Comparison of normalization methods for CodeLink Bioarray data.
Comparison of normalization methods for CodeLink Bioarray data.
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DOI：
10.1186/1471-2105-6-309
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发表时间：
2005-12-28
影响因子：
3
通讯作者：
Kaminski N
中科院分区：
文献类型：
--
作者：
Wu W;Dave N;Tseng GC;Richards T;Xing EP;Kaminski N
微阵列数据的质量会严重影响下游分析的准确性。为了减少这些数据的变异性和增强信号的再现性，已经提出和评估了许多标准化方法，其中大部分是针对从cDNA微阵列和Affyssin基因芯片获得的数据。CodeLink Bioarrays是一种新出现的单色寡核苷酸微阵列平台。到目前为止，还没有报告评价CodeLink Bioarrays标准化方法的研究。我们比较了五种现有的归一化方法，在降噪和信号保留方面：中位数（由制造商建议），CyclicLoess，Quantile，Iset和Qspline。将这些方法应用于CodeLink Bioarrays生成的两个真实的数据集（时间过程数据集和肺部疾病相关数据集），并使用多重统计显著性检验进行评估。与Median相比，CyclicLoess和Qspline在降低变异性和保留信号方面表现出显著且最一致的改善。CyclicLoess似乎比Qspline保留更多的信号。在两个数据集中，分位数比中位数减少了更多的变异性，但未能在时间过程数据集中始终保留更多的信号。Iset在时间过程数据集中的降噪或信号增强方面都没有优于Median。中值不足以降低变异性或有效保留CodeLink生物阵列数据的信号。CyclicLoess是一种更适合对这些数据进行标准化的方法。CyclicLoess似乎也是五种不同的标准化策略中最有效的方法。
The quality of microarray data can seriously affect the accuracy of downstream analyses. In order to reduce variability and enhance signal reproducibility in these data, many normalization methods have been proposed and evaluated, most of which are for data obtained from cDNA microarrays and Affymetrix GeneChips. CodeLink Bioarrays are a newly emerged, single-color oligonucleotide microarray platform. To date, there are no reported studies that evaluate normalization methods for CodeLink Bioarrays. We compared five existing normalization approaches, in terms of both noise reduction and signal retention: Median (suggested by the manufacturer), CyclicLoess, Quantile, Iset, and Qspline. These methods were applied to two real datasets (a time course dataset and a lung disease-related dataset) generated by CodeLink Bioarrays and were assessed using multiple statistical significance tests. Compared to Median, CyclicLoess and Qspline exhibit a significant and the most consistent improvement in reduction of variability and retention of signal. CyclicLoess appears to retain more signal than Qspline. Quantile reduces more variability than Median in both datasets, yet fails to consistently retain more signal in the time course dataset. Iset does not improve over Median in either noise reduction or signal enhancement in the time course dataset. Median is insufficient either to reduce variability or to retain signal effectively for CodeLink Bioarray data. CyclicLoess is a more suitable approach for normalizing these data. CyclicLoess also seems to be the most effective method among the five different normalization strategies examined.
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DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D
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
影响因子：
5.8
作者：
Ballman, KV;Grill, DE;Therneau, TM
通讯作者：
Therneau, TM
影响因子：
5.8
作者：
Edwards, D
通讯作者：
Edwards, D