Capturing heterogeneity in gene expression studies by surrogate variable analysis.
Capturing heterogeneity in gene expression studies by surrogate variable analysis.
复制标题
通过替代变量分析捕获基因表达研究中的异质性。
DOI：
10.1371/journal.pgen.0030161
复制
发表时间：
2007-09
期刊：
影响因子：
4.5
通讯作者：
Storey, John D.
中科院分区：
文献类型：
--
作者：
Leek, Jeffrey T.;Storey, John D.
它已明确表明，遗传，环境，人口和技术因素可能对基因表达水平产生重大影响。除了感兴趣的测量变量之外，由于未知、未测量或过于复杂而无法通过简单模型捕获的因素，往往会有信号源。我们发现，未能将这些异质性来源纳入分析可能会对研究产生广泛和不利的影响。这不仅会降低功率或诱导不必要的跨基因依赖性，而且还会为许多基因引入虚假信号源。这种现象即使在设计良好的随机研究中也是如此。我们引入替代变量分析（SVA）来克服表达研究中异质性带来的问题。SVA可以与标准分析技术结合应用，以准确捕获表达与任何感兴趣的建模变量之间的关系。我们将SVA应用于疾病类别，时间进程和基因表达研究的遗传学。我们表明，SVA提高了全基因组表达研究中分析的生物学准确性和重现性。在科学和医学研究中，在收集数据以了解两个变量之间的关系时必须非常小心，例如药物及其对疾病的影响。在任何特定的研究中，都会有许多其他变量在起作用，例如年龄和性别对疾病的影响。我们发现，在同时测量数千个基因表达水平的研究中，这些问题变得令人惊讶地关键。由于我们的基因组，环境和人口统计特征的复杂性，在分析基因表达水平时有许多变异来源。在任何研究中，都不可能测量可能影响我们基因表达的每一个变量。尽管如此，我们表明，通过同时考虑所有的表达水平，人们实际上可以恢复这些重要的遗漏变量的影响，并基本上产生一个分析，如果所有相关变量都包括在内。与传统的研究相反，在这种情况下，大量的数据使得这种称为替代变量分析的方法成为可能。我们推测，替代变量分析将是有用的，在许多大规模的基因表达研究。
It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce “surrogate variable analysis” (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies. In scientific and medical studies, great care must be taken when collecting data to understand the relationship between two variables, such as a drug and its effect on a disease. In any given study there will be many other variables at play, such as the effects of age and sex on the disease. We show that in studies where the expression levels of thousands of genes are measured at once, these issues become surprisingly critical. Due to the complexity of our genomes, environment, and demographic features, there are many sources of variation when analyzing gene expression levels. In any given study, it is impossible to measure every single variable that may be influencing how our genes are expressed. Despite this, we show that by considering all expression levels simultaneously, one can actually recover the effects of these important missed variables and essentially produce an analysis as if all relevant variables were included. As opposed to traditional studies, the massive amount of data available in this setting is what makes the method, called surrogate variable analysis, possible. We hypothesize that surrogate variable analysis will be useful in many large-scale gene expression studies.
登录
查看更多内容
影响因子：
0.9
作者：
Cai, Gengqian;Sarkar, Sanat K.
通讯作者：
Sarkar, Sanat K.
影响因子：
2.1
作者：
Dabney, Alan R.;Storey, John D.
通讯作者：
Storey, John D.
DOI：
10.1073/pnas.0533805100
发表时间：
2003-03-04
影响因子：
11.1
作者：
Hedenfalk, I;Ringnér, M;Trent, J
通讯作者：
Trent, J
DOI：
10.1073/pnas.150242097
发表时间：
2000-07-18
影响因子：
11.1
作者：
Holter, NS;Mitra, M;Fedoroff, NV
通讯作者：
Fedoroff, NV
DOI：
10.1111/j.1467-9868.2005.00509.x
发表时间：
2005-01-01
影响因子：
5.8
作者：
Owen, AB
通讯作者：
Owen, AB