### Install ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Recommended installation via R devtools or manual installation of dependencies.

```r
# Install ichorCNA from GitHub (Recommended)
install.packages("devtools")
library(devtools)
install_github("broadinstitute/ichorCNA")

# Manual installation - install dependencies first
install.packages("plyr")
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("HMMcopy")
BiocManager::install("GenomeInfoDb")
BiocManager::install("GenomicRanges")

# Then from command line in the ichorCNA directory:
# R CMD INSTALL ichorCNA
```

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

### Install ichorCNA package manually

Source: https://github.com/broadinstitute/ichorcna/wiki/Installation

Command line installation after cloning the repository.

```bash
## from the command line and in the directory where ichorCNA github was cloned.
R CMD INSTALL ichorCNA
```

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

### Install ichorCNA via devtools

Source: https://github.com/broadinstitute/ichorcna/wiki/Installation

Recommended method for installing the package directly from GitHub using R.

```R
install.packages("devtools")
library(devtools)
install_github("broadinstitute/ichorCNA")
```

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

### Launch ichorCNA R script

Source: https://github.com/broadinstitute/ichorcna/blob/master/scripts/README.md

Example command to execute the ichorCNA R script with specific input files, ploidy, and normal contamination parameters.

```bash
Rscript ../runIchorCNA.R --libdir ../../R/ --datadir ../../inst/extdata/ \
  --id tumor_sample1 --WIG results/readDepth/tumor_sample1.bin1000000.wig \
  --ploidy "c(2,3)" --normal "c(0.5,0.6,0.7,0.8,0.9)" --maxCN 5 \
  --includeHOMD False --chrs "c(1:22, \"X\")" --chrTrain "c(1:22)" \
  --estimateNormal True --estimatePloidy True --estimateScPrevalence True \
  --scStates "c(1,3)" --centromere ../../inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt \
  --exons.bed None --txnE 0.9999 --txnStrength 10000 --plotFileType png --plotYLim "c(-2,4)" \
  --outDir results/ichorCNA/tumor_sample1/ > logs/ichorCNA/tumor_sample1.log 2> logs/ichorCNA/tumor_sample1.log
```

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

### Initialize Tumor Fraction Parameter for Low Tumor Content

Source: https://github.com/broadinstitute/ichorcna/wiki/Parameter-tuning-and-settings

Set initial non-tumor values to guide the EM step towards better global optima for low tumor fraction samples. Expected values range from 5% down to 0.1%.

```bash
--normal "c(0.95, 0.99, 0.995, 0.999)"
```

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

### Install R dependencies

Source: https://github.com/broadinstitute/ichorcna/wiki/Installation

Required R packages for ichorCNA, including HMMcopy and GenomicRanges.

```R
## install from CRAN
install.packages("plyr") 
## install packages from
source("https://bioconductor.org/biocLite.R")
BiocManager::install("HMMcopy")  
BiocManager::install("GenomeInfoDb")  
BiocManager::install("GenomicRanges")
```

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

### Run HMMsegment in ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Runs the HMMsegment function in ichorCNA after initial setup. This function performs the core segmentation and copy number calling. Ensure that `tumour_copy`, `valid`, `param`, and `chrTrain` are properly defined before execution.

```r
library(ichorCNA)

# After running HMMsegment, correct integer copy numbers
hmmResults <- HMMsegment(list(sample=tumour_copy), valid, dataType="copy",
                         param=param, chrTrain=chrTrain, maxiter=50,
                         estimateNormal=TRUE, estimatePloidy=TRUE,
                         estimateSubclone=TRUE, verbose=TRUE)
```

