Yet Another Package for Signature Analysis

Documentation for package ‘YAPSA’ version 1.32.0

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-- A --

add_annotation	Add information to an annotation data structure
add_as_fist_to_list	Add an element as first entry to a list
aggregate_exposures_by_category	Aggregate exposures by category
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
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
annotate_intermut_dist_cohort	Annotate the intermutation distance of variants cohort-wide
annotate_intermut_dist_PID	Annotate the intermutation distance of variants per PID
annotation_exposures_barplot	Plot the exposures of a cohort with different layers of annotation
annotation_exposures_list_barplot	Plot the exposures of a cohort with different layers of annotation for SNV and INDEL signatures
annotation_heatmap_exposures	Heatmap to cluster the PIDs on their signature exposures (ComplexHeatmap)
attribute_nucleotide_exchanges	Attribute the nucleotide exchange for an SNV
attribute_sequence_contex_indel	Attribution of sequence context and size for an INDEL
attribution_of_indels	Attribution of variant into one onf the 83 INDEL categories
average_over_present	Useful functions on data frames

-- B --

build_gene_list_for_pathway

Build a gene list for a given pathway name

-- C --

chosen_AlexInitialArtif_sigInd_df	Test and example data
chosen_signatures_indices_df	Test and example data
classify_indels	INDEL function V1 - not compartible with AlexandrovSignatures
compare_exposures	Compares alternative exposures
compare_expousre_sets	Compare two sets of exposures by cosine distance
compare_sets	Compare two sets of signatures by cosine distance
compare_SMCs	Compare all strata from different stratifications
compare_to_catalogues	Compare one mutational catalogue to reference mutational catalogues
complex_heatmap_exposures	Heatmap to cluster the PIDs on their signature exposures (ComplexHeatmap)
computeLogLik	Compute the loglikelihood
compute_comparison_stat_df	Extract statistical measures for entity comparison
confidence_indel_calulation	Wrapper to compute confidence intervals for SNV and INDEL signatures of a cohort or single-sample
confidence_indel_only_calulation	Wrapper to compute confidence intervals for only INDEL signatures.
confIntExp	Compute confidence intervals
correct_rounded	Readjust the vector to it's original norm after rounding
cosineDist	Compute the cosine distance of two vectors
cosineMatchDist	Compute an altered cosine distance of two vectors
COSMIC_subgroups_df	Test and example data
create_indel_mutation_catalogue_from_df	Wrapper to create an INDEL mutational catalog from a vlf-like data frame
create_indel_mut_cat_from_df	Create a Mutational catalog from a data frame
create_mutation_catalogue_from_df	Create a Mutational Catalogue from a data frame
create_mutation_catalogue_from_VR	Create a Mutational Catalogue from a VRanges Object
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
cutoffs	Cutoffs for a supervised analysis of mutational signatures.
cutoffs_pcawg	Opt. cutoffs, PCAWG SNV signatures, including artifacts
cut_breaks_as_intervals	Wrapper for cut

-- D --

deriveSigInd_df	Derive a signature_indices_df object
disambiguateVector	Disambiguate a vector

-- E --

enrichSigs	Compare to background distribution
exampleINDEL_YAPSA	Data structures used in examples, Indel tests and the Indel signature vignette of the YAPSA package.
exampleYAPSA	Test and example data
exchange_colour_vector	Colours codes for displaying SNVs
exome_mutCatRaw_df	Example mutational catalog for the exome vignette
exposures_barplot	Wrapper for enhanced_barplot
extract_names_from_gene_list	Return gene names from gene lists

-- F --

find_affected_PIDs

Find samples affected

-- G --

GenomeOfNl_raw	Example data for the Indel vignette
getSequenceContext	Extracts the sequence context up and downstream of a nucleotide position
get_extreme_PIDs	Return those PIDs which have an extreme pattern for signature exposure

-- H --

hclust_exposures

Cluster the PIDs according to their signature exposures

-- L --

LCD	Linear Combination Decomposition
LCD_complex_cutoff	LCD with a signature-specific cutoff on exposures
LCD_complex_cutoff_combined	LCD with a signature-specific cutoff on exposures
LCD_complex_cutoff_consensus	LCD with a signature-specific cutoff on exposures
LCD_complex_cutoff_perPID	LCD with a signature-specific cutoff on exposures
LCD_extractCohort_callPerPID	LCD with a signature-specific cutoff on exposures
LCD_SMC	CD stratification analysis
logLikelihood	Compute a loglikelihood ratio test
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

