Gene set analyses for interpreting microarray experiments on prokaryotic organisms.
Gene set analyses for interpreting microarray experiments on prokaryotic organisms.
复制标题
基因设定分析用于解释原核生物的微阵列实验。
DOI：
10.1186/1471-2105-9-469
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发表时间：
2008-11-05
影响因子：
3
通讯作者：
Taylor, Ronald C.
中科院分区：
文献类型：
--
作者：
Tintle, Nathan L.;Best, Aaron A.;DeJongh, Matthew;Van Bruggen, Dirk;Heffron, Fred;Porwollik, Steffen;Taylor, Ronald C.
尽管DNA微阵列被广泛使用，但如何最好地解释它们测量的基因转录水平的财富仍然存在问题。最近，已经提出了使用生物学定义的基因组进行解释的方法，而不是逐个基因地检查结果。尽管有严重的局限性，基于Fisher精确检验的方法仍然是当实验重复次数很少时用于基因集分析的少数合理选择之一，这通常是原核生物的情况。我们扩展了五种基因集分析方法，从多个重复的实验中使用，用于重复较少的实验。然后，我们使用模拟和真实的数据来比较这些方法彼此之间，并与Fisher精确检验（FET）方法。作为模拟的结果，我们发现，一种名为MAXMEAN-NR的方法，保持了假阳性结果的名义率（I型错误率），同时提供了良好的统计能力和鲁棒性，以各种基因集分布的集大小至少为10。其他方法（ABSSUM-NR或SUM-NR）被证明是强大的集大小小于10。对三组实验数据的分析表明了相似的结果。此外，MAXMEAN-NR方法被证明能够检测生物学相关的设置为显着的，当其他方法（包括FET）不能。我们还发现，流行的GSEA-NR方法相比，MAXMEAN-NR表现不佳。MAXMEAN-NR是一种基因集分析方法，用于重复次数很少的实验，这在原核生物中很常见。模拟和真实的数据分析结果表明，与FET和其他方法相比，MAXMEAN-NR方法提供了更高的稳健性和结果的生物相关性，同时保持了标称I类错误率。
Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes. We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR. MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.
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影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
影响因子：
3.2
作者：
Bausch, C;Ramsey, M;Conway, T
通讯作者：
Conway, T
影响因子：
12.3
作者：
Levine, David M.;Haynor, David R.;Johnson, Jason M.
通讯作者：
Johnson, Jason M.
影响因子：
5.8
作者：
Al-Shahrour, F;Díaz-Uriarte, R;Dopazo, J
通讯作者：
Dopazo, J
影响因子：
3
作者：
Kim, SY;Volsky, DJ
通讯作者：
Volsky, DJ