacts
#'
#' \code{cutoffPCAWG_SBS_WGSWES_artifPid_df}: Optimal cutoffs for
#' \code{\link{PCAWG_SP_SBS_sigs_Artif_df}}, i.e. initially published
#' signatures,including artifact signatures, trained in a single-sample
#' resolution.
#'
#' @name cutoffPCAWG_SBS_WGSWES_artifPid_df
#' @rdname cutoffs_pcawg
#'
NULL
#' Opt. cutoffs, PCAWG SNV signatures, only validated signatures
#'
#' \code{cutoffPCAWG_SBS_WGSWES_realPid_df}: Optimal cutoffs for
#' \code{\link{PCAWG_SP_SBS_sigs_Real_df}}, i.e. initially published
#' signatures, only validated signatures, trained in a single-sample
#' resolution.
#'
#' @name cutoffPCAWG_SBS_WGSWES_realPid_df
#' @rdname cutoffs_pcawg
#'
NULL
#' Opt. cutoffs, PCAWG Indel signatures, only validated signatures
#'
#' \code{cutoffPCAWG_ID_WGS_Pid_df}: Optimal cutoffs for
#' \code{\link{PCAWG_SP_ID_sigs_df}}, i.e. initially published signatures,
#' signatures, trained in a single-sample resolution.
#'
#' @name cutoffPCAWG_ID_WGS_Pid_df
#' @rdname cutoffs_pcawg
#'
NULL
#' Correction factors for different target capture kits
#'
#' List of lists with correction factors for different target capture kits.
#' The elements of the overall list are lists, every one carrying information
#' for one target capture kit (and namend after it). The elements of these
#' sublists are 64 dimensional vectors with correction factors for all
#' triplets. They were computed using counts of occurence of the respective
#' triplets in the target capture and in the reference genome and making
#' ratios (either for the counts themselves as in \code{abs_cor} or for the
#' relative occurences in \code{rel_cor}). The information in this data
#' structure may be used as input to
#' \code{\link{normalizeMotifs_otherRownames}}.
#'
#' @docType data
#' @name targetCapture_cor_factors
#' @usage data(targetCapture_cor_factors)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @return A list of lists of data frames
#'
NULL
#' Colours codes for displaying SNVs
#'
#' Vector attributing colours to nucleotide exchanges used when displaying
#' SNV information, e.g. in a rainfall plot.
#'
#' @docType data
#' @name exchange_colour_vector
#' @usage data(exchange_colour_vector)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @return A named character vector
#'
NULL
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# Copyright © 2014-2019 The YAPSA package contributors
# This file is part of the YAPSA package. The YAPSA package is licenced under
# GPL-3
#' Data for mutational signatures
#'
#' The numerical data of the mutational signatures published initially by
#' Alexandrov et al. (Nature 2013) and Alexandrov et al., (Bioaxiv 2018) is
#' stored in data frames with endings \code{_sig_df}, the associated
#' meta-information is stored in data frames with endings \code{_sigInd_df}.
#' There are several instances of \code{_sig_df} and \code{_sigInd_df},
#' corresponding to results and data obtained at different times and with
#' different raw data. There always is a one-to-one correspondence between
#' a \code{_sig_df} and a \code{_sigInd_df}. The data frames of type
#' \code{_sig_df} have as many rows as there are features, i.e. 96 if
#' analyzing mutational signatures of SNVs in a triplet context, and as
#' many columns as there are signatures.
#' Data frames of type \code{_sigInd_df} have as many rows as there are
#' signatures in the corresponding \code{_sig_df} and several columns:
#' \itemize{
#' \item \code{sig}: signature name
#' \item \code{index}: corresponding to the row index of the signature
#' \item \code{colour}: colour for visualization in stacked barplots
#' \item \code{process}: asserted biological process
#' \item \code{cat.coarse}: categorization of the signatures according
#' to the asserted biological processes at low level of detail
#' \item \code{cat.medium}: categorization of the signatures according
#' to the asserted biological processes at intermediate level of detail
#' \item \code{cat.high}: categorization of the signatures according
#' to the asserted biological processes at high level of detail
#' \item \code{cat.putative}: categorization of the signatures according
#' to the asserted biological processes based on clustering and inference
#' }
#' Please note, that categorization columns are only present for the data
#' frames corrosponding to the data from Alexandorv et al. (Nature 2013).
