### Install CNAqc Package

Source: https://github.com/caravagnalab/cnaqc/blob/master/README.md

Install the CNAqc package from GitHub using the devtools package. Ensure devtools is installed first if you haven't already.

```r
# install.packages("devtools")
devtools::install_github("caravagnalab/CNAqc")
```

--------------------------------

### Get mCNAqc Statistics

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/get_mCNAqc_stats.html

Example of how to call the get_mCNAqc_stats function. This example is conditional and will not run by default.

```r
if (FALSE) get_mCNAqc_stats(x) # \dontrun{}

```

--------------------------------

### Initialize CNAqc Object and Plot Segment Size Distribution

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/plot_segment_size_distribution.html

This example demonstrates how to initialize a CNAqc object using example data and then plot the distribution of segment sizes. Ensure the 'CNAqc' package is loaded and example data is available.

```r
data('example_dataset_CNAqc', package = 'CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
plot_segment_size_distribution(x)
```

--------------------------------

### GNU GPL Notice for Interactive Program Start

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/LICENSE.html

This is a template notice to be displayed when a program starts in interactive mode, informing users about its free software status and warranty.

```text
<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type 'show c' for details.
```

--------------------------------

### Initialize CNAqc object and extract drivers

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/get_drivers.html

This example demonstrates how to initialize a CNAqc object with example data and then use the `get_drivers` function to extract driver mutation information. It shows the expected output format.

```r
data('example_dataset_CNAqc', package = 'CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
get_drivers(x)
```

--------------------------------

### Load Example Data and Initialize CNAqc Object

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/CNA_gene.html

Loads the example dataset and initializes a CNAqc object. This is a prerequisite for using the CNA_gene function with sample data.

```R
data('example_dataset_CNAqc', package = 'CNAqc')
print(example_dataset_CNAqc)
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
```

--------------------------------

### Load and Print Example Dataset

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/init.html

Loads the example dataset 'example_dataset_CNAqc' provided with the CNAqc package and prints its contents. This dataset includes mutations, copy number alterations, purity, and reference genome information.

```R
data('example_dataset_CNAqc', package = 'CNAqc')
print(example_dataset_CNAqc)
```

--------------------------------

### Initialize CNAqc Object and Detect Overfragmentation

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/detect_arm_overfragmentation.html

This example demonstrates how to initialize a CNAqc object with example data and then apply the detect_arm_overfragmentation function. It also shows the expected output messages during the process.

```R
data('example_dataset_CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
#> 
#> ── CNAqc - CNA Quality Check ───────────────────────────────────────────────────
#> 
#> ℹ Using reference genome coordinates for: GRCh38.
#> ✔ Found annotated driver mutations: TTN, CTCF, and TP53.
#> ✔ Fortified calls for 12963 somatic mutations: 12963 SNVs (100%) and 0 indels.
#> ! CNAs have no CCF, assuming clonal CNAs (CCF = 1).
#> ✔ Fortified CNAs for 267 segments: 267 clonal and 0 subclonal.
#> ✔ 12963 mutations mapped to clonal CNAs.

x = detect_arm_overfragmentation(x)
#> ℹ One-tailed Binomial test: 8 tests, alpha 0.01. Short segments: 0.2% of the reference arm.
#> ✔ chr7p,  p = 1.7179869184e-24 ~ 34 segments, 34 short.
#> ✔ chr1p,  p = 1.62738995200002e-15 ~ 24 segments, 23 short.
#> ✔ chr11q,  p = 2.4788992e-07 ~ 14 segments, 12 short.
#> ✔ chr1q,  p = 4.34176000000001e-08 ~ 13 segments, 12 short.
#> ✔ chr12q,  p = 4.34176000000001e-08 ~ 13 segments, 12 short.
#> ✔ chr3q,  p = 4.52608e-06 ~ 12 segments, 10 short.
#> ✔ chr7q,  p = 4.52608e-06 ~ 12 segments, 10 short.
#> ✔ chr8p,  p = 9.21599999999998e-07 ~ 11 segments, 10 short.
#> ℹ 8 significantly overfragmented chromosome arms (alpha level 0.01).
```