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

### Display ichorCNA Help Options

Source: https://github.com/broadinstitute/ichorcna/wiki/Usage

Invoke the help flag to view all available configuration parameters and their default settings.

```bash
Rscript runIchorCNA.R --help
Usage: runIchorCNA.R [options]

Options:
	--WIG=WIG
		Path to tumor WIG file. Required.

	--NORMWIG=NORMWIG
		Path to normal WIG file. Default: [NULL]

	--gcWig=GCWIG
		Path to GC-content WIG file; Required

	--mapWig=MAPWIG
		Path to mappability score WIG file. Default: [NULL]

	--normalPanel=NORMALPANEL
		Median corrected depth from panel of normals. Default: [NULL]

	--exons.bed=EXONS.BED
		Path to bed file containing exon regions. Default: [NULL]

	--id=ID
		Patient ID. Default: [test]

	--centromere=CENTROMERE
		File containing Centromere locations; if not provided then will use hg19 version from ichorCNA package. Default: [NULL]

	--rmCentromereFlankLength=RMCENTROMEREFLANKLENGTH
		Length of region flanking centromere to remove. Default: [1e+05]

	--normal=NORMAL
		Initial normal contamination; can be more than one value if additional normal initializations are desired. Default: [0.5]

	--scStates=SCSTATES
		Subclonal states to consider. Default: [NULL]

	--coverage=COVERAGE
		PICARD sequencing coverage. Default: [NULL]

	--lambda=LAMBDA
		Initial Student's t precision; must contain 4 values (e.g. c(1500,1500,1500,1500)); if not provided then will automatically use based on variance of data. Default: [NULL]

	--lambdaScaleHyperParam=LAMBDASCALEHYPERPARAM
		Hyperparameter (scale) for Gamma prior on Student's-t precision. Default: [3]

	--ploidy=PLOIDY
		Initial tumour ploidy; can be more than one value if additional ploidy initializations are desired. Default: [2]

	--maxCN=MAXCN
		Total clonal CN states. Default: [7]

	--estimateNormal=ESTIMATENORMAL
		Estimate normal. Default: [TRUE]

	--estimateScPrevalence=ESTIMATESCPREVALENCE
		Estimate subclonal prevalence. Default: [TRUE]

	--estimatePloidy=ESTIMATEPLOIDY
		Estimate tumour ploidy. Default: [TRUE]

	--maxFracCNASubclone=MAXFRACCNASUBCLONE
		Exclude solutions with fraction of subclonal events greater than this value. Default: [0.7]

	--maxFracGenomeSubclone=MAXFRACGENOMESUBCLONE
		Exclude solutions with subclonal genome fraction greater than this value. Default: [0.5]

	--minSegmentBins=MINSEGMENTBINS
		Minimum number of bins for largest segment threshold required to estimate tumor fraction; if below this threshold, then will be assigned zero tumor fraction.

	--altFracThreshold=ALTFRACTHRESHOLD
		Minimum proportion of bins altered required to estimate tumor fraction; if below this threshold, then will be assigned zero tumor fraction. Default: [0.05]

	--chrNormalize=CHRNORMALIZE
		Specify chromosomes to normalize GC/mappability biases. Default: [c(1:22)]

	--chrTrain=CHRTRAIN
		Specify chromosomes to estimate params. Default: [c(1:22)]

	--chrs=CHRS
		Specify chromosomes to analyze. Default: [c(1:22,"X")]

	--normalizeMaleX=NORMALIZEMALEX
		If male, then normalize chrX by median. Default: [TRUE]

	--fracReadsInChrYForMale=FRACREADSINCHRYFORMALE
		Threshold for fraction of reads in chrY to assign as male. Default: [0.001]

	--includeHOMD=INCLUDEHOMD
		If FALSE, then exclude HOMD state. Useful when using large bins (e.g. 1Mb). Default: [FALSE]

	--txnE=TXNE
		Self-transition probability. Increase to decrease number of segments. Default: [0.9999999]

	--txnStrength=TXNSTRENGTH
		Transition pseudo-counts. Exponent should be the same as the number of decimal places of --txnE. Default: [1e+07]

	--plotFileType=PLOTFILETYPE
		File format for output plots. Default: [pdf]

	--plotYLim=PLOTYLIM
		ylim to use for chromosome plots. Default: [c(-2,2)]

	--outDir=OUTDIR
		Output Directory. Default: [./]

	--libdir=LIBDIR
		Script library path. Usually exclude this argument unless custom modifications have been made to the ichorCNA R package code and the user would like to source those R files. Default: [NULL]
```

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

### Create WIG file for PoN sample

Source: https://github.com/broadinstitute/ichorcna/wiki/Create-Panel-of-Normals

Generates a WIG file from a BAM file using HMMcopy readCounter for use in a Panel of Normals.

```bash
/path/to/HMMcopy/bin/readCounter --window 1000000 --quality 20 \
    --chromosome "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y" \
    /path/to/tumor.bam > /path/to/tumor.wig
```

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

### View ichorCNA help menu

Source: https://github.com/broadinstitute/ichorcna/blob/master/scripts/README.md

Command to display the usage instructions and available command-line arguments for the ichorCNA script.

```bash
>Rscript runIchorCNA.R --help

Usage: runIchorCNA.R [options]


Options:
        -l LIBDIR, --libdir=LIBDIR
                Script library path

        --datadir=DATADIR
                Reference wig dir path

        -t WIG, --WIG=WIG
                Path to tumor WIG file.

        --NORMWIG=NORMWIG
                Path to normal WIG file.

        --normalPanel=NORMALPANEL
                Median corrected depth from panel of normals

        -e EXONS.BED, --exons.bed=EXONS.BED
                Path to bed file containing exon regions.

        --id=ID
                Patient ID.


        -n NORMAL, --normal=NORMAL
                Initial normal contamination

        --scStates=SCSTATES
                Subclonal states to consider

        -p PLOIDY, --ploidy=PLOIDY
                Initial tumour ploidy

        -m MAXCN, --maxCN=MAXCN
                Total clonal CN states

        --estimateNormal=ESTIMATENORMAL
                Estimate normal.

        --estimateScPrevalence=ESTIMATESCPREVALENCE
                Estimate subclonal prevalence.

        --estimatePloidy=ESTIMATEPLOIDY
                Estimate tumour ploidy.

 (plus more arguments)
```