#'
#' @docType data
#' @name sigs
#' @usage data(sigs)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @references Alexandrov et al. (Nature 2013)
#'
NULL
#' @docType data
#' @name sigs_pcawg
#' @usage data(sigs_pcawg)
#' @author Lea Jopp-Saile \email{huebschmann.daniel@@googlemail.com}
#' @references Alexandrov et al. (Biorxiv 2018)
#'
NULL
#' Data for initial sigs, including artifacts
#'
#' \code{AlexInitialArtif_sig_df}: Data frame of the signatures published
#' initially by Alexandrov et al.
#' (Nature 2013). There are 27 signatures which constitute the columns, 22 of
#' which were validated by an orhtogonal sequencing technology. These 22 are in
#' the first 22 columns of the data frame. The column names are \emph{A} pasted
#' to the number of the signature, e.g. \emph{A5}. The nonvalidated signatures
#' have an additional letter in their naming convention: either
#' \emph{AR1} - \emph{AR3} or \emph{AU1} - \emph{AU2}. The rownames are the
#' features, i.e. an encoding of the nucleotide exchanges in their
#' trinucleotide context, e.g. \emph{C>A ACA}. In total there are 96 different
#' features and therefore 96 rows when dealing with a trinucleotide context.
#'
#' @source \code{AlexInitial}: \url{ftp://ftp.sanger.ac.uk/pub/cancer/
#' AlexandrovEtAl/signatures.txt}
#' @name AlexInitialArtif_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for initial sigs, including artifacts
#'
#' \code{AlexInitialArtif_sigInd_df}: Meta-information for
#' \code{AlexInitialArtif_sig_df}
#'
#' @name AlexInitialArtif_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for initial sigs, only validated
#'
#' \code{AlexInitialValid_sig_df}: Data frame of only the validated signatures
#' published initially by Alexandrov et al. (Nature 2013), corresponding to the
#' first 22 columns of \code{AlexInitialArtif_sig_df}
#'
#' @name AlexInitialValid_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for initial sigs, only validated
#'
#' \code{AlexInitialValid_sigInd_df}: Meta-information for
#' \code{AlexInitialValid_sig_df}
#'
#' @name AlexInitialValid_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for Cosmic sigs, only validated
#'
#' \code{AlexCosmicValid_sig_df}: Data frame of the updated signatures list
#' maintained by Ludmil Alexandrov at
#' \url{http://cancer.sanger.ac.uk/cosmic/signatures}. The column names are
#' \emph{AC} pasted to the number of the signature, e.g. \emph{AC5}. The naming
#' convention for the rows is as described for
#' \code{\link{AlexInitialArtif_sig_df}}.
#'
#' @source \code{AlexCosmic}: \url{http://cancer.sanger.ac.uk/cancergenome/
#' assets/signatures_probabilities.txt}
#' @name AlexCosmicValid_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for Cosmic sigs, only validated
#'
#' \code{AlexCosmicValid_sigInd_df}: Meta-information for
#' \code{AlexCosmicValid_sig_df}
#'
#' @name AlexCosmicValid_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for Cosmic sigs, including artifacts
#'
#' \code{AlexCosmicArtif_sig_df}: Data frame of the updated signatures list
#' maintained by Ludmil Alexandrov at
#' \url{http://cancer.sanger.ac.uk/cosmic/signatures} and complemented by the
#' artifact signatures from the initial publication, i.e. the last 5 columns of
#' \code{\link{AlexInitialArtif_sig_df}}. The column names are \emph{AC} pasted
#' to the number of the signature, e.g. \emph{AC5}. The naming convention for
#' the rows is as described for \code{\link{AlexInitialArtif_sig_df}}.
#'
#' @name AlexCosmicArtif_sig_df
#' @rdname sigs
#'
NULL
#' Meta-info for Cosmic sigs, including artifacts
#'
#' \code{AlexCosmicArtif_sigInd_df}: Meta-information for
#' \code{AlexCosmicArtif_sig_df}
#'
#' @name AlexCosmicArtif_sigInd_df
#' @rdname sigs
#'
NULL
#' Data for PCAWG SNV signatures (COSMIC v3), including artifacts
#'
#' \code{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.