--------------------------------

### Example: Convert CNAqc objects to maftools object

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/as_maftools_cohort.html

This example demonstrates the typical workflow for converting CNAqc objects to a maftools object. It involves initializing a CNAqc object, augmenting it with MAF annotations from a VCF file, and then extracting the maftools object.

```R
if(FALSE)
    {
       # Create your CNAqc object (omissis here) from an original "file.vcf"
       x = init(mutations = ..., cna = ..., purity = ...)
    
       # Offline, create your MAF annotations as file "file_vcf.maf" from "file.vcf"
       # vcf2maf file.vcf .... file_vcf.maf
    
       # Import into R/CNAqc
       x = augment_with_maf(x, maf = "file_vcf.maf")
    
       # Extraction
       x %>% as_maftools_obj
    }
```

--------------------------------

### Load Example Dataset

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a1_Introduction.html

Loads a template dataset provided by the CNAqc package for demonstration purposes.

```r
# Load template data
data('example_dataset_CNAqc', package = 'CNAqc')
```

--------------------------------

### Example Usage of annotate_variants

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/annotate_variants.html

Demonstrates how to use the annotate_variants function with example data. Load the CNAqc library, load an example dataset, extract mutations, and then annotate them.

```R
library(CNAqc)

data('example_dataset_CNAqc', package = 'CNAqc')

mutations <- example_dataset_CNAqc$mutations

mutations_annotated <- annotate_variants(mutations)
```

--------------------------------

### Get Sample Names from CNAqc/mCNAqc Object

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/get_sample_name.html

Use this function to retrieve a vector of sample names from a CNAqc or mCNAqc object. The example provided is a non-runnable placeholder.

```R
if (FALSE) get_sample_name(x) # \dontrun{}
    
    
```

--------------------------------

### Load and Display Example PCAWG Data

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a3_peaks_detection.html

Loads the example PCAWG object from the CNAqc package and prints a summary of its contents, including clonal and subclonal copy number alterations, driver mutations, and peak QC results. This is the initial step for performing QC analysis.

```R
library(CNAqc)
# We work with the PCAWG object
x = CNAqc::example_PCAWG

print(x)
```

--------------------------------

### Initialize CNAqc Object and Plot Genome-Wide Counts

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/plot_gw_counts.html

This snippet demonstrates how to initialize a CNAqc object with example data and then plot the genome-wide mutation counts. It requires the 'CNAqc' package and example dataset.

```R
data('example_dataset_CNAqc', package = 'CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
plot_gw_counts(x)
```

--------------------------------

### Initialize CNAqc Object and Smooth Segments

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/smooth_segments.html

This example demonstrates how to initialize a CNAqc object with example data and then apply the smooth_segments function to merge adjacent clonal copy number segments. It shows the state of the CNAqc object before and after smoothing, including plotting the smoothing results.

```r
data('example_dataset_CNAqc', package = 'CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
# Before smoothing
print(x)
x = smooth_segments(x)
# After smoothing
plot_smoothing(x)
```

--------------------------------

### Print PCAWG Example Data

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a2_Plots.html

This snippet prints the example PCAWG object from the CNAqc package, showing a summary of clonal and subclonal CNAs.

```r
CNAqc::example_PCAWG %>% print()
```

--------------------------------

### Print 'cnaqc' object

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/print.cnaqc.html

Demonstrates how to print an object of class 'cnaqc'. This example requires loading the 'example_PCAWG' dataset from the CNAqc package.

```R
if (FALSE) {
#
# data('example_PCAWG', package = 'CNAqc')
#
# print(example_PCAWG)
#
}
```