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

### Set Initial Ploidy to Diploid for Low Tumor Content

Source: https://github.com/broadinstitute/ichorcna/wiki/Parameter-tuning-and-settings

For low tumor fraction cases, it can be challenging to predict the ploidy value, so initializing to diploid is recommended.

```bash
--ploidy "c(2)"
```

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

### Create Panel of Normals (PoN)

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Generates a custom PoN from healthy donor cfDNA samples. Requires a file list of WIG files and corresponding GC/map/centromere reference files.

```bash
# Create a file listing all normal WIG files (one per line)
cat > normal_wigs.txt << EOF
/path/to/normal1.wig
/path/to/normal2.wig
/path/to/normal3.wig
/path/to/normal4.wig
/path/to/normal5.wig
EOF

# Generate Panel of Normals for hg19
Rscript /path/to/ichorCNA/scripts/createPanelOfNormals.R \
    --filelist normal_wigs.txt \
    --gcWig /path/to/ichorCNA/inst/extdata/gc_hg19_1000kb.wig \
    --mapWig /path/to/ichorCNA/inst/extdata/map_hg19_1000kb.wig \
    --centromere /path/to/ichorCNA/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt \
    --outfile my_custom_PoN

# Generate Panel of Normals for hg38
Rscript /path/to/ichorCNA/scripts/createPanelOfNormals.R \
    --filelist normal_wigs.txt \
    --gcWig /path/to/ichorCNA/inst/extdata/gc_hg38_1000kb.wig \
    --mapWig /path/to/ichorCNA/inst/extdata/map_hg38_1000kb.wig \
    --centromere /path/to/ichorCNA/inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt \
    --genomeStyle UCSC \
    --outfile my_custom_PoN_hg38
```

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

### Configure GC and mappability wig files

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Required wig files for GC content and mappability, which must match the configured bin size.

```yaml
# must use gc wig file corresponding to same binSize (required)
ichorCNA_gcWig: ../../inst/extdata/gc_hg19_1000kb.wig
# must use map wig file corresponding to same binSize (required)
ichorCNA_mapWig:  ../../inst/extdata/map_hg19_1000kb.wig
```

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

### Clone ichorCNA repository

Source: https://github.com/broadinstitute/ichorcna/wiki/Installation

Manual step to retrieve the source code from GitHub.

```bash
git clone git@github.com:broadinstitute/ichorCNA.git
```

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

### Initialize HMM Parameters

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Configures default HMM parameters for segmentation. Use this to define copy number states and transition priors before running HMMsegment.

```r
library(ichorCNA)

# Load your log ratio data
logR <- as.data.frame(tumour_copy$copy)
chrInd <- as.character(seqnames(tumour_copy)) %in% c(1:22)
valid <- tumour_copy$valid

# Get default parameters with standard settings
param <- getDefaultParameters(logR[valid & chrInd, , drop=FALSE],
                              maxCN = 5,           # Maximum copy number state
                              ct.sc = c(1, 3),     # Subclonal states
                              ploidy = 2,          # Initial ploidy
                              e = 0.9999999,       # Self-transition probability
                              e.sameState = 10,    # Transition modifier for same states
                              strength = 10000000, # Transition pseudo-counts
                              includeHOMD = FALSE) # Exclude homozygous deletion

# Customize parameters for specific use cases
```

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

### Run Snakemake Workflow on Cluster

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Submits the Snakemake workflow to a cluster using qsub, with a maximum of 50 concurrent jobs.

```bash
# Run on cluster with qsub (max 50 jobs)
snakemake -s ichorCNA.snakefile --cluster-sync "qsub" -j 50 --jobscript config/cluster.sh
```

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

### Configure Low Tumor Fraction Settings

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Adjust parameters for samples with less than 5% tumor fraction to improve estimation accuracy.

```bash
# Settings optimized for low tumor fraction samples
Rscript /path/to/ichorCNA/scripts/runIchorCNA.R \
    --id low_tf_sample \
    --WIG /path/to/tumor.wig \
    --gcWig /path/to/ichorCNA/inst/extdata/gc_hg19_1000kb.wig \
    --mapWig /path/to/ichorCNA/inst/extdata/map_hg19_1000kb.wig \
    --centromere /path/to/ichorCNA/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt \
    --normalPanel /path/to/ichorCNA/inst/extdata/HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds \
    --normal "c(0.95, 0.99, 0.995, 0.999)" \
    --ploidy "c(2)" \
    --maxCN 3 \
    --estimateScPrevalence FALSE \
    --scStates "c()" \
    --chrs "c(1:22)" \
    --chrTrain "c(1:22)" \
    --outDir ./results/
```