#'
#' @source \code{PCAWG_SNV}: \url{https://www.synapse.org/#!Synapse:syn11738319}
#' @name PCAWG_SP_SBS_sigs_Artif_df
#' @rdname sigs_pcawg
#'
NULL
#' Meta-info for PCAWG SNV signatures, including artifacts
#'
#' \code{PCAWG_SP_SBS_sigInd_Artif_df}: Meta-information for
#' \code{PCAWG_SP_SBS_sigs_Artif_df}
#'
#' @name PCAWG_SP_SBS_sigInd_Artif_df
#' @rdname sigs_pcawg
#'
NULL
#' Data for PCAWG SNV signatures (COSMIC v3), only validated
#'
#' \code{PCAWG_SP_SBS_sigs_Real_df}: Data frame of only the validated
#' signatures published by Alexandrov et al. (Biorxiv 2018), corresponding
#' to the column 1-26, 28-42 and 44 of the \code{PCAWG_SP_SBS_sigs_Artif_df}
#' data frame
#'
#' @name PCAWG_SP_SBS_sigs_Real_df
#' @rdname sigs_pcawg
#'
NULL
#' Meta-info for PCAWG SNV signatures, only validated
#'
#' \code{PCAWG_SP_SBS_sigInd_Real_df}: Meta-information for
#' \code{PCAWG_SP_SBS_sigs_Real_df}
#'
#' @name PCAWG_SP_SBS_sigInd_Real_df
#' @rdname sigs_pcawg
#'
NULL
#' Data for PCAWG Indel signatures (COSMIC v3)
#'
#' \code{PCAWG_SP_ID_sigs_df}: Data frame with Indel signatures published by
#' Alexandrov et al. (Biorxiv 2018) which were decomposed with the method
#' SigProfiler. There are 17 Sigantures reported but as supervised signatures
#' are only valid for whole genome sequencing data analysis. In whole genome
#' sequencing data the Indel signature ID15 was not discribed and thus is not
#' part of this data set. In total 83 features are described. The categorization
#' consideres the size of the insertion and delition, the motif, and the
#' sequence context. Hereby the number of repetition or patial repetition of the
#' motif is determined.
#'
#' @source \code{PCAWG_INDEL}: \url{https://cancer.sanger.ac.uk/cosmic/
#' signatures/ID}
#' @name PCAWG_SP_ID_sigs_df
#' @rdname sigs_pcawg
#'
NULL
#' Meta-info for PCAWG Indel signatures
#'
#' \code{PCAWG_SP_ID_sigInd_df}: Meta-information for
#' \code{PCAWG_SP_ID_sigs_df}
#'
#' @name PCAWG_SP_ID_sigInd_df
#' @rdname sigs_pcawg
#'
NULL
#' Test and example data
#'
#' Data structures used in examples, SNV tests and the SNV signature vignette
#' of the YAPSA package.
#'
#' @docType data
#' @name exampleYAPSA
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @references \url{http://www.ncbi.nlm.nih.gov/pubmed/23945592}
#'
NULL
#' Data structures used in examples, Indel tests and the Indel signature
#' vignette of the YAPSA package.
#'
#' @docType data
#' @name exampleINDEL_YAPSA
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @references \url{http://www.ncbi.nlm.nih.gov/pubmed/23945592}
#'
NULL
#' Subgroup information for some samples in the vignette
#'
#' \code{lymphoma_PID_df}: A data frame carrying subgroup information for a
#' subcohort of samples used in the vignette. Data in the vignette is
#' downloaded from
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in. In the vignette we want to restrict the
#' analysis to only a fraction of these involved samples. The data frame
#' \code{lymphoma_PID_df} carries the sample identifiers (PID) as rownames and
#' the attributed subgroup in a column called \code{subgroup}.
#'
#' @name lymphoma_PID_df
#' @usage data(lymphoma_PID)
#' @rdname exampleYAPSA
#'
NULL
#' Test data for complex functions
#'
#' \code{lymphoma_test_df}: A data frame carrying point mutation calls. It
#' represents a subset of the data stored in
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in. The data frame \code{lymphoma_test_df} has
#' only the variants occuring in the sample identifiers (PIDs) 4112512, 4194218
#' and 4121361.
