Algorithm-driven artifacts in median polish summarization of microarray data.
Algorithm-driven artifacts in median polish summarization of microarray data.
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DOI：
10.1186/1471-2105-11-553
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发表时间：
2010-11-11
影响因子：
3
通讯作者：
Usadel B
中科院分区：
文献类型：
--
作者：
Giorgi FM;Bolger AM;Lohse M;Usadel B
使用Affymetrix型寡核苷酸微阵列对转录强度的高通量测量在过去十年中产生了大量数据。存在不同的预处理技术来将这些芯片测量的原始信号强度转换为基因表达估计。虽然这些技术已经在差异基因表达分析的背景下得到了广泛的基准测试，但在基于共表达的研究(如样本分类)中，它们的性能得到评估的例子很少。在本文中，我们在阵列间相关性分析的背景下对三个最常用的归一化过程(MAS5、RMA和GCRMA)进行了基准测试，证实并推广了RMA和GCRMA在归一化时始终高估样本相似性的发现。我们确定，中间的磨光总结负责产生这些过度相似的人工产物的很大一部分。此外，我们还表明，大多数受影响的试剂组也显示出内部信号不一致，并且往往由击中不同基因转录本的单个探针组成。最后，我们对RMA/GCRMA摘要过程进行了更正，该过程大大减少了阵列间相关性伪影，而不影响差异表达基因的检测。我们提出了TRMA作为RMA的改进，以标准化基于相关性的分析的微阵列实验。
High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification. In the present paper we benchmark the three most used normalization procedures (MAS5, RMA and GCRMA) in the context of inter-array correlation analysis, confirming and extending the finding that RMA and GCRMA consistently overestimate sample similarity upon normalization. We determine that median polish summarization is responsible for generating a large proportion of these over-similarity artifacts. Furthermore, we show that most affected probesets show also internal signal disagreement, and tend to be composed by individual probes hitting different gene transcripts. We finally provide a correction to the RMA/GCRMA summarization procedure that massively reduces inter-array correlation artifacts, without affecting the detection of differentially expressed genes. We propose tRMA as a modification of RMA to normalize microarray experiments for correlation-based analysis.
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如何决定？从短寡核苷酸阵列数据中计算基因表达的不同方法将产生不同的结果。
影响因子：
3
作者：
Millenaar, FF;Okyere, J;May, ST;van Zanten, M;Voesenek, LACJ;Peeters, AJM
通讯作者：
Peeters, AJM
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
DOI：
10.1073/pnas.011404098
发表时间：
2001-01-02
影响因子：
11.1
作者：
Li, C;Wong, WH
通讯作者：
Wong, WH
影响因子：
5.8
作者：
Hubbell, E;Liu, WM;Mei, R
通讯作者：
Mei, R