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

### Preview Snakemake Workflow

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Performs a dry run of the Snakemake workflow to preview the execution plan without actually running any jobs.

```bash
# Preview workflow (dry run)
cd /path/to/ichorCNA/scripts/snakemake
snakemake -s ichorCNA.snakefile -np
```

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

### Set chromosomes and bin size

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Defines the chromosomes to analyze and the window size for coverage computation.

```yaml
chrs:
  1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y
binSize:  1000000 # set window size to compute coverage
```

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

### Generate Panel of Normals

Source: https://github.com/broadinstitute/ichorcna/wiki/Create-Panel-of-Normals

Executes the createPanelOfNormals.R script to compile a PoN from a list of WIG files.

```bash
Rscript createPanelOfNormals.R 
    --filelist /path/to/wig_files.txt 
    --gcWig /path/to/gc.wig --mapWig /path/to/map.wig 
    --centromere /path/to/centromeres_file.txt 
    --outfile base_outfile_name
```

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

### Run ichorCNA Script

Source: https://github.com/broadinstitute/ichorcna/wiki/Usage

This section details how to run the ichorCNA analysis using the provided R script and explains key command-line arguments.

```APIDOC
## Run ichorCNA

This section describes how to manually run ichorCNA using the `runIchorCNA.R` script.

### Method
Command Line Execution

### Endpoint
`/path/to/ichorCNA/scripts/runIchorCNA.R`

### Parameters

#### Command-line Arguments

- **--id** (string) - Required - Patient ID. Default: [test]
- **--WIG** (string) - Required - Path to tumor WIG file.
- **--NORMWIG** (string) - Optional - Path to normal WIG file. Default: [NULL]
- **--gcWig** (string) - Required - Path to GC-content WIG file.
- **--mapWig** (string) - Optional - Path to mappability score WIG file. Default: [NULL]
- **--normalPanel** (string) - Optional - Median corrected depth from panel of normals. Default: [NULL]
- **--exons.bed** (string) - Optional - Path to bed file containing exon regions. Default: [NULL]
- **--centromere** (string) - Optional - File containing Centromere locations; if not provided then will use hg19 version from ichorCNA package. Default: [NULL]
- **--rmCentromereFlankLength** (numeric) - Optional - Length of region flanking centromere to remove. Default: [1e+05]
- **--normal** (numeric or vector) - Optional - Initial normal contamination; can be more than one value if additional normal initializations are desired. Default: [0.5]
- **--scStates** (vector) - Optional - Subclonal states to consider. Default: [NULL]
- **--coverage** (numeric) - Optional - PICARD sequencing coverage. Default: [NULL]
- **--lambda** (vector) - Optional - Initial Student's t precision; must contain 4 values (e.g. c(1500,1500,1500,1500)); if not provided then will automatically use based on variance of data. Default: [NULL]
- **--lambdaScaleHyperParam** (numeric) - Optional - Hyperparameter (scale) for Gamma prior on Student's-t precision. Default: [3]
- **--ploidy** (numeric or vector) - Optional - Initial tumour ploidy; can be more than one value if additional ploidy initializations are desired. Default: [2]
- **--maxCN** (numeric) - Optional - Total clonal CN states. Default: [7]
- **--estimateNormal** (boolean) - Optional - Estimate normal. Default: [TRUE]
- **--estimateScPrevalence** (boolean) - Optional - Estimate subclonal prevalence. Default: [TRUE]
- **--estimatePloidy** (boolean) - Optional - Estimate tumour ploidy. Default: [TRUE]
- **--maxFracCNASubclone** (numeric) - Optional - Exclude solutions with fraction of subclonal events greater than this value. Default: [0.7]
- **--maxFracGenomeSubclone** (numeric) - Optional - Exclude solutions with subclonal genome fraction greater than this value. Default: [0.5]
- **--minSegmentBins** (numeric) - Optional - Minimum number of bins for largest segment threshold required to estimate tumor fraction; if below this threshold, then will be assigned zero tumor fraction.
- **--altFracThreshold** (numeric) - Optional - Minimum proportion of bins altered required to estimate tumor fraction; if below this threshold, then will be assigned zero tumor fraction. Default: [0.05]
- **--chrNormalize** (vector) - Optional - Specify chromosomes to normalize GC/mappability biases. Default: [c(1:22)]
- **--chrTrain** (vector) - Optional - Specify chromosomes to estimate params. Default: [c(1:22)]
- **--chrs** (vector) - Optional - Specify chromosomes to analyze. Default: [c(1:22, "X")]
- **--normalizeMaleX** (boolean) - Optional - If male, then normalize chrX by median. Default: [TRUE]
- **--fracReadsInChrYForMale** (numeric) - Optional - Threshold for fraction of reads in chrY to assign as male. Default: [0.001]
- **--includeHOMD** (boolean) - Optional - If FALSE, then exclude HOMD state. Useful when using large bins (e.g. 1Mb). Default: [FALSE]
- **--txnE** (numeric) - Optional - Self-transition probability. Increase to decrease number of segments. Default: [0.9999999]
- **--txnStrength** (numeric) - Optional - Transition pseudo-counts. Exponent should be the same as the number of decimal places of --txnE. Default: [1e+07]
- **--plotFileType** (string) - Optional - File format for output plots. Default: [pdf]
- **--plotYLim** (vector) - Optional - ylim to use for chromosome plots. Default: [c(-2,2)]
- **--outDir** (string) - Optional - Output Directory. Default: [./]
- **--libdir** (string) - Optional - Script library path. Usually exclude this argument unless custom modifications have been made to the ichorCNA R package code and the user would like to source those R files. Default: [NULL]

### Request Example
```bash
Rscript /path/to/ichorCNA/scripts/runIchorCNA.R --id tumor_sample \
  --WIG /path/to/tumor.wig --ploidy "c(2,3)" --normal "c(0.5,0.6,0.7,0.8,0.9)" --maxCN 5 \
  --gcWig /path/to/ichorCNA/inst/extdata/gc_hg19_1000kb.wig \
  --mapWig /path/to/ichorCNA/inst/extdata/map_hg19_1000kb.wig \
  --centromere /path/to/ichorCNA/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt \
  --normalPanel /path/to/ichorCNA/inst/extdata/HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds \
  --includeHOMD False --chrs "c(1:22, \"X\")" --chrTrain "c(1:22)" \
  --estimateNormal True --estimatePloidy True --estimateScPrevalence True \
  --scStates "c(1,3)" --txnE 0.9999 --txnStrength 10000 --outDir ./ 
```

### Help

To view all available options, run the script with the `--help` flag:

