Evaluation of methods for oligonucleotide array data via quantitative real-time PCR.
Evaluation of methods for oligonucleotide array data via quantitative real-time PCR.
复制标题
通过定量实时PCR评估寡核苷酸阵列数据的方法。
DOI：
10.1186/1471-2105-7-23
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发表时间：
2006-01-17
影响因子：
3
通讯作者：
Kerr, KF
中科院分区：
文献类型：
--
作者：
Qin, LX;Beyer, RP;Hudson, FN;Linford, NJ;Morris, DE;Kerr, KF
目前有许多不同的方法用于处理和总结来自Affytron寡核苷酸阵列的探针水平数据。验证这些方法并确定那些最有效的方法是非常有意义的。没有单一的最佳方法来进行这种验证，需要多种方法。此外，收集基因表达数据是为了回答各种科学问题，同一种方法可能并不适用于所有问题。到目前为止，只有少数验证研究已经完成，其中大多数依赖于spike-in数据集，并专注于检测差异表达的问题。在这里，我们寻求擅长估计相对表达的方法。我们评估方法，通过确定那些给出最强的线性相关的表达测量阵列和“金标准”测定。定量逆转录聚合酶链反应（qRT-PCR）通常被生物学家认为是测量基因表达的“金标准”测定，并且通常用于确认来自微阵列数据的发现。在这里，我们使用qRT-PCR测量来验证处理寡核苷酸阵列数据的组成部分的方法：背景调整、归一化、错配调整和概率汇总。我们的方法比spike-in研究的一个优点是，方法是在一个真实的数据集上验证的，该数据集是为了解决一个科学问题而收集的。我们最初确定了六种流行的方法中的三种，这些方法在中等和高强度基因的寡核苷酸阵列和RT-PCR数据之间始终产生最佳一致性。这三种方法通常被称为MAS 5、gcRMA和dChip失配模式。对于中等强度和高强度基因，我们确定使用来自错配探针的数据（如在MAS 5和dChip错配中）和基于序列的背景调整方法（如在gcRMA中）作为方法性能中最重要的因素。然而，我们发现使用低强度基因的错配探针的方法可靠性较差，这与以前的研究一致。我们提倡使用基于序列的背景调整来代替失配调整，以在整个强度谱中获得最佳结果。没有一种标准化或概率总结的方法显示出任何一致的优势。
There are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is generally considered the "gold-standard" assay for measuring gene expression by biologists and is often used to confirm findings from microarray data. Here we use qRT-PCR measurements to validate methods for the components of processing oligo array data: background adjustment, normalization, mismatch adjustment, and probeset summary. An advantage of our approach over spike-in studies is that methods are validated on a real dataset that was collected to address a scientific question. We initially identify three of six popular methods that consistently produced the best agreement between oligo array and RT-PCR data for medium- and high-intensity genes. The three methods are generally known as MAS5, gcRMA, and the dChip mismatch mode. For medium- and high-intensity genes, we identified use of data from mismatch probes (as in MAS5 and dChip mismatch) and a sequence-based method of background adjustment (as in gcRMA) as the most important factors in methods' performances. However, we found poor reliability for methods using mismatch probes for low-intensity genes, which is in agreement with previous studies. We advocate use of sequence-based background adjustment in lieu of mismatch adjustment to achieve the best results across the intensity spectrum. No method of normalization or probeset summary showed any consistent advantages.
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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：
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：
3
作者：
Ploner A;Miller LD;Hall P;Bergh J;Pawitan Y
通讯作者：
Pawitan Y
影响因子：
2.7
作者：
Etienne, W;Meyer, MH;Meyer, RA
通讯作者：
Meyer, RA
影响因子：
5.8
作者：
Cope, LM;Irizarry, RA;Speed, TP
通讯作者：
Speed, TP