Evaluating the differential expression of a set of genes belonging to a common biological process or ontology has proven to be a very useful tool for biological discovery. However, existing gene-set association methods are limited to applications that evaluate differential expression across k ≥ 2 treatment groups or biological categories. This limitation precludes researchers from most effectively evaluating the association with other phenotypes that may be more clinically meaningful, such as quantitative variables or censored survival time variables. Projection onto the Orthogonal Space Testing (POST) is proposed as a general procedure that can robustly evaluate the association of a gene-set with several different types of phenotypic data (categorical, ordinal, continuous, or censored). For each gene-set, POST transforms the gene profiles into a set of eigenvectors and then uses statistical modeling to compute a set of z-statistics that measure the association of each eigenvector with the phenotype. The overall gene-set statistic is the sum of squared z-statistics weighted by the corresponding eigenvalues. Finally, bootstrapping is used to compute a p-value. POST may evaluate associations with or without adjustment for covariates. In simulation studies, it is shown that the performance of POST in evaluating the association with a categorical phenotype is similar to or exceeds that of existing methods. In evaluating the association of 875 biological processes with the time to relapse of pediatric acute myeloid leukemia, POST identified the well-known oncogenic WNT signaling pathway as its top hit. These results indicate that POST can be a very useful tool for evaluating the association of a gene-set with a variety of different phenotypes.

中文翻译：

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POST：高维数据中基于集合的关联分析框架

评估属于共同生物过程或本体的一组基因的差异表达已被证明是一种非常有用的生物发现工具。然而，现有的基因集关联方法仅限于评估跨 k ≥ 2 个治疗组或生物学类别的差异表达的应用。这种限制使研究人员无法最有效地评估与可能更具有临床意义的其他表型的关联，例如定量变量或截尾生存时间变量。投影到正交空间测试 (POST) 被提议作为一种通用程序，可以稳健地评估基因集与几种不同类型的表型数据（分类、有序、连续或审查）的关联。对于每个基因集，POST 将基因图谱转换为一组特征向量，然后使用统计建模来计算一组 z 统计量，用于测量每个特征向量与表型的关联。整体基因集统计量是由相应特征值加权的平方 z 统计量的总和。最后，自举用于计算 p 值。POST 可以在调整或不调整协变量的情况下评估关联。在模拟研究中，表明 POST 在评估与分类表型的关联方面的性能类似于或超过现有方法的性能。在评估 875 个生物过程与小儿急性髓系白血病复发时间的关联时，POST 确定众所周知的致癌 WNT 信号通路是其首要目标。