```bash
Rscript runIchorCNA.R --help
```
```

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

### Run Snakemake Workflow Locally

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Executes the Snakemake workflow locally, utilizing up to 5 CPU cores.

```bash
# Run locally with 5 cores
snakemake -s ichorCNA.snakefile --cores 5
```

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

### Set Initial Parameters for ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Sets initial values for tumor ploidy, normal contamination, and subclone prevalence.

```r
# Set initial values
param$phi_0 <- 2       # Initial tumor ploidy
param$n_0 <- 0.5       # Initial normal contamination (1 - tumor fraction)
param$sp_0 <- 0.5      # Initial subclone prevalence
```

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

### Configure ichorCNA model parameters

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Sets parameters for normal fraction, ploidy, and estimation flags.

```yaml
ichorCNA_chrs:  c(1:22, "X")
# chrs used for training ichorCNA parameters, e.g. tumor fraction.
ichorCNA_chrTrain:  c(1:22)
# non-tumor fraction parameter restart values; higher values should be included for cfDNA
ichorCNA_normal:  c(0.5,0.6,0.7,0.8,0.9,0.95)
# ploidy parameter restart values
ichorCNA_ploidy:  c(2,3)
ichorCNA_estimateNormal:  TRUE
ichorCNA_estimatePloidy:  TRUE
```

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

### Generate Read Count WIG File

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Create WIG files from indexed BAM files using HMMcopy's readCounter utility.

```bash
# Generate WIG file with 1Mb bins from a BAM file
# BAM file must be indexed (.bam.bai in same directory)
/path/to/HMMcopy/bin/readCounter --window 1000000 --quality 20 \
    --chromosome "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y" \
    /path/to/tumor.bam > /path/to/tumor.wig

# For 500kb bins (higher resolution, requires more coverage)
/path/to/HMMcopy/bin/readCounter --window 500000 --quality 20 \
    --chromosome "1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y" \
    /path/to/tumor.bam > /path/to/tumor.500kb.wig
```

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

### Run ichorCNA Analysis

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Execute the main analysis script to process WIG files and estimate tumor fraction.

```bash
# Basic ichorCNA analysis with essential parameters
Rscript /path/to/ichorCNA/scripts/runIchorCNA.R \
    --id tumor_sample \
    --WIG /path/to/tumor.wig \
    --gcWig /path/to/ichorCNA/inst/extdata/gc_hg19_1000kb.wig \
    --mapWig /path/to/ichorCNA/inst/extdata/map_hg19_1000kb.wig \
    --centromere /path/to/ichorCNA/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt \
    --normalPanel /path/to/ichorCNA/inst/extdata/HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds \
    --ploidy "c(2,3)" \
    --normal "c(0.5,0.6,0.7,0.8,0.9)" \
    --maxCN 5 \
    --includeHOMD False \
    --chrs "c(1:22, \"X\")" \
    --chrTrain "c(1:22)" \
    --estimateNormal True \
    --estimatePloidy True \
    --estimateScPrevalence True \
    --scStates "c(1,3)" \
    --txnE 0.9999 \
    --txnStrength 10000 \
    --outDir ./results/

# For hg38 reference genome
Rscript /path/to/ichorCNA/scripts/runIchorCNA.R \
    --id tumor_sample_hg38 \
    --WIG /path/to/tumor.wig \
    --gcWig /path/to/ichorCNA/inst/extdata/gc_hg38_1000kb.wig \
    --mapWig /path/to/ichorCNA/inst/extdata/map_hg38_1000kb.wig \
    --centromere /path/to/ichorCNA/inst/extdata/GRCh38.GCA_000001405.2_centromere_acen.txt \
    --normalPanel /path/to/ichorCNA/inst/extdata/HD_ULP_PoN_hg38_1Mb_median_normAutosome_median.rds \
    --genomeBuild hg38 \
    --genomeStyle UCSC \
    --ploidy "c(2,3)" \
    --normal "c(0.5,0.6,0.7,0.8,0.9)" \
    --maxCN 5 \
    --outDir ./results/

# View all available options
Rscript /path/to/ichorCNA/scripts/runIchorCNA.R --help
```