-- M --

makeVRangesFromDataFrame	Construct a VRanges Object from a data frame
make_catalogue_strata_df	Group strata from different stratification axes
make_comparison_matrix	Compute a similarity matrix for different strata
make_strata_df	Group strata from different stratification axes
make_subgroups_df	Make a custom data structure for subgroups
melt_exposures	Generically melts exposure data frames
merge_exposures	Merge exposure data frames
MutCat_indel_df	Example mutational catalog for the Indel vignette

-- N --

normalizeMotifs_otherRownames	Normalize Somatic Motifs with different rownames
normalize_df_per_dim	Useful functions on data frames

-- P --

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.
plotExchangeSpectra	Plot the spectra of nucleotide exchanges
plotExchangeSpectra_indel	Plot the spectra of nucleotide exchanges of INDELs
plotExposuresConfidence	Plot exposures including confidence intervals
plotExposuresConfidence_indel	Plot exposures including confidence intervals for exposures of SNVs and INDELs
plot_exposures	Plot the exposures of a cohort
plot_relative_exposures	Plot the exposures of a cohort
plot_SMC	Plot results of the Stratification of a Mutational Catalogue
plot_strata	Plot all strata from different stratification axes together

-- R --

read_entry	Read a single vcf-like file into a single data frame
read_list	Read a single vcf-like file into a single data frame
relateSigs	Make unique assignments between sets of signatures
rel_lymphoma_Nature2013_COSMIC_cutoff_exposures_df	Test and example data
repeat_df	Create a data frame with default values
round_precision	Round to a defined precision
run_annotate_vcf_pl	Wrapper function to annotate addition information
run_comparison_catalogues	Compare all strata from different stratifications
run_comparison_general	Compare all strata from different stratifications
run_kmer_frequency_correction	Provide comprehensive correction factors for kmer content
run_kmer_frequency_normalization	Provide normalized correction factors for kmer content
run_plot_strata_general	Wrapper function for 'plot_strata'
run_SMC	Wrapper function for the Stratification of a Mutational Catalogue

-- S --

sd_over_present	Useful functions on data frames
shapiro_if_possible	Wrapper for Shapiro test but allow for all identical values
sigs	Data for mutational signatures
sigs_pcawg	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.
SMC	Stratification of a Mutational Catalogue
SMC_perPID	Run SMC at a per sample level
split_exposures_by_subgroups	Split an exposures data frame by subgroups
stat_plot_subgroups	Plot averaged signature exposures per subgroup
stat_test_SMC	Apply statistical tests to a stratification (SMC)
stat_test_subgroups	Test for differences in average signature exposures between subgroups
stderrmean	Compute the standard error of the mean
stderrmean_over_present	Useful functions on data frames
sum_over_list_of_df	Elementwise sum over a list of (numerical) data frames

-- T --

targetCapture_cor_factors	Correction factors for different target capture kits
testSigs	Test for significance of alternative models cohort wide
test_exposureAffected	Test significance of association
test_gene_list_in_exposures	Test if mutated PIDs are enriched in signatures
transform_rownames_deconstructSigs_to_YAPSA	Change rownames from one naming convention to another
transform_rownames_MATLAB_to_R	Change rownames from one naming convention to another
transform_rownames_nature_to_R	Change rownames from one naming convention to another
transform_rownames_R_to_MATLAB	Change rownames from one naming convention to another
transform_rownames_YAPSA_to_deconstructSigs	Change rownames from one naming convention to another
translate_to_1kG	Translate chromosome names to the hg19 naming convention
translate_to_hg19	Translate chromosome names to the hg19 naming convention
trellis_rainfall_plot	Create a rainfall plot in a trellis structure

-- V --

variateExp	Wrapper to compute confidence intervals for a cohort
variateExpSingle	Wrapper for the likelihood ratio test

-- Y --

YAPSA

Generate R documentation from inline comments.