--------------------------------

### Initialize CNAqc object with example data

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a8_annotate_variants.html

Initializes a CNAqc object using example mutation, CNA, and purity data. It specifies the reference genome as 'hg19' and provides feedback on the data processing, including identified driver mutations and fortified calls.

```r
# Dataset available with the package 
data('example_dataset_CNAqc', package = 'CNAqc')

x = CNAqc::init(
  mutations = example_dataset_CNAqc$mutations, 
  cna = example_dataset_CNAqc$cna,
  purity = example_dataset_CNAqc$purity,
  ref = 'hg19')
#> 
#> ── CNAqc - CNA Quality Check ───────────────────────────────────────────────────
#> ℹ Using reference genome coordinates for: hg19.
#> ✔ Found annotated driver mutations: TTN, CTCF, and TP53.
#> ✔ Fortified calls for 12963 somatic mutations: 12963 SNVs (100%) and 0 indels.
#> ! CNAs have no CCF, assuming clonal CNAs (CCF = 1).
#> ✔ Fortified CNAs for 267 segments: 267 clonal and 0 subclonal.
#> ✔ 12963 mutations mapped to clonal CNAs.
```

--------------------------------

### Print m_cnaqc object

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/print.m_cnaqc.html

This snippet demonstrates how to print an object of class 'm_cnaqc'. It requires loading example data and then calling the print function on it. This example is wrapped in a \dontrun{} block, indicating it's for demonstration and might not be run by default.

```R
data('example_multisample', package = 'CNAqc')

print(example_multisample)
```

--------------------------------

### Load and Inspect Example PCAWG Tumour Data

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/example_PCAWG.html

Loads the example PCAWG tumour data into an R session and prints a summary of the CNAqc object. This is useful for exploring subclonal CNAs and associated QC metrics.

```r
data(example_PCAWG)
example_PCAWG
```

--------------------------------

### Load Example CNAqc Dataset

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/example_dataset_CNAqc.html

Loads the example_dataset_CNAqc into the R environment. This dataset contains SNVs, CNAs, and purity information.

```R
data(example_dataset_CNAqc)
```

--------------------------------

### Initialize CNAqc Object and Subset SNVs

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/subset_snvs.html

Demonstrates how to initialize a CNAqc object with example data and then use subset_snvs to filter for SNVs. This is useful for preparing data for SNV-focused analysis.

```r
data('example_dataset_CNAqc', package = 'CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
subset_snvs(x)
```

--------------------------------

### Inspect Example CNAqc Dataset

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/example_dataset_CNAqc.html

Displays the contents of the loaded example_dataset_CNAqc. The dataset is a list containing mutations, copy number alterations (cna), purity, and reference genome information.

```R
example_dataset_CNAqc
```

--------------------------------

### Get PASS percentage from CNAqc object

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/get_PASS_percentage.html

This snippet demonstrates how to use the get_PASS_PASS_percentage function with a CNAqc object. It requires loading example data and shows the expected tibble output.

```R
data("example_PCAWG", package = 'CNAqc')
get_PASS_percentage(example_PCAWG)
#> Joining with `by = join_by(QC_PASS)`
#> # A tibble: 2 × 4
#>   QC_PASS Length N_segments N_mutations
#>   <lgl>    <dbl>      <dbl>       <dbl>
#> 1 TRUE     0.437      0.164       0.437
#> 2 NA       0.563      0.836       0.563
```

--------------------------------

### Initialize CNAqc Object and Plot Icon CNA

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/plot_icon_CNA.html

This snippet demonstrates how to initialize a CNAqc object using example data and then generate a circular plot of clonal CNAs using the plot_icon_CNA function. Ensure the CNAqc package is installed and loaded.

```r
data('example_dataset_CNAqc', package = 'CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna = example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)
plot_icon_CNA(x)
```

--------------------------------

### Install Bioconductor Packages for Variant Annotation

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a8_annotate_variants.html

Installs required Bioconductor packages for variant annotation, including transcript databases, genome sequences, and utility packages. Ensure BiocManager is installed first.

```R
# Reference against which we mapped the reads
reference_genome <- example_dataset_CNAqc$reference

# All those packages are distributed in Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager", repos="http://cran.us.r-project.org")

# We have to install the corresponding txdb package for transcript annotations
paste0("TxDb.Hsapiens.UCSC.",reference_genome,".knownGene") %>% BiocManager::install()

# We have to install also the BS database for the sequences (it may take some time)
paste0("BSgenome.Hsapiens.UCSC.",reference_genome) %>% BiocManager::install()

# Then these two packages provide useful utilities to deal with biological databases
"Organism.dplyr" %>% BiocManager::install()
#> 
#> The downloaded binary packages are in
#>  /var/folders/d_/4f5j5g5d48xbfygg74bn6wyh0000gn/T//RtmpCn5CLa/downloaded_packages
"org.Hs.eg.db" %>% BiocManager::install()
```