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

### View ichorCNA Parameters

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Display the contents of the parameters file generated by ichorCNA. This file contains estimated tumor fraction, ploidy, and other relevant metrics.

```bash
cat results/tumor_sample.params.txt
```

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

### Configure ichorCNA script and normal panel paths

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Specifies the path to the main R script and the optional normal panel for data normalization.

```yaml
# included in GitHub repo
ichorCNA_rscript:  ../runIchorCNA.R
# use panel matching same bin size (optional)
ichorCNA_normalPanel: ../../inst/extdata/HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds
```

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

### Configure targeted intervals and centromeres

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Optional configuration for targeted regions like exons and centromere locations.

```yaml
# use bed file if sample has targeted regions, eg. exome data (optional)
ichorCNA_exons:  NULL
ichorCNA_centromere:  ../../inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt
```

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

### Define Samples for Snakemake Workflow

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Defines the samples to be processed by the ichorCNA Snakemake workflow, mapping sample IDs to BAM file paths.

```yaml
# config/samples.yaml - Define samples
samples:
  tumor_sample_1: /path/to/bam/tumor1.bam
  tumor_sample_2: /path/to/bam/tumor2.bam
  tumor_sample_3: /path/to/bam/tumor3.bam
```

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

### Configure segmentation sensitivity

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Sets parameters for segmentation sensitivity and specificity.

```yaml
# higher (e.g. 0.9999999) leads to higher specificity and fewer segments
# lower (e.g. 0.99) leads to higher sensitivity and more segments
ichorCNA_txnE:  0.9999
# higher (e.g. 10000000) leads to higher specificity and fewer segments
```

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

### Execute ichorCNA via Command Line

Source: https://github.com/broadinstitute/ichorcna/wiki/Usage

Use the runIchorCNA.R script to process tumor samples. Ensure all required paths for WIG, GC, mappability, and centromere files are correctly specified.

```bash
Rscript /path/to/ichorCNA/scripts/runIchorCNA.R --id tumor_sample \
  --WIG /path/to/tumor.wig --ploidy "c(2,3)" --normal "c(0.5,0.6,0.7,0.8,0.9)" --maxCN 5 \
  --gcWig /path/to/ichorCNA/inst/extdata/gc_hg19_1000kb.wig \
  --mapWig /path/to/ichorCNA/inst/extdata/map_hg19_1000kb.wig \
  --centromere /path/to/ichorCNA/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt \
  --normalPanel /path/to/ichorCNA/inst/extdata/HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds \
  --includeHOMD False --chrs "c(1:22, \"X\")" --chrTrain "c(1:22)" \
  --estimateNormal True --estimatePloidy True --estimateScPrevalence True \
  --scStates "c(1,3)" --txnE 0.9999 --txnStrength 10000 --outDir ./
```

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

### Configure Snakemake Pipeline for ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Configures the ichorCNA pipeline for Snakemake, specifying paths to scripts, reference data, and analysis parameters.

```yaml
# config/config.yaml - Pipeline configuration
readCounterScript: /path/to/HMMcopy/bin/readCounter
chrs: 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,X,Y
binSize: 1000000

ichorCNA_rscript: /path/to/ichorCNA/scripts/runIchorCNA.R
ichorCNA_normalPanel: /path/to/ichorCNA/inst/extdata/HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds
ichorCNA_gcWig: /path/to/ichorCNA/inst/extdata/gc_hg19_1000kb.wig
ichorCNA_mapWig: /path/to/ichorCNA/inst/extdata/map_hg19_1000kb.wig
ichorCNA_centromere: /path/to/ichorCNA/inst/extdata/GRCh37.p13_centromere_UCSC-gapTable.txt
ichorCNA_exons: NULL

ichorCNA_chrs: c(1:22, "X")
ichorCNA_chrTrain: c(1:22)
ichorCNA_normal: c(0.5,0.6,0.7,0.8,0.9,0.95)
ichorCNA_ploidy: c(2,3)
ichorCNA_maxCN: 5
ichorCNA_includeHOMD: FALSE
ichorCNA_estimateNormal: TRUE
ichorCNA_estimatePloidy: TRUE
ichorCNA_estimateClonality: TRUE
ichorCNA_scStates: c(1,3)
ichorCNA_txnE: 0.9999
ichorCNA_txnStrength: 10000
ichorCNA_genomeBuild: hg19
ichorCNA_genomeStyle: NCBI
ichorCNA_plotFileType: pdf
ichorCNA_plotYlim: c(-2,2)
```

