[{"_1":2,"_172":-5,"_173":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_151":152},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":11},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":85,"_104":105,"_142":-5,"_143":11,"_144":17,"_145":11,"_146":-5,"_147":11,"_148":-5,"_149":150},"canonicalUrl","https://hub.docker.com/r/fjdceph/acnviewer/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","fjdceph","reponame","acnviewer","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":17,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2017-04-09T21:24:36.31271Z","description","Comprehensive genome-wide visualization of absolute copy number and copy neutral variations","is_private","last_updated","2019-04-10T17:11:42.023477Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",1479,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","# aCNViewer\ncomprehensive genome-wide visualization of absolute copy number and copy neutral variations\n\n**Contact:** Victor Renault / Alexandre How-Kit (aCNViewer@cephb.fr)\n\naCNViewer (Absolute CNV Viewer) is a tool which allows the visualization of absolute CNVs and cn-LOH across a group of cancer samples. aCNViewer proposes three graphical representations : dendrograms, bidimensional heatmaps allowing the visualization of chromosomal regions sharing similar abnormality patterns and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. aCNViewer include a complete pipeline allowing the processing of raw data from SNP array (in tumor-only or paired tumor / normal mode) and whole exome/genome sequencing experiments (in paired tumor / normal mode only) using respectively ASCAT and Sequenza algorithms to generate absolute CNV and cn-LOH data used for the graphical outputs.\n\n\n## Table of contents\n- [Installation](#installation)\n * [from Docker](#docker-installation)\n * [from source](#installation-from-source)\n * [installation validation](#installation-validation)\n- [Overview](#overview)\n- [Tutorial](#tutorial):\n * [Glossary](#glossary)\n * Processing SNP array data:\n + [Affymetrix](#affymetrix)\n - [Test using Ascat results](#testaffyascat) (**start here if you want an overview of all the plotting options and different usage scenarios**):\n * [all plots](#allPlots)\n * [use custom colors for plots](#customColors)\n * [GISTIC example](#gisticExample)\n * [heatmap using relative copy number values](#heatmapRel)\n * [heatmap with CNVs ordered by genomic position](heatmapGenPos)\n * [heatmap using copy number values](#heatmapCNV)\n * [dendrogram example](#dendroExample)\n * [custom graphical output formats](#outputFormatExamples)\n - [Test using Affymetrix Cel files](#testaffycel)\n + [Illumina](#illumina)\n * [Processing NGS data](#ngs)\n + [from paired (tumor / normal) bams](#testsequenzaraw)\n + [from Sequenza segment files](#testsequenzacnvs)\n * [Processing CNV file](#processing-cnv-files)\n + [from ASCAT](#testaffyascat)\n + [from PennCNV](#penncnv)\n + [from Sequenza](#testsequenzacnvs)\n + [from other tools](#othercnvformats)\n- [Output files](#outputfiles)\n * [ASCAT](#ascat)\n * [GISTIC](#gistic-outputs)\n * [Sequenza](#sequenza)\n * [Histogram](#histogramoutputs)\n * [Dendrograms and heatmaps](#dendrograms-and-heatmaps)\n- [Limitations](#limitations)\n- [Citation](#citation)\n\n***\n\n## Installation\n\n### Docker installation\n\nThe easiest way to install aCNViewer is to install the [Docker application](https://hub.docker.com/r/fjdceph/acnviewer/) (supports multi-threading but not computer clusters which are better suited for processing NGS bams):\n`docker pull fjdceph/acnviewer`\n\naCNViewer docker image requires about 20GB of space to install so if you run into an error while pulling the image locally, you probably need to [change the location of docker images from /var/lib/docker/ to a location with more space](https://linuxconfig.org/how-to-move-docker-s-default-var-lib-docker-to-another-directory-on-ubuntu-debian-linux) and try again.