CNVKIT_BATCH(1)

💖 CNVKIT_BATCH(1) — cnvkit_batch 💖

okay bestie, listen up 🫶 this is the one command that runs the whole CNVkit pipeline on one or more BAM files. like, the entire glow-up, start to finish, no cap ✨

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🌸 NAME 🌸

cnvkit_batch — Run the complete CNVkit pipeline on one or more BAM files. 💅

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✨ DESCRIPTION ✨

here's the vibe, fr fr — the usage looks like this:

usage: cnvkit.py batch [-h] [-m {hybrid,amplicon,wgs}]
       [--segment-method {cbs,flasso,haar,none,hmm,hmm-tumor,hmm-germline}]
       [-y] [-c] [--drop-low-coverage] [-p [PROCESSES]] [--rscript-path PATH]
       [-n [FILES ...]] [-f FILENAME] [-t FILENAME] [-a FILENAME]
       [--annotate FILENAME] [--short-names] [--target-avg-size TARGET_AVG_SIZE]
       [-g FILENAME] [--antitarget-avg-size ANTITARGET_AVG_SIZE]
       [--antitarget-min-size ANTITARGET_MIN_SIZE]
       [--output-reference FILENAME] [--cluster] [-r REFERENCE] [-d DIRECTORY]
       [--scatter] [--diagram] [bam_files ...]

like, that's a lot of options but we're gonna go through every single one because you deserve to know 😤💖

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🎀 positional arguments 🎀

• bam_files — Mapped sequence reads (.bam). your actual data, the main character 👑

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🌈 options 🌈

• -h, --help — show this help message and exit. the classic escape hatch 🚪
• -m {hybrid,amplicon,wgs}, --seq-method {hybrid,amplicon,wgs}, --method {hybrid,amplicon,wgs} — Sequencing assay type: hybridization capture (hybrid), targeted amplicon sequencing (amplicon), or whole genome sequencing (wgs). Determines whether and how to use antitarget bins. [Default: hybrid] 🧬
• --segment-method {cbs,flasso,haar,none,hmm,hmm-tumor,hmm-germline} — Method used in the segment step. [Default: cbs] ✂️
• -y, --male-reference, --haploid-x-reference — Use or assume a male reference (i.e. female samples will have +1 log-CNR of chrX; otherwise male samples would have -1 chrX). ok so this one matters, don't skip it 🧪
• -c, --count-reads — Get read depths by counting read midpoints within each bin. (An alternative algorithm). 🔢
• --drop-low-coverage — Drop very-low-coverage bins before segmentation to avoid false-positive deletions in poor-quality tumor samples. genuinely a good idea for messy samples, ngl 🙈
• -p [PROCESSES], --processes [PROCESSES] — Number of subprocesses used to running each of the BAM files in parallel. Without an argument, use the maximum number of available CPUs. [Default: process each BAM in serial] 🏃‍♀️💨
• --rscript-path PATH — Path to the Rscript executable to use for running R code. Use this option to specify a non-default R installation. [Default: Rscript] 🧚

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💜 To construct a new copy number reference 💜

• -n [FILES ...], --normal [FILES ...] — Normal samples (.bam) used to construct the pooled, paired, or flat reference. If this option is used but no filenames are given, a "flat" reference will be built. Otherwise, all filenames following this option will be used. 🫶
• -f FILENAME, --fasta FILENAME — Reference genome, FASTA format (e.g. UCSC hg19.fa) 🧬
• -t FILENAME, --targets FILENAME — Target intervals (.bed or .list) 🎯
• -a FILENAME, --antitargets FILENAME — Antitarget intervals (.bed or .list) 🚧
• --annotate FILENAME — Use gene models from this file to assign names to the target regions. Format: UCSC refFlat.txt or ensFlat.txt file (preferred), or BED, interval list, GFF, or similar. 📝
• --short-names — Reduce multi-accession bait labels to be short and consistent. ✨
• --target-avg-size TARGET_AVG_SIZE — Average size of split target bins (results are approximate). 📏
• -g FILENAME, --access FILENAME — Regions of accessible sequence on chromosomes (.bed), as output by the access command. 🔍
• --antitarget-avg-size ANTITARGET_AVG_SIZE — Average size of antitarget bins (results are approximate). 📐
• --antitarget-min-size ANTITARGET_MIN_SIZE — Minimum size of antitarget bins (smaller regions are dropped). 🗑️
• --output-reference FILENAME — Output filename/path for the new reference file being created. (If given, ignores the -o/--output-dir option and will write the file to the given path. Otherwise, reference.cnn will be created in the current directory or specified output directory.) 💾
• --cluster — Calculate and use cluster-specific summary stats in the reference pool to normalize samples. 🧮

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💅 To reuse an existing reference 💅

• -r REFERENCE, --reference REFERENCE — Copy number reference file (.cnn). ♻️

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🦋 Output options 🦋

• -d DIRECTORY, --output-dir DIRECTORY — Output directory. 📁
• --scatter — Create a whole-genome copy ratio profile as a PDF scatter plot. 📊
• --diagram — Create an ideogram of copy ratios on chromosomes as a PDF. 🗺️

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k bestie that's the whole thing — every flag, every default, every path, all of it. go run your pipeline, you're gonna eat 💅🔥

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okay that's the page!! this was cnvkit.py batch 0.9.10, dated July 2023 — go forth and slay your copy number analysis, ily, bye 🎀💖✨