--------------------------------

### Initializing CNAqc with Sequenza Output

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a7_example_MSeq.html

Loads Sequenza output for a low-cellularity sample and initializes the CNAqc object. This involves loading segments and purity information from Sequenza and then using CNAqc::init to prepare the data for analysis.

```r
# Low cellularity solution sequenza  (bad calls)
Sequenza_bad_calls = load_SQ_output(Sequenza_URL, sample = 'Set6_42', run = 'lowcell')

# CNA segments and purity
cna = Sequenza_bad_calls$segments
purity = Sequenza_bad_calls$purity

# CNAqc data object
x = CNAqc::init(
  mutations = snvs,
  cna = cna,
  purity = purity,
  ref = "GRCh38") %>%
  CNAqc::analyze_peaks(matching_strategy = 'closest')
```

--------------------------------

### Example Tumor Purity Value

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a1_Introduction.html

Shows an example of a single numeric value representing tumor purity, which is the percentage of reads from tumor cells, ranging from 0 to 1.

```r
print(example_dataset_CNAqc$purity)
    #> [1] 0.89
```

--------------------------------

### Post-process seqz File by Binning with Sequenza

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a9_automatic.html

This command takes the generated 'seqz' file and bins it, preparing it for normalization and segmentation. Specify the input 'seqz' file and desired output file name.

```bash
sequenza-utils seqz_binning --seqz out.seqz.gz -w 50 -o out small.seqz.gz
```

--------------------------------

### Convert CNAqc object to maftools object

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/as_maftools_obj.html

This example demonstrates the process of converting a CNAqc object to a maftools object. It includes steps for initializing the CNAqc object, augmenting it with MAF annotations (assuming an external vcf2maf conversion), and finally performing the conversion using as_maftools_obj.

```R
if(FALSE)
{
   # Create your CNAqc object (omissis here) from an original "file.vcf"
   x = init(mutations = ..., cna = ..., purity = ...)

   # Offline, create your MAF annotations as file "file_vcf.maf" from "file.vcf"
   # vcf2maf file.vcf .... file_vcf.maf

   # Import into R/CNAqc
   x = augment_with_maf(x, maf = "file_vcf.maf")

   # Extraction
   x %>% as_maftools_obj
}
```

--------------------------------

### Load and Print Sequenza Data for a Sample

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a7_example_MSeq.html

Loads Sequenza copy number data for a specific sample and run, then prints the loaded segments, purity, and ploidy.

```R
Sequenza_URL = "https://raw.githubusercontent.com/caravagnalab/CNAqc_datasets/main/MSeq_Set06/Copy%20Number/"

# Final sequenza run (good calls)
Sequenza_good_calls = load_SQ_output(Sequenza_URL, sample = 'Set6_42', run = 'final')

print(Sequenza_good_calls)
```

--------------------------------

### Standard GNU GPL Notice for Source Files

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/LICENSE.html

This is a template notice to be included at the beginning of each source file of a program to ensure it is free software and to state the exclusion of warranty.

```text
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
```

--------------------------------

### Initialize mCNAqc Object with Multiple Samples

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/b10_MultiCNAqc.html

Use `multisample_init` to create a multi-sample CNAqc object. Set `QC_filter` to TRUE to use only segments passing quality control across samples. `keep_original` preserves the initial CNAqc objects, and `discard_private` controls whether to retain mutations present in all samples.

```R
example_multisample = CNAqc::multisample_init(cnaqc_objs = CNAqc_samples, 
                                                  QC_filter = TRUE, # use only segments passing QC
                                                  keep_original = TRUE, # keep the original CNAqc objects too
                                                  discard_private = FALSE) # keep also not private mutations among the samples
```

