Removing technical variability in RNA-seq data using conditional quantile normalization.
Removing technical variability in RNA-seq data using conditional quantile normalization.
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DOI：
10.1093/biostatistics/kxr054
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发表时间：
2012-04
期刊：
影响因子：
--
通讯作者：
Wu Z
中科院分区：
文献类型：
--
作者：
Hansen KD;Irizarry RA;Wu Z
在全基因组范围内测量基因表达的能力是分子生物学中最有前途的成就之一。微阵列技术首先允许这一点，但由于不必要的变异性来源而充满了问题。经过十年的统计方法发展，其中许多问题现已得到缓解。最近开发的RNA测序（RNA-seq）技术产生了很大的兴奋，部分原因是与微阵列相比降低了变异性。然而，我们发现RNA-seq数据显示出不必要的和模糊的变异性，类似于在微阵列中首次观察到的。特别是，我们发现鸟嘌呤-胞嘧啶含量（GC含量）对基因表达测量具有很强的样品特异性影响，如果不进行校正，会导致下游结果的假阳性。我们还报告了通常观察到的数据失真，证明了数据规范化的必要性。在这里，我们描述了一种统计方法，该方法将精度提高了42%，而不损失准确性。我们得到的条件分位数归一化算法结合了鲁棒的广义回归，以消除由确定性特征（如GC含量和分位数归一化）引入的系统偏差，以纠正全局失真。
The ability to measure gene expression on a genome-wide scale is one of the most promising accomplishments in molecular biology. Microarrays, the technology that first permitted this, were riddled with problems due to unwanted sources of variability. Many of these problems are now mitigated, after a decade's worth of statistical methodology development. The recently developed RNA sequencing (RNA-seq) technology has generated much excitement in part due to claims of reduced variability in comparison to microarrays. However, we show that RNA-seq data demonstrate unwanted and obscuring variability similar to what was first observed in microarrays. In particular, we find guanine-cytosine content (GC-content) has a strong sample-specific effect on gene expression measurements that, if left uncorrected, leads to false positives in downstream results. We also report on commonly observed data distortions that demonstrate the need for data normalization. Here, we describe a statistical methodology that improves precision by 42% without loss of accuracy. Our resulting conditional quantile normalization algorithm combines robust generalized regression to remove systematic bias introduced by deterministic features such as GC-content and quantile normalization to correct for global distortions.
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影响因子：
4.5
作者：
Pai AA;Bell JT;Marioni JC;Pritchard JK;Gilad Y
通讯作者：
Gilad Y
影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
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Dudoit S
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64.8
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通讯作者：
Skol, Andrew
影响因子：
2.1
作者：
Carvalho, Benilton;Bengtsson, Henrik;Irizarry, Rafael A.
通讯作者：
Irizarry, Rafael A.
影响因子：
64.8
作者：
通讯作者：
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