\n\n\n### Installation from source\n\naCNViewer can also be installed from its source by:\n1. downloading aCNViewer's data (includes test data sets and most of the third-party softwares listed in the [dependencies](#dependencies) section)\n1. installing the dependencies listed [below](#dependencies).\n1. downloading the github source code from this page:\n`git clone https://github.com/FJD-CEPH/aCNViewer`\n\n\n### Installation validation\n\nOnce aCNViewer is installed, you can run [unit tests](#unitTests) in order to check that everything is fine.\n\n\n#### Dependencies:\n\nMost of the dependencies (except R and python), along with test data sets, are packaged in the archive [aCNViewer_DATA.tar.gz](https://www.cng.fr/genodata/pub/LIVER/aCNViewer_DATA.tar.gz) in `aCNViewer_DATA/bin`. You can find more details below:\n\n* APT ([Affymetrix Power Tools](http://www.affymetrix.com/estore/partners_programs/programs/developer/tools/powertools.affx#1_2)) if you plan to process raw Affymetrix SNP arrays (to uncompress into `BIN_DIR`)\n\n* a recent version of R (version ≥ 3.2) with [ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html) installed for generating the different graphical outputs:\n + [ASCAT](https://www.crick.ac.uk/peter-van-loo/software/ASCAT) (will be automatically installed if not already installed) if you are analyzing raw SNP array data\n + [Sequenza](https://cran.r-project.org/web/packages/sequenza/index.html) (will be automatically installed if not already installed) if you are analyzing paired (tumor / normal) bams\n + [plotrix](https://cran.r-project.org/web/packages/plotrix/index.html) for plotting dendrograms (will be automatically installed if not already installed)\n + [gplots](https://cran.r-project.org/web/packages/gplots/index.html)\n + [RColorBrewer](https://cran.r-project.org/web/packages/RColorBrewer/index.html)\n\n* samtools if you are analyzing paired (tumor / normal) bams. As Sequenza does not support newer mpileup file formats produced by more recent versions of samtools, use a version prior to Sequenza release date (2015-10-10): samtools version [0.1.19](https://sourceforge.net/projects/samtools/files/samtools/0.1.19/samtools-0.1.19.tar.bz2/download) for example is compatible.\n\n* [tQN](http://cbbp.thep.lu.se/~markus/software/tQN/tQN-1.1.2.zip) if you plan to process raw Illumina SNP arrays (to uncompress into `BIN_DIR`) and run tQN normalisation. If the cluster file for the Illumina SNP array you plan to analyze is not in the tQN lib folder, you can download additional cluster files from [here](http://cbbp.thep.lu.se/~markus/software/tQN/)\n\n* [GISTIC](http://portals.broadinstitute.org/cgi-bin/cancer/publications/pub_paper.cgi?mode=view&paper_id=216&p=t) if you want to have an advanced statistical way to prioritize regions of interest. Create a folder named `GISTIC_VERSION` in `BIN_DIR` and uncompress the GISTIC archive into it. Follow the instructions listed in `INSTALL.txt` at the root of the GISTIC folder in order to install MATLAB Component Runtime required by GISTIC and set the associated environment variables (`LD_LIBRARY_PATH` and `XAPPLRESDIR`).\n\n* Python with version ≥ 2.7\n\n\n## Overview:\n![Overview of aCNViewer:](/img/aCNViewer.png?raw=true \"Overview of aCNViewer\") \n\n\n\n***\n\n\n## Tutorial\n\nThe results of all the examples below can be found in `aCNViewer_DATA/allTests` in their respective target folder. All examples of this tutorial are implemented as unit tests and can be run at once using: `DOCKER_OR_PYTHON` `-P testAll -t TARGET_DIR [--fastTest 0 --smallMem 0 --runGISTIC 0]`. \n\nIf `--fastTest` is set to `1`, only tests which run in a *reasonable* amount of time will be run (all tests except [Illumina SNP array](#illumina), [paired bams with Sequenza](#testsequenzaraw), [GISTIC](#gistic) and [Affymetrix SNP arrays from CEL files](#testaffycel)). If `--runGistic` is `1`, GISTIC will be tested and if `--smallMem` is set `1`, GISTIC will run in small memory mode and will only require about 10GB of RAM vs 50GB of RAM at the expense of a longer running time.