Suppr超能文献

噪声采样方法:一种方差分析方法,可用于通过DNA微阵列测量的差异调节基因的稳健选择。

Noise sampling method: an ANOVA approach allowing robust selection of differentially regulated genes measured by DNA microarrays.

作者信息

Draghici Sorin, Kulaeva Olga, Hoff Bruce, Petrov Anton, Shams Soheil, Tainsky Michael A

机构信息

Department of Computer Science, Wayne State University, 431 State Hall, Detroit, MI, 48202, USA.

出版信息

Bioinformatics. 2003 Jul 22;19(11):1348-59. doi: 10.1093/bioinformatics/btg165.

Abstract

MOTIVATION

A crucial step in microarray data analysis is the selection of subsets of interesting genes from the initial set of genes. In many cases, especially when comparing a specific condition to a reference, the genes of interest are those which are differentially expressed. Two common methods for gene selection are: (a) selection by fold difference (at least n fold variation) and (b) selection by altered ratio (at least n standard deviations away from the mean ratio).

RESULTS

The novel method proposed here is based on ANOVA and uses replicate spots to estimate an empirical distribution of the noise. The measured intensity range is divided in a number of intervals. A noise distribution is constructed for each such interval. Bootstrapping is used to map the desired confidence levels from the noise distribution corresponding to a given interval to the measured log ratios in that interval. If the method is applied on individual arrays having replicate spots, the method can calculate an overall width of the noise distribution which can be used as an indicator of the array quality. We compared this method with the fold change and unusual ratio method. We also discuss the relationship with an ANOVA model proposed by Churchill et al. In silico experiments were performed while controlling the degree of regulation as well as the amount of noise. Such experiments show the performance of the classical methods can be very unsatisfactory. We also compared the results of the 2-fold method with the results of the noise sampling method using pre and post immortalization cell lines derived from the MDAH041 fibroblasts hybridized on Affymetrix GeneChip arrays. The 2-fold method reported 198 genes as upregulated and 493 genes as downregulated. The noise sampling method reported 98 gene upregulated and 240 genes downregulated at the 99.99% confidence level. The methods agreed on 221 genes downregulated and 66 genes upregulated. Fourteen genes from the subset of genes reported by both methods were all confirmed by Q-RT-PCR. Alternative assays on various subsets of genes on which the two methods disagreed suggested that the noise sampling method is likely to provide fewer false positives.

摘要

动机

微阵列数据分析中的一个关键步骤是从初始基因集中选择感兴趣的基因子集。在许多情况下,特别是在将特定条件与参考条件进行比较时,感兴趣的基因是那些差异表达的基因。两种常见的基因选择方法是:(a) 通过倍数差异(至少n倍变化)进行选择,以及(b) 通过变化比率(至少偏离平均比率n个标准差)进行选择。

结果

这里提出的新方法基于方差分析,并使用重复点来估计噪声的经验分布。将测量的强度范围划分为多个区间。为每个这样的区间构建一个噪声分布。使用自展法将对应于给定区间的噪声分布中的所需置信水平映射到该区间内测量的对数比率。如果将该方法应用于具有重复点的单个阵列,该方法可以计算噪声分布的总体宽度,其可作为阵列质量的指标。我们将此方法与倍数变化和异常比率方法进行了比较。我们还讨论了与Churchill等人提出的方差分析模型的关系。在控制调节程度以及噪声量的同时进行了计算机模拟实验。此类实验表明经典方法的性能可能非常不理想。我们还比较了2倍法的结果与使用在Affymetrix基因芯片阵列上杂交的源自MDAH041成纤维细胞的永生化前后细胞系的噪声采样方法的结果。2倍法报告198个基因上调,493个基因下调。噪声采样方法在99.99%置信水平下报告98个基因上调和240个基因下调。两种方法在221个基因下调和66个基因上调上达成一致。两种方法报告的基因子集中的14个基因均通过Q-RT-PCR得到证实。对两种方法存在分歧的各种基因子集进行的替代分析表明,噪声采样方法可能产生较少的假阳性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验