Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Avinguda Diagonal, 648, Barcelona, 08028, Spain.
BMC Bioinformatics. 2019 Aug 27;20(1):441. doi: 10.1186/s12859-019-3008-x.
Although a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of "enough similarity") between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance-based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit.
In this work, this method is extended to establish the equivalence of any number of gene lists. Additionally, an algorithm to obtain the smallest equivalence limit that would allow equivalence between two or more lists to be declared is presented. This algorithm is at the base of an iterative method of graphic visualization to represent the most to least equivalent gene lists. These methods deal adequately with the problem of adjusting for multiple testing. The applicability of these techniques is illustrated in two typical situations: (i) a collection of cancer-related gene lists, suggesting which of them are more reasonable to combine -as claimed by the authors- and (ii) a collection of pathogenesis-based transcript sets, showing which of these are more closely related. The methods developed are available in the goProfiles Bioconductor package.
The method provides a simple yet powerful and statistically well-grounded way to classify a set of genes or other feature lists by establishing their equivalence at a given equivalence threshold. The classification results can be viewed using standard visualization methods. This may be applied to a variety of problems, from deciding whether a series of datasets generating the lists can be combined to the simplification of groups of lists.
虽然已经开发了一些基于基因列表生物学意义的比较方法，但 goProfiles 方法是少数用于此目的的方法之一。它由将基因列表投影到预先定义的基因本体论水平组成，以便可以使用多项式模型进行估计和测试。特别有趣的是，它可以用于证明两个列表之间的等效性（在“足够相似”的意义上），而不是证明它们之间的差异，这似乎在概念上更适合于建立基因列表之间相似性的最终目标。已经得出了一种使用基于距离的方法和置信区间包含原理的等效性方法。如果两个配置文件之间距离的单侧置信区间上限低于预先设定的等效极限，则声明等效性。
在这项工作中，该方法扩展到建立任意数量的基因列表的等效性。此外，还提出了一种算法来获得允许声明两个或更多列表之间等效性的最小等效极限。该算法是一种图形可视化迭代方法的基础，用于表示最等效和最不等效的基因列表。这些方法适当地解决了多重测试调整的问题。这些技术的适用性在两种典型情况下得到了说明：（i）一组与癌症相关的基因列表，建议其中哪些更合理-正如作者所声称的那样-和（ii）一组基于发病机制的转录组，显示哪些更密切相关。开发的方法可在 goProfiles Bioconductor 包中使用。
该方法提供了一种简单但强大且具有统计学依据的方法，通过在给定的等效阈值下建立它们的等效性来对一组基因或其他特征列表进行分类。分类结果可以使用标准可视化方法查看。这可以应用于各种问题，从决定是否可以组合生成列表的一系列数据集到简化列表组。