\n\n\n### Glossary:\n\nLet's call:\n- `aCNViewer_DATA` the location where the test data set [aCNViewer_DATA.tar.gz](https://www.cng.fr/genodata/pub/LIVER/aCNViewer_DATA.tar.gz) has been uncompressed into\n\n- `BIN_DIR` the folder containing all third-party softwares located in `aCNViewer_DATA/bin`. Here is the list of files and folders that should be in `BIN_DIR`:\n * apt-*: [Affymetrix Power Tools](#apt) binaries\n * ascat: contains ASCAT file for GC correction (this folder is automatically created by aCNViewer and GC files are automatically downloaded)\n + GC_Affy250k.txt\n + GC_AffySNP6_102015.txt\n + gc_done\n + GC_Illumina660k.txt\n + GC_IlluminaOmniexpress.txt\n * GISTIC*: installation folder of [GISTIC](#gistic)\n * PennCNV: contains PennCNV-Affy protocols and helper scripts (will be automatically created by aCNViewer from [gw6.tar.gz](http://www.openbioinformatics.org/penncnv/download/gw6.tar.gz))\n * samtools*: installation folder of [samtools](#samtools)\n * tQN*: installation folder of [tQN](#tqn)\n * ../genomes: folder located in the parent folder of `BIN_DIR` with one folder per genomic build. Each genomic build folder `BUILD` should contain at least:\n + one file named `BUILD.centro.txt` with centromere positions for each chromosome of the genomic build (can be generated using `curl -s \"http://hgdownload.cse.ucsc\\\n.edu/goldenPath/BUILD/database/cytoBand.txt.gz\" | gunzip -c | grep acen > BUILD.centro.txt`)\n + a tab-delimited file named `BUILD.chrom.sizes` with 2 columns respectively chromosome name and chromosome length (can be downloaded from UCSC Genome browser)\n + optionnaly, a reference fasta file (with one of the extension `.fa`, `.fa.gz`, `.fasta` or `.fasta.gz`) if you plan to use Sequenza\n \n- `DOCKER_OR_PYTHON` refers to the fact that `docker run fjdceph/acnviewer` or `python aCNViewer/code/aCNViewer.py` can be used as a prefix to run aCNViewer depending on the chosen installation method.\n\n\n### Requirements:\n\nDownload the test data set [aCNViewer_DATA.tar.gz (~5GB and ~20GB uncompressed)](https://www.cng.fr/genodata/pub/LIVER/aCNViewer_DATA.tar.gz). In terms of computing resources: if you plan to:\n- run Sequenza on paired bam files, an access to a computer cluster is highly recommended as even though aCNViewer will be able to process your data in multi-threading mode, it may take quite a long time depending on the number of sample pairs to analyze\n- run GISTIC in order to have a robust statistical way to prioritize recurrent regions of interest, a machine with at least 50GB of RAM is necessary with `--smallMem 0` and 10GB with `--smallMem 1` (this option will make GISTIC run substantially longer)\n\n\n\n### Processing SNP array data\n\n#### Affymetrix\n\n##### TestAffyAscat\nGenerate all available plots from ASCAT segment files using base resolution for the quantitative histograms and using a window size of 2Mbp for the other plots:
\n\n`DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt`\n\n![quantitative stacked histogram example:](/img/GSE9845_lrr_baf.segments_hg18_cov_hist.png?raw=true \"Quantitative stacked histogram example\")\n\n![Histogram of heterozygous / homozygous CNVs:](/img/GSE9845_lrr_baf.segments_hg18_cov_hist_hetHom.png?raw=true \"Histogram of heterozygous / homozygous CNVs\")\n\nHere are other typical plots you may be interested in:\n\nCustomize colors:\n\n`DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_RCOLOR --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --rColorFile aCNViewer_DATA/rColor.txt`\n\n\nQuantitative histogram with GISTIC results:\n\n`DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_GISTIC --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --runGISTIC 1`\n\nIf you have trouble running this example (in particular if your machine freezes or you get the message \"Killed\" in the \"_gistic.txt.err\" file), it may be due to a lack of resources in the machine you are using. In that case, please add the following option to the command above `--smallMem 1` so that GISTIC runs in compressed memory mode. You can view the GISTIC results with [significant broad events](/img/all_lesions.conf_90.txt?raw=true) and [significant focal events](/img/broad_significance_results.txt?raw=true).