基因组学研究中等效性的p值、q值和后验概率
P-values, q-values and posterior probabilities for equivalence in genomics studies
摘要：
等价性检验在基因组学研究中具有重要意义，但迄今为止这方面的研究很少。在本文中，我们定义了基因表达等效性的概念，并确定了基因等效性的“证据强度”度量。在全基因组研究中，通常的做法是根据观察到的基因特异性P值或调整后的P值对基因进行排名，这被认为是为了衡量针对无差异基因表达的原假设的证据强度。我们在这里表明，无论是经验和正式的，等效P值不满足基本的一致性要求的有效的证据强度措施的等效性。这意味着广泛使用的q值（Storey，2002）定义为每个基因的最小阳性错误发现率，这将导致在发现集中包含相应的P值，不能转化为等效性检验框架。然而，当表示为后验概率时，我们发现q值确实满足一些基本的一致性要求，这些要求是等效性证据的可信度量。我们提出了一个简单的估计的q值从后验概率的等效性，并分析数据从小鼠干细胞芯片实验，证明了这里提出的理论和方法。
英文摘要：
Equivalence testing is of emerging importance in genomics studies but has hitherto been little studied in this content. In this paper, we define the notion of equivalence of gene expression and determine a `strength of evidence' measure for gene equivalence. It is common practice in genome-wide studies to rank genes according to observed gene-specific P-values or adjusted P-values, which are assumed to measure the strength of evidence against the null hypothesis of no differential gene expression. We show here, both empirically and formally, that the equivalence P-value does not satisfy the basic consistency requirements for a valid strength of evidence measure for equivalence. This means that the widely-used q-value (Storey, 2002) defined for each gene to be the minimum positive false discovery rate that would result in the inclusion of the corresponding P-value in the discovery set, cannot be translated to the equivalence testing framework. However, when represented as a posterior probability, we find that the q-value does satisfy some basic consistency requirements needed to be a credible measure of evidence for equivalence. We propose a simple estimate for the q-value from posterior probabilities of equivalence, and analyse data from a mouse stem cell microarray experiment which demonstrate the theory and methods presented here.
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