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

### Invoke Snakemake workflow

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Commands to run the ichorCNA Snakemake workflow locally or on a cluster.

```bash
# show commands and workflow
snakemake -s ichorCNA.snakefile -np
```

```bash
# run the workflow locally using 5 cores
snakemake -s ichorCNA.snakefile --cores 5
```

```bash
# run the workflow on qsub using a maximum of 50 jobs. 
# Broad UGER cluster parameters can be set directly in config/cluster.sh. 
snakemake -s ichorCNA.snakefile --cluster-sync "qsub" -j 50 --jobscript config/cluster.sh
```

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

### Run ichorCNA on SLURM Cluster

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Execute the ichorCNA workflow on a SLURM cluster using Snakemake. Specify the Snakefile, cluster type, number of jobs, and cluster configuration file.

```bash
snakemake -s ichorCNA.snakefile --cluster "sbatch" -j 50 --cluster-config config/cluster_slurm.yaml
```

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

### View Segment File Header

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Display the first few lines of the segment file generated by ichorCNA. This file contains segmented copy number information compatible with IGV.

```bash
head results/tumor_sample.seg.txt
```

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

### Output Parameters to File with ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Writes the parameters used in the ichorCNA analysis to a specified file.

```r
# Output parameters to file
outputParametersToFile(hmmResults, file = "./results/tumor_sample.params.txt")
```

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

### Define cfDNA samples in YAML

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

The samples field must be defined in the YAML configuration file to map sample names to their respective BAM files.

```yaml
samples:
  tumor_sample_1:  /path/to/bam/tumor.bam
```

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

### Configure ichorCNA genome style

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Sets the chromosome naming style for the output.

```yaml
ichorCNA_genomeStyle: UCSC # sets output chromosome naming style
```

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

### Train and Analyze Autosomes Only

Source: https://github.com/broadinstitute/ichorcna/wiki/Parameter-tuning-and-settings

Exclude the X chromosome from analysis and training to simplify the process and reduce complexity for low tumor fraction samples.

```bash
--chrs "c(1:22)" --chrTrain "c(1:22)"
```

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

### Extract Purity, Ploidy, and Cell Prevalence from HMM Results

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Extracts estimated purity, ploidy, and cell prevalence from the results of HMMsegment.

```r
iter <- hmmResults$results$iter
purity <- 1 - hmmResults$results$n[1, iter]
ploidy <- hmmResults$results$phi[1, iter]
cellPrev <- 1 - hmmResults$results$sp[1, iter]
```

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

### Perform HMM Segmentation

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Executes HMM-based segmentation to predict copy number states. Requires pre-processed read counts and initialized HMM parameters.

```r
library(ichorCNA)
library(HMMcopy)
library(GenomicRanges)

# Load and process WIG file
tumour_reads <- wigToGRanges("/path/to/tumor.wig")
gc <- wigToGRanges("/path/to/gc_hg19_1000kb.wig")
map <- wigToGRanges("/path/to/map_hg19_1000kb.wig")

# Define chromosomes and load centromere file
chrs <- c(as.character(1:22), "X")
centromere <- read.delim("/path/to/GRCh37.p13_centromere_UCSC-gapTable.txt",
                         header=TRUE, stringsAsFactors=FALSE, sep="\t")

# Load and correct read counts
counts <- loadReadCountsFromWig(tumour_reads, chrs=chrs, gc=gc, map=map,
                                centromere=centromere, flankLength=100000,
                                genomeStyle="NCBI", chrNormalize=c(1:22))
tumour_copy <- counts$counts
gender <- counts$gender

# Normalize by panel of normals (optional)
normal_panel <- "/path/to/HD_ULP_PoN_1Mb_median_normAutosome_mapScoreFiltered_median.rds"
tumour_copy <- normalizeByPanelOrMatchedNormal(tumour_copy, chrs=chrs,
                                                normal_panel=normal_panel,
                                                gender=gender$gender)

# Set up HMM parameters
logR <- as.data.frame(tumour_copy$copy)
chrTrain <- c(1:22)
chrInd <- as.character(seqnames(tumour_copy)) %in% chrTrain
valid <- tumour_copy$valid

param <- getDefaultParameters(logR[valid & chrInd, , drop=FALSE],
                              maxCN=5, includeHOMD=FALSE,
                              ct.sc=c(1,3), ploidy=2,
                              e=0.9999, strength=10000)
param$phi_0 <- 2  # Initial ploidy
param$n_0 <- 0.5  # Initial normal fraction

# Run HMM segmentation
hmmResults <- HMMsegment(list(sample=tumour_copy), valid, dataType="copy",
                         param=param, chrTrain=chrTrain, maxiter=50,
                         estimateNormal=TRUE, estimatePloidy=TRUE,
                         estimateSubclone=TRUE, verbose=TRUE)

# Access results
tumor_fraction <- 1 - hmmResults$results$n[, hmmResults$results$iter]
ploidy <- hmmResults$results$phi[, hmmResults$results$iter]
segments <- hmmResults$results$segs[[1]]
copy_number_bins <- hmmResults$cna[[1]]

print(paste("Tumor Fraction:", round(tumor_fraction, 4)))
print(paste("Ploidy:", round(ploidy, 2)))
```

