CNV的量化

这里以TCGA计划为标准：https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/CNV_Pipeline/
Numeric focal-level Copy Number Variation (CNV) values were generated with “Masked Copy Number Segment” files from tumor aliquots using GISTIC2, on a project level.
Only protein-coding genes were kept, and their numeric CNV values were further thresholded by a noise cutoff of 0.3:

• Genes with focal CNV values smaller than -0.3 are categorized as a “loss” (-1)
• Genes with focal CNV values larger than 0.3 are categorized as a “gain” (+1)
• Genes with focal CNV values between and including -0.3 and 0.3 are categorized as “neutral” (0).
  在CNVKIT软件也有：https://cnvkit.readthedocs.io/en/stable/calling.html
  In a diploid genome, a single-copy gain in a perfectly pure, homogeneous sample has a copy ratio of 3/2. In log2 scale, this is log2(3/2) = 0.585, and a single-copy loss is log2(1/2) = -1.0.
  在COSMIC数据库也有说明：https://cancer.sanger.ac.uk/cosmic/help/cnv/overview

  思考题

  一个样本的CNV好计算，也容易理解。
  但不同病人，或者同一个病人的不同肿瘤部位的CNV状态，就是多个样本如何计算相关性呢？