Package index • CNAqc

Available data References and datasets included in the package.
`chr_coordinates_hg19`	Coordinates for hg19 chromosomes.
`chr_coordinates_GRCh38`	Coordinates for GRCh38 chromosomes.
`example_dataset_CNAqc`	Example CNAqc dataset.
`example_PCAWG`	Example PCAWG tumour
`fpr_test`	Data (simulation performance) from the trainig set to auto-tune epsilon.
`intogen_drivers`	List of Intogen driver genes per tumour type.
`gene_coordinates_hg19`	Coordinates for hg19 genes
`gene_coordinates_GRCh38`	Coordinates for GRCh38 genes
Data processing Functions to manipulate and visualise the data.
`init()`	Creates a CNAqc object.
`Mutations()`	Extract mutations.
`CNA()`	Extract CNAs.
`CNA_gene()`	Extract per-gene copy number status.
`get_drivers()`	Extract drivers data.
`plot_data_histogram()`	Plot the read-counts data histograms.
`plot_segments()`	Plot CNA segments.
`plot_segment_size_distribution()`	Plot the length of clonal simple CNAs.
`plot_karyotypes()`	Plot counts and numbers of clonal simple CNAs.
`plot_gw_counts()`	Plot genome-wide mutation counts.
`plot_gw_depth()`	Plot genome-wide coverage.
`plot_gw_vaf()`	Plot genome-wide VAFs.
`plot_icon_CNA()`	A circular plot for simple clonal CNAs.
`plot_qc()`	Plot a summary of QC results.
`split_by_chromosome()`	Split a dataset by chromosome.
`subsample()`	Randomly subsample mutations.
`subset_by_segment_karyotype()`	Subset by clonal segments.
`subset_by_segment_totalcn()`	Subset clonal simple segments total copy number.
`subset_by_minimum_CCF()`	Subset mutations by minimum CCF
`subset_by_segment_minmutations()`	Retain clonal segments with minimum number of mutations.
`subset_snvs()`	Subset only SNVs.
`print(<cnaqc>)`	Print for class `'cnaqc'`.
`plot(<cnaqc>)`	Plot for class `'cnaqc'`.
`wt_mutant_alleles()`	Compute WT and mutant alleles per gene
Purity and ploidy QC Analysis of calls by peak detection.
`analyze_peaks()`	QC by peak-detection algorithms.
`auto_tolerance()`	Determine the optimal error tolerance to QC clonal simple CNAs.
`inspect_segment()`	Plot VAFs across chromosomes.
`plot_peaks_analysis()`	Plot the results of peak analysis.
`get_PASS_percentage()`	Returns percentage of passed segments
CCF estimation Computation of Cancer Cell Fractions data.
`compute_CCF()`	Compute CCF values.
`CCF()`	Extract CCF estimates.
`plot_CCF()`	Plot the CCF estimates in the data.
`plot_gw_ccf()`	Plot genome-wide CCFs.
Smoothing Analysis and plotting functions to smooth segments.
`smooth_segments()`	Smooth simple clonal CNAs.
`plot_smoothing()`	Plot smoothed and non-smoohted segments.
Overfragmentation detection Analysis and plotting functions to smooth segments.
`detect_arm_overfragmentation()`	Determines arm-level over-fragmentation patterns.
`plot_arm_fragmentation()`	Plot the arm level fragmentation test.
Pipelines and parsers Pipelines and parsers implemented with CNAqc.
`Sequenza_CNAqc()`	CNAqc-based purity-optimisation pipeline for Sequenza.
`parse_Battenberg()`	Parse Battenberg calls.
Variants annotation detection Annotation of putative variant drivers.
`annotate_variants()`	Annotate variants and drivers.
Cohort functions Functions to visualise multiple objects at once.
`plot_multisample_CNA()`	Plots CNAs from multiple samples.
Mutational Signatures Functions to convert and store mutational signature tables
`SBS()`	Augment SBS data for mutational signatures deconvolution
`SBS_counts()`	Extract SBS count data
`plot_SBS()`	Plost SBS counts
MAF interface Functions to convert from CNAqc to Maf format.
`as_maftools_obj()`	Convert a CNAqc object to a maftools object.
`as_maftools_cohort()`	Convert a list of CNAqc object to a maftools object.
`augment_with_maf()`	Import MAF annotations.
`augment_with_vep()`	Import VEP annotations.

Reference

Available data

Data processing

Purity and ploidy QC

CCF estimation

Smoothing

Overfragmentation detection

Pipelines and parsers

Variants annotation detection

Cohort functions

Mutational Signatures

MAF interface