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

### Configure subclonal copy number states

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Defines subclonal states and enables clonality estimation.

```yaml
# states to use for subclonal CN
ichorCNA_scStates:  c(1,3)
ichorCNA_estimateClonality: TRUE
```

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

### Configure copy number limits and homozygous deletion

Source: https://github.com/broadinstitute/ichorcna/wiki/SnakeMake-pipeline-for-ichorCNA

Sets the maximum copy number and toggles the inclusion of homozygous deletion states.

```yaml
# set maximum copy number to use
ichorCNA_maxCN:  5
# TRUE/FALSE to include homozygous deletion state
ichorCNA_includeHOMD: FALSE
```

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

### Set Default Parameters for ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Sets default parameters for ichorCNA, with options for higher specificity (fewer segments) or higher sensitivity (more segments).

```r
param_high_spec <- getDefaultParameters(logR[valid & chrInd, , drop=FALSE],
                                        maxCN = 5,
                                        e = 0.99999999,    # Higher self-transition
                                        strength = 100000000)

# Higher sensitivity (more segments):
param_high_sens <- getDefaultParameters(logR[valid & chrInd, , drop=FALSE],
                                        maxCN = 5,
                                        e = 0.999,         # Lower self-transition
                                        strength = 1000)
```

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

### Reduce Number of Copy Number States

Source: https://github.com/broadinstitute/ichorcna/wiki/Parameter-tuning-and-settings

Lowering the maximum number of copy number states (e.g., to 3) helps reduce complexity. Increase to 4 if high-level copy number events are known from prior samples.

```bash
--maxCN 3
```

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

### Access Corrected Results from ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Accesses the corrected segments and bin-level copy number data after running `correctIntegerCN`.

```r
# Access corrected results
corrected_segments <- correctedResults$segs
corrected_bins <- correctedResults$cn
```

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

### Disable Subclonal Copy Number Event Estimation

Source: https://github.com/broadinstitute/ichorcna/wiki/Parameter-tuning-and-settings

Turn off subclonal event detection for low tumor fraction samples, as these are difficult to detect. Ensure the subclonal states list is empty.

```bash
--estimateScPrevalence FALSE --scStates "c()"
```

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

### Access ichorCNA Parameter Information

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Accesses and prints various parameter details from an ichorCNA parameter object.

```r
# Access parameter information
print(param$ct)                  # Copy number states
print(param$ct.sc.status)        # Subclonal status for each state
print(param$lambda)              # Student's t precision
print(param$jointCNstates)       # Joint copy number states
```

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

### Output HMM Results with ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Writes HMM results, including segments and bin-level data, to output files. This function is used after running `HMMsegment` and `correctIntegerCN`.

```r
library(ichorCNA)

# After running HMMsegment and correctIntegerCN
# Output results to files
outputHMM(
    cna = hmmResults$cna,
    segs = hmmResults$results$segs,
    results = hmmResults$results,
    patientID = "tumor_sample",
    outDir = "./results/"
)
```

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

### View Corrected Copy Number Calls

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Prints the head of the corrected segments data frame to display copy number calls, including corrected values.

```r
# View corrected calls
print(head(corrected_segments[, c("chr", "start", "end", "copy.number",
                                   "Corrected_Copy_Number", "Corrected_Call")]))
```

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

### Correct Integer Copy Number with ichorCNA

Source: https://context7.com/broadinstitute/ichorcna/llms.txt

Corrects high-level amplifications in integer copy numbers using estimated purity and ploidy. This function is typically run after HMMsegment.

```r
# Correct integer copy number
correctedResults <- correctIntegerCN(
    cn = hmmResults$cna[[1]],
    segs = hmmResults$results$segs[[1]],
    purity = purity,
    ploidy = ploidy,
    cellPrev = cellPrev,
    maxCNtoCorrect.autosomes = 5,
    maxCNtoCorrect.X = 5,
    minPurityToCorrect = 0.1,
    gender = gender$gender,
    chrs = c(1:22, "X"),
    correctHOMD = FALSE
)
```

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