#'
#' @name lymphoma_test_df
#' @usage data(lymphoma_test)
#' @rdname exampleYAPSA
#'
#' @examples
#' data(lymphoma_test)
#' head(lymphoma_test_df)
#' dim(lymphoma_test_df)
#' table(lymphoma_test_df$PID)
#'
NULL
#' Example data for the vignette
#'
#' \code{lymphoma_Nature2013_raw_df}: A data frame carrying point mutation
#' calls. It represents a subset of the data stored in
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in.
#'
#' @name lymphoma_Nature2013_raw_df
#' @usage data(lymphoma_Nature2013_raw)
#' @rdname exampleYAPSA
#'
#' @examples
#' data(lymphoma_Nature2013_raw)
#' head(lymphoma_Nature2013_raw_df)
#' dim(lymphoma_Nature2013_raw_df)
#'
NULL
#' Example data for the Indel vignette
#'
#' \code{GenomeOfNl_raw}: A data frame contains the gemiline varinats of
#' the dutch population. carrying point mutation
#' calls. It represents a subset of the data stored in
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/
#' somatic_mutation_data/Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#' In the file available under that link somatic point mutation calls from
#' several samples are listed in a vcf-like format. One column encodes the
#' sample the variant was found in.
#'
#' @name GenomeOfNl_raw
#' @usage data(GenomeOfNl_raw)
#' @references release version 5 \url{http://www.nlgenome.nl/?page_id=9}
#' @return A data frame in a vcf-like format
#'
#' @examples
#' data(GenomeOfNl_raw)
#' head(GenomeOfNl_raw)
#' dim(GenomeOfNl_raw)
#'
NULL
#' Example mutational catalog for the SNV vignette
#'
#' \code{lymphomaNature2013_mutCat_df}: A data frame in the format of a SNV mutation catalog.
#' The mutational catalog contains SNV variants from the
#' \code{lymphoma_Nature2013_raw_df} data. Mutational catalog was created with
#' \code{create_mutation_catalogue_from_df} function.
#'
#' @name lymphomaNature2013_mutCat_df
#' @usage data(lymphomaNature2013_mutCat_df)
#' @references paste0("ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/",
#' "somatic_mutation_data/Lymphoma B-cell/",
#' "Lymphoma B-cell_clean_somatic_mutations_",
#' "for_signature_analysis.txt")
#' @return A data fame in the layout of a SNV mutational catalog
#'
#' @examples
#' data(lymphomaNature2013_mutCat_df)
#' head(lymphomaNature2013_mutCat_df)
#' dim(lymphomaNature2013_mutCat_df)
#'
NULL
#' Example mutational catalog for the exome vignette
#'
#' \code{exome_mutCatRaw_df}: A data frame in the format of a SNV mutation
#' catalog. The mutational catalog contains SNV variants from a cohort of
#' small-cell lung cancer published by Rudin et al. (Nature Genetics 2012)
#' which was later used in the de novo discovery analysis of mutational
#' signatures in human cancer by Alexandrov et al. (Nature 2013).
#'
#' @name exome_mutCatRaw_df
#' @usage data(smallCellLungCancerMutCat_NatureGenetics2012)
#' @references \url{https://www.nature.com/articles/ng.2405}
#' @return A data fame in the layout of a SNV mutational catalog
#'
#' @examples
#' data(smallCellLungCancerMutCat_NatureGenetics2012)
#' head(exome_mutCatRaw_df)
#' dim(exome_mutCatRaw_df)
#'
NULL
#' Example mutational catalog for the Indel vignette
#'
#' \code{MutCat_indel_df}: A data frame in the format of a mutation catalog.
#' The mutational catalog contains Indel variants from the
#' \code{GenomeOfNl_raw} data. Variants were random sampled for 15 artificial
#' patient for the purpose to have a Indel mutational catalog and have to
#' show the functionality of the package. The results of the mutational
#' catalog should not be interpreted fot they biological relevance.
#' Mutational catalog was created with
#' \code{create_indel_mutation_catalogue_from_df} function.
