Statistical methods for gene set co-expression analysis.

Statistical methods for gene set co-expression analysis.
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DOI:
10.1093/bioinformatics/btp502
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发表时间:
2009-11-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
通讯作者:
Kendziorski C
Kendziorski C
中科院分区:
其他
文献类型:
--
作者:
Choi Y;Kendziorski C

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动机:微阵列实验的力量来自于识别在生物条件下差异调节的基因。到目前为止,差异调节最常被认为是指差异表达,并且有许多用于鉴定差异表达(DE)基因或基因集的有用方法。然而,这样的方法不能识别许多相关类别的差异调节基因。一个重要的例子涉及差异共表达(DC)基因。结果:我们提出了一种方法,基因集共表达分析(GSCA),以确定DC基因集。GSCA方法提供了一个错误发现率受控的有趣基因集列表,不要求基因在至少一种生物学条件下高度相关,并且很容易应用于来自单个或多个实验的数据,正如我们使用肺癌和糖尿病研究的数据所证明的那样。可用性:GSCA方法在R中实施,可在www.biostat.wisc.edu/GSCA/上查阅。联系方式:kendzior@biostat.wisc.edu补充信息:补充数据可从生物信息学在线网站获得。
Motivation: The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes. Results: We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes. Availability: The GSCA approach is implemented in R and available at www.biostat.wisc.edu/∼kendzior/GSCA/. Contact: kendzior@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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