Using the R Package crlmm for Genotyping and Copy Number Estimation.
Using the R Package crlmm for Genotyping and Copy Number Estimation.
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DOI：
10.18637/jss.v040.i12
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发表时间：
2011-05
影响因子：
5.8
通讯作者：
R. Scharpf;R. Irizarry;Matthew E. Ritchie;B. Carvalho;I. Ruczinski
中科院分区：
文献类型：
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作者：
R. Scharpf;R. Irizarry;Matthew E. Ritchie;B. Carvalho;I. Ruczinski
Affymetrix 等基因分型平台可用于评估数百万个标记的基因型-表型以及拷贝数-表型关联。虽然在 HapMap 样本上进行评估时，基因分型算法基本上是一致的，但评估拷贝数变化的工具则变化更大，而且常常不一致。对这种不一致的一种解释是，拷贝数估计值容易受到不同时间或不同实验室处理的样本组之间的系统差异的影响。不针对批次效应进行调整的分析算法很容易产生虚假的关联度量。 R 包 crlmm 实现了一个多级模型，该模型可根据批次效应进行调整并提供特定于等位基因的拷贝数估计。本文阐述了估计等位基因特异性拷贝数的工作流程，以及将标记水平估计与免费的 Bioconductor 软件集成以推断拷贝数增加或丢失的区域。所有分析均在统计环境 R 中进行。
Genotyping platforms such as Affymetrix can be used to assess genotype-phenotype as well as copy number-phenotype associations at millions of markers. While genotyping algorithms are largely concordant when assessed on HapMap samples, tools to assess copy number changes are more variable and often discordant. One explanation for the discordance is that copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. Analysis algorithms that do not adjust for batch effects are prone to spurious measures of association. The R package crlmm implements a multilevel model that adjusts for batch effects and provides allele-specific estimates of copy number. This paper illustrates a workflow for the estimation of allele-specific copy number and integration of the marker-level estimates with complimentary Bioconductor software for inferring regions of copy number gain or loss. All analyses are performed in the statistical environment R.