--------------------------------

### Compute WT and mutant alleles per gene

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/wt_mutant_alleles.html

This example demonstrates how to use the wt_mutant_alleles function with a CNAqc object that has MAF annotations. It includes steps for initializing the CNAqc object, augmenting it with MAF data, and then calling the wt_mutant_alleles function.

```R
if(FALSE)
{
   # Create your CNAqc object (omissis here) from an original "file.vcf"
   x = init(mutations = ..., cna = ..., purity = ...)

   # Offline, create your MAF annotations as file "file_vcf.maf" from "file.vcf"
   # vcf2maf file.vcf .... file_vcf.maf

   # Import into R/CNAqc
   x = augment_with_maf(x, maf = "file_vcf.maf")

   # check they are in (there should be many columns with "MAF." prefix)
   x %>% Mutations %>% colnames

   # With MAF-imported that is the target column
   wt_mutant_alleles(x, gene_column = 'MAF.Hugo_Symbol')
}
```

--------------------------------

### Load CNAqc Package

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a6_fragmentation.html

Loads the CNAqc package in R. Ensure the package is installed before running.

```R
library(CNAqc)
#> ✔ Loading CNAqc, 'Copy Number Alteration quality check'. Support : <https://caravagn.github.io/CNAqc/>
```

--------------------------------

### Initialize Multiple CNAqc Objects

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/b10_MultiCNAqc.html

Create a list of CNAqc objects, one for each sample, by providing mutations, CNA data, purity, and sample name. Ensure the reference genome is specified. List names must match sample names.

```R
CNAqc_samples = lapply(names(sample_mutations), function(x) {
  CNAqc::init(mutations = sample_mutations[[x]], 
                  cna = cna[[x]], 
                  purity = purity[[x]], 
                  sample = x,
                  ref = "GRCh38")
})

names(CNAqc_samples) = sapply(CNAqc_samples, function(x) {x$sample})
```

--------------------------------

### Available Data

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/index.html

Lists and describes the datasets included within the CNAqc package, such as chromosome coordinates and example datasets.

```APIDOC
## Available Data

This section lists and describes the datasets included within the CNAqc package.

*   `chr_coordinates_hg19`: Coordinates for hg19 chromosomes.
*   `chr_coordinates_GRCh38`: Coordinates for GRCh38 chromosomes.
*   `chr_coordinates_GRCm38`: Coordinates for GRCm38 chromosomes.
*   `example_dataset_CNAqc`: Example CNAqc dataset.
*   `fpr_test`: Data (simulation performance) from the training set to auto-tune epsilon.
*   `example_PCAWG`: Example PCAWG tumour.
*   `intogen_drivers`: List of Intogen driver genes per tumour type.
*   `gene_coordinates_hg19`: Coordinates for hg19 genes.
*   `gene_coordinates_GRCh38`: Coordinates for GRCh38 genes.
```

--------------------------------

### Generate GC Wiggle Track File with Sequenza

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a9_automatic.html

This command processes a FASTA file to create a GC Wiggle track file, a prerequisite for Sequenza's BAM processing. Ensure the FASTA file path and output file name are correctly specified.

```bash
sequenza-utils gc_wiggle --fasta hg19.fa -o hg19.gc50Base.wig.gz
```

--------------------------------

### Load CNAqc Package and Dependencies

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/articles/a1_Introduction.html

Loads the CNAqc package and the dplyr package for data manipulation. Ensure these packages are installed before running.

```r
library(CNAqc)
#> ✔ Loading CNAqc, 'Copy Number Alteration quality check'. Support : <https://caravagn.github.io/CNAqc/>

require(dplyr)
#> Loading required package: dplyr
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
```

--------------------------------

### Initialize CNAqc object and augment with SBS data

Source: https://github.com/caravagnalab/cnaqc/blob/master/docs/reference/SBS.html

Initializes a CNAqc object with mutation and CNA data, then augments it with SBS data for mutational signature deconvolution. This example demonstrates the typical workflow for preparing SBS data.

```R
if (FALSE) { # \dontrun{
data('example_dataset_CNAqc')
x = init(mutations = example_dataset_CNAqc$mutations, cna =example_dataset_CNAqc$cna, purity = example_dataset_CNAqc$purity)

x = SBS(x)

# All SBS data
print(x$SBS)
} # }
```