\n\n\nHeatmap of relative copy number values only for the clinical feature `BCLC stage` with the chromosome legend position set at `0,.55` i.e. at the left-most of the graph and at 55% on the y axis and the group legend position set at `.9,1.05` (basically at the top right corner):\n\n`DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_HEATMAP1 --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --plotAll 0 --heatmap 1 --dendrogram 0 -G \"BCLC stage\" --chrLegendPos 0,.55 --groupLegendPos .9,1.05 --useRelativeCopyNbForClustering 1`\n\n![Heatmap of relative copy number values using the clinical feature `BCLC stage`:](/img/matrix_relCopyNb_2000000nt_heatmap_BCLC_stage_ward_10.png?raw=true \"Heatmap of relative copy number values using the clinical feature `BCLC stage`\")\n\n\n\nHeatmap with regions ordered by genomic positions (only clustering on samples):\n\n`DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_HEATMAP_GENPOS --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --plotAll 0 --heatmap 1 --dendrogram 0 -G \"BCLC stage\" --chrLegendPos 0,.55 --groupLegendPos .9,1.05 --useRelativeCopyNbForClustering 1 --keepGenomicPosForHistogram 1`\n\n![Heatmap of relative copy number values with regions ordered by genomic positions using the clinical feature `BCLC stage`:](/img/matrix_relCopyNb_2000000nt_heatmap_BCLC_stage_ward_11.png?raw=true \"Heatmap of relative copy number values with regions ordered by genomic positions using the clinical feature `BCLC stage`\")\n\n\nHeatmap with copy number values:\n\n`DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_HEATMAP2 --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --plotAll 0 --heatmap 1 --dendrogram 0 -G \"BCLC stage\" --chrLegendPos 0,.55 --groupLegendPos .9,1.05`\n\n![Heatmap of copy number values using the clinical feature `BCLC stage`:](/img/matrix_rawCopyNb_2000000nt_heatmap_BCLC_stage_ward_00.png?raw=true \"Heatmap of copy number values using the clinical feature `BCLC stage`\")\n\n\nDendrogram with copy number values:\n\n`DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_DENDRO --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --plotAll 0 --heatmap 0 --dendrogram 1 -G \"BCLC stage\" -u 1`\n\n![Dendrogram of copy number values using the clinical feature `BCLC stage`:](/img/matrix_rawCopyNb_2000000nt_dendro_BCLC_stage_rawCopyNb.png?raw=true \"Dendrogram of copy number values using the clinical feature `BCLC stage`\")\n\n\nCustomize output formats:\n\n- all outputs set to pdf: `DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_PDF --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --outputFormat pdf`\n\n- all output set to jpg: `DOCKER_OR_PYTHON` `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_PDF --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --outputFormat jpg`\n\n- heatmaps set to `bmp`, histograms to `tiff` and dendrograms to `pdf` with the R plot parameters `width=10,height=8`: `-f aCNViewer_DATA/snpArrays250k_sty/GSE9845_lrr_baf.segments.txt -t TEST_AFFY_OTHER_OUT --refBuild hg18 -w 2000000 -b aCNViewer_DATA/bin --sampleFile aCNViewer_DATA/snpArrays250k_sty/GSE9845_clinical_info2.txt --outputFormat \"heat:bmp;hist:tiff;dend:pdf(width=10,height=8)\"`\n\n\n==**Here is the full command:**==\n\n`DOCKER_OR_PYTHON` `-f ASCAT_SEGMENT_FILE --refBuild REF_BUILD -b` `BIN_DIR` `[--histogram HISTOGRAM --lohToPlot LOH_TO_PLOT --useFullResolutionForHist USE_FULL_RESOLUTION_FOR_HIST] [-c CHR_SIZE_FILE -t OUTPUT_DIR -C CENTROMERE_FILE -w WINDOW_SIZE --sampleFile SAMPLE_FILE -G PHENOTYPIC_COLUMN_NAME --rColorFile RCOLOR_FILE --plotAll PLOT_ALL --outputFormat OUTPUT_FORMAT --ploidyFile PLOIDY_FILE --sampleToProcessList SAMPLE_TO_PROCESS_LIST --sampleToExcludeList SAMPLE_TO_EXCLUDE_LIST --sampleAliasFile SAMPLE_ALIAS_FILE] [--heatmap HEATMAP --labRow LAB_ROW --labCol LAB_COL --cexCol CEX_COL --cexRow CEX_ROW --height HEIGHT --width WIDTH --margins MARGINS --hclust HCLUST --groupLegendPos GROUP_LEGEND_POS --chrLegendPos CHR_LEGEND_POS --useRelativeCopyNbForClustering USE_RELATIVE_COPY_NB_FOR_CLUSTERING --keepGenomicPosForHistogram KEEP_GENOMIC_POS] [--dendrogram DENDROGRAM --useShape USE_SHAPE] [--runGISTIC RUN_GISTIC --geneGistic GENE_GISTIC --smallMem SMALL_MEM --broad BROAD --brLen BR_LEN --conf CONF --armPeel ARM_PEEL --saveGene SAVE_GENE --gcm GCM]`
\nwhere:\n* `ASCAT_SEGMENT_FILE`: ASCAT segment file (`ascat.output$segments` obtained by running `ascat.runAscat`) with the following columns:\n + `sample`\n + `chr`\n + `startpos`\n + `endpos`\n + `nMajor`\n + `nMinor`\n* `REF_BUILD`: the genome build used to generate the CNV segments (`hg18` and `hg19` are currently supported. If you want to add another build `BUILD`, please add a folder in `BUILD` in `aCNViewer_DATA/genomes` containing at least a tab-delimited file named `BUILD.chrom.sizes` with each chromosome name and length and a tab-delimited file named `BUILD.centro.txt` with the centromere positions by chr [this file can be generated using `curl -s \"http://hgdownload.cse.ucsc.edu/goldenPath/BUILD/database/cytoBand.txt.gz\" | gunzip -c | grep acen > centro_build.txt`])\n\n**The following options are general plotting options:**\n* `CHR_SIZE_FILE`: a tab-delimited file with 2 columns respectively chromosome name and chromosome length. When `REF_BUILD` is set, `CHR_SIZE_FILE` is automatically set to `aCNViewer_DATA/genomes/REF_BUILD.chrom.sizes`.\n* `CENTROMERE_FILE`: file giving the centromere bounds. Can be generated using `curl -s \"http://hgdownload.cse.ucsc.edu/goldenPath/BUILD/database/cytoBand.txt.gz\" | gunzip -c | grep acen > centro_build.txt`. When `REF_BUILD` is set, `CENTROMERE_FILE` is automatically set to `aCNViewer_DATA/genomes/REF_BUILD.centro.txt`.\n* `WINDOW_SIZE`: segment size in bp. Please note that alternatively, `-p PERCENTAGE` can be used instead of `-w WINDOW_SIZE` in order to set the segment size in percentage of chromosome length where `PERCENTAGE` is a floating number between 0 and 100. If `WINDOW_SIZE` and `PERCENTAGE` are null then `WINDOW_SIZE` is set to 2Mb by default.\n* `SAMPLE_FILE`: a tab-delimited file that should contain a column named `Sample` with the name of each sample and at least another column with the phenotypic / clinical feature. This file can contain a `sampleAlias` which will be used as the official sample id if provided.\n* `PHENOTYPIC_COLUMN_NAME` is optional and refers to the name of the column of the phenotypic / clinical feature of interest in `SAMPLE_FILE`. If you omit this parameter, one plot per feature defined in `SAMPLE_FILE` will be generated.\n* `RCOLOR_FILE`: file allowing to customize graph colors: colors for histograms can be overriden using a section named \"[histogram]\" which should contain exactly 10 colors [one per line] corresponding to CNV values in the following order: \"≤ -4\", \"-3\", \"-2\", \"-1\", \"1\", \"2\", \"3\", \"4\", \"5\", \"≥ 6\"). Histogram colors for heterozygous / homozygous CNVs can be changed using the section \"[heteroHomo]\" which should contain 6 colors corresponding to the values \"-Hom\", \"-Het\", \"=Hom\", \"=Het\", \"+Hom\", \"+Het\". Colors for dendrograms can be redefined using the section \"[group]\" which should contain at least the same number of colors than the number of distinct values for the phenotypic / clinical feature of interest. Colors for heatmaps are customizable using the section \"[chr]\" and should contain 22 colors corresponding to chromosomes 1 to 22], the section \"[group]\" (the same as previously seen for dendrograms) and the section \"[heatmap]\" which should contain 10 colors (one per line) corresponding to CNV values in the following order: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"≥ 9\". An example can be found [here](/img/rColor.txt).