#'
#' @name MutCat_indel_df
#' @usage data(GenomeOfNl_MutCat)
#' @references Mutational catalog created form release version 5 of the Genome
#' of NL
#' \url{http://www.nlgenome.nl/?page_id=9}
#' @return A data fame in the layout of a Indel mutational catalog
#'
#' @examples
#' data(GenomeOfNl_MutCat)
#' head(MutCat_indel_df)
#' dim(MutCat_indel_df)
#'
NULL
#' Test exposures for plot functions
#'
#' \code{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}: Data frame with
#' exposures for testing the plot functions. Data taken from
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#'
#' @name lymphoma_Nature2013_COSMIC_cutoff_exposures_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Test normalized exposures for plot functions
#'
#' \code{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}: Data frame with
#' normalized or relative exposures for testing the plot functions. Data taken
#' from
#' \url{ftp://ftp.sanger.ac.uk/pub/cancer/AlexandrovEtAl/somatic_mutation_data/
#' Lymphoma B-cell/
#' Lymphoma B-cell_clean_somatic_mutations_for_signature_analysis.txt}.
#'
#' @name rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Subgroup information for test data for plot functions
#'
#' \code{COSMIC_subgroups_df}: Subgroup information for the data stored in
#' \code{\link{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}} and
#' \code{\link{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}}.
#'
#' @name COSMIC_subgroups_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Sigs info (initial, including artifacts) for test data for plot functions
#'
#' \code{chosen_AlexInitialArtif_sigInd_df}: Signature information for the
#' data stored in
#' \code{\link{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}} and
#' \code{\link{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}}.
#'
#' @name chosen_AlexInitialArtif_sigInd_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Sigs info (Cosmic, only validated) for test data for plot functions
#'
#' \code{chosen_signatures_indices_df}: Signature information for the data
#' stored in
#' \code{\link{lymphoma_Nature2013_COSMIC_cutoff_exposures_df}} and
#' \code{\link{rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df}}.
#'
#' @name chosen_signatures_indices_df
#' @usage data(lymphoma_cohort_LCD_results)
#' @rdname exampleYAPSA
#'
NULL
#' Cutoffs for a supervised analysis of mutational signatures.
#'
#' Series of data frames with signature-specific cutoffs. All values represent
#' optimal cutoffs. The optimal cutoffs were determined for different choices
#' of parameters in the cost function of the optimization. The row index is
#' equivalent to the ratio between costs for false negative attribution and
#' false positive attribution. The columns correspond to the different
#' signatures. To be used with \code{\link{LCD_complex_cutoff}}.
#' There are two different sets of cutoffs one for the signatures described by
#' Alexandrov et al.(Natue 2013) and one for the signatures dokumented in
#' Alexandriv et al. (biorxiv 2018). The calculation of the PCAWG signature
#' specific cutoffs was perfomed in a single-sample resolution which are both
#' valid for whole genome and whole exome sequencing data analysis.
#'
#' @docType data
#' @name cutoffs
#' @usage data(cutoffs)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#'
NULL
#' @docType data
#' @name cutoffs_pcawg
#' @usage data(cutoffs_pcawg)
#' @author Lea Jopp-Saile \email{huebschmann.daniel@@googlemail.com}
#'
NULL
#' Opt. cutoffs, rel exposures for the COSMIC sigs, only validated
#'
#' \code{cutoffCosmicValid_rel_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicValid_sig_df}}, i.e. COSMIC signatures, only
#' validated, trained on relative exposures.
#'
#' @name cutoffCosmicValid_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, rel exposures for the COSMIC sigs, including artifacts
#'
#' \code{cutoffCosmicArtif_rel_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicArtif_sig_df}}, i.e. COSMIC signatures, including
#' artifact signatures, trained on relative exposures.
#'
#' @name cutoffCosmicArtif_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the COSMIC sigs, only validated
#'
#' \code{cutoffCosmicValid_abs_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicValid_sig_df}}, i.e. COSMIC signatures, only
#' validated, trained on absolute exposures.
#'
#' @name cutoffCosmicValid_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the COSMIC sigs, including artifacts
#'
#' \code{cutoffCosmicArtif_abs_df}: Optimal cutoffs for
#' \code{\link{AlexCosmicArtif_sig_df}}, i.e. COSMIC signatures, including
#' artifact signatures, trained on absolute exposures.
