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CNV的量化

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生信技能树
发布2019-03-07 17:36:41
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发布2019-03-07 17:36:41
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文章被收录于专栏:生信技能树

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?

欢迎留言讨论

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原始发表:2019-02-17,如有侵权请联系 cloudcommunity@tencent.com 删除

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