\n* `PLOT_ALL`: specify whether all available plots should be generated. The default value is `1`.\n* `OUTPUT_FORMAT`: allow to customize output formats for the different types of available plots (histograms, heatmaps and dendrograms). Examples of use can be found [above](#outputFormatExamples). The default value is `hist:png(width=4000,height=1800,res=300);hetHom:png(width=4000,height=1800,res=300);dend:png(width=4000,height=2200,res=300);heat:pdf(width=10,height=12)`.\n* `PLOIDY_FILE`: custom ploidy values for each sample. Can either be a tab-delimited file with at least 2 columns: \"sample\" and \"ploidy\" or an integer which will set the same ploidy to all samples. By default, the ploidy is calculated using the CNV file segmented in fragments of 10% of chromosomal length and its value will be the most represented CNV value for each sample.\n* `SAMPLE_TO_PROCESS_LIST`: comma-separated string or file with one sample per line used to restrict the list of samples to process by aCNViewer.\n* `SAMPLE_TO_EXCLUDE_LIST`: comma-separated string or file with one sample per line used to exclude a list of samples from analyses.\n* `SAMPLE_ALIAS_FILE`: optional parameter used to change the sample name to a preferred sample name. It is a tab-delimited file with 2 columns: one for the sample name and a second one with the preferred sample name.\n\n\n**The following options are histogram specific**:\n* `HISTOGRAM`: specify whether an histogram should be generated. The default value is `0` but its value is overriden to `1` when option `--plotAll 1` is set.\n* `LOH_TO_PLOT`: histogram option for LOH plotting. Values should be one of \"cn-LOH\" for plotting cn-LOH only, \"LOH\" for LOH only, \"both\" for cn-LOH and LOH or \"none\" to disable this feature. The default value is \"cn-LOH\".\n* `USE_FULL_RESOLUTION_FOR_HIST`: tell whether to plot histogram using full resolution i.e. CNVs are not segmented according to a user-defined length through windowing approach. The default value is `1`. If `0`, the resolution of the plot will be given by either `WINDOW_SIZE` or `PERCENTAGE`.\n\n\n**The following options are GISTIC options** (more details can be found [here](ftp://ftp.broadinstitute.org/pub/GISTIC2.0/GISTICDocumentation_standalone.htm)):\n* `RUN_GISTIC`: specify whether to run GISTIC in order to have a statistical way to prioritize regions of interest. The default value is `0`.\n* `GENE_GISTIC`: tell whether gene GISTIC algorithm should be used to calculate the significance of deletions at a gene level instead of a marker level. The default value is `1`.\n* `SMALL_MEM`: tell GISTIC whether to use memory compression at the cost of a longer runtime. The default value is `0`.\n* `BROAD`: tell GISTIC to run the broad-level analysis as well. The default value is `1`.\n* `BR_LEN`: set GISTIC's `broad_len_cutoff`. The default value is `0.5`.\n* `CONF`: set the confidence level used to calculate the region containing a driver. The default value is `0.9`.\n* `ARM_PEEL`: set GISTIC's `arm_peeloff`. The default value is `1`.\n* `SAVE_GENE`: tell GISTIC whether to save gene tables. The default value is `1`.\n* `GCM`: set GISTIC's `gene_collapse_method`. The default value is `extreme`.\n\n\n**The following options are mainly specific to heatmaps while a few are related to dendrograms:**\n\n* `HEATMAP` is an optional parameter used only if `PLOT_ALL` is set to `0` to tell whether to plot heatmaps or not. The default value is `1`\n* `LAB_ROW` is an optional parameter telling whether heatmap's row names (chromosomal regions) should be shown. The default value is `0`\n* `LAB_COL` is an optional parameter telling whether heatmap's column names (sample names) should be shown. 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