#'
#' @name cutoffCosmicArtif_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, rel exposures for the initial sigs, only validated
#'
#' \code{cutoffInitialValid_rel_df}: Optimal cutoffs for
#' \code{\link{AlexInitialValid_sig_df}}, i.e. initially published signatures,
#' only validated signatures, trained on relative exposures.
#'
#' @name cutoffInitialValid_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, rel exposures for the initial sigs, including artifacts
#'
#' \code{cutoffInitialArtif_rel_df}: Optimal cutoffs for
#' \code{\link{AlexInitialArtif_sig_df}}, i.e. initially published signatures,
#' including artifact signatures, trained on relative exposures.
#'
#' @name cutoffInitialArtif_rel_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the initial sigs, only validated
#'
#' \code{cutoffInitialValid_abs_df}: Optimal cutoffs for
#' \code{\link{AlexInitialValid_sig_df}}, i.e. initially published signatures,
#' only validated signatures, trained on absolute exposures.
#'
#' @name cutoffInitialValid_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, abs exposures for the initial sigs, including artifacts
#'
#' \code{cutoffInitialArtif_abs_df}: Optimal cutoffs for
#' \code{\link{AlexInitialArtif_sig_df}}, i.e. initially published signatures,
#' including artifact signatures, trained on absolute exposures.
#'
#' @name cutoffInitialArtif_abs_df
#' @rdname cutoffs
#'
NULL
#' Opt. cutoffs, PCAWG SNV signatures, including artifacts
#'
#' \code{cutoffPCAWG_SBS_WGSWES_artifPid_df}: Optimal cutoffs for
#' \code{\link{PCAWG_SP_SBS_sigs_Artif_df}}, i.e. initially published
#' signatures,including artifact signatures, trained in a single-sample
#' resolution.
#'
#' @name cutoffPCAWG_SBS_WGSWES_artifPid_df
#' @rdname cutoffs_pcawg
#'
NULL
#' Opt. cutoffs, PCAWG SNV signatures, only validated signatures
#'
#' \code{cutoffPCAWG_SBS_WGSWES_realPid_df}: Optimal cutoffs for
#' \code{\link{PCAWG_SP_SBS_sigs_Real_df}}, i.e. initially published
#' signatures, only validated signatures, trained in a single-sample
#' resolution.
#'
#' @name cutoffPCAWG_SBS_WGSWES_realPid_df
#' @rdname cutoffs_pcawg
#'
NULL
#' Opt. cutoffs, PCAWG Indel signatures, only validated signatures
#'
#' \code{cutoffPCAWG_ID_WGS_Pid_df}: Optimal cutoffs for
#' \code{\link{PCAWG_SP_ID_sigs_df}}, i.e. initially published signatures,
#' signatures, trained in a single-sample resolution.
#'
#' @name cutoffPCAWG_ID_WGS_Pid_df
#' @rdname cutoffs_pcawg
#'
NULL
#' Correction factors for different target capture kits
#'
#' List of lists with correction factors for different target capture kits.
#' The elements of the overall list are lists, every one carrying information
#' for one target capture kit (and namend after it). The elements of these
#' sublists are 64 dimensional vectors with correction factors for all
#' triplets. They were computed using counts of occurence of the respective
#' triplets in the target capture and in the reference genome and making
#' ratios (either for the counts themselves as in \code{abs_cor} or for the
#' relative occurences in \code{rel_cor}). The information in this data
#' structure may be used as input to
#' \code{\link{normalizeMotifs_otherRownames}}.
#'
#' @docType data
#' @name targetCapture_cor_factors
#' @usage data(targetCapture_cor_factors)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @return A list of lists of data frames
#'
NULL
#' Colours codes for displaying SNVs
#'
#' Vector attributing colours to nucleotide exchanges used when displaying
#' SNV information, e.g. in a rainfall plot.
#'
#' @docType data
#' @name exchange_colour_vector
#' @usage data(exchange_colour_vector)
#' @author Daniel Huebschmann \email{huebschmann.daniel@@googlemail.com}
#' @return A named character vector
#'
NULL
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