Meta-analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta-analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large-scale analysis of disease-associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta-analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi-tissue systems models of disease pathology using public data.

中文翻译：

---

疾病特征在组织和实验中都很强大。

结合基因表达微阵列实验的荟萃分析为单个实验中不明显的疾病分子病理生理学提供了新的见解。尽管已建立的微阵列技术可重复性可作为荟萃分析的基础，但实验间的病理生理学可重复性尚未得到很好的确立。在这项研究中，我们对从 NCBI GEO 获得的疾病相关实验进行了大规模分析，并评估了它们在广泛的疾病和组织类型中的一致性。在评估 429 个实验（代表来自 8435 个微阵列的 238 种疾病和 122 种组织）时，我们发现了测量相同疾病状况的实验之间普遍病理生理学一致性的证据。此外，我们发现跨组织疾病的分子特征总体上比跨疾病组织表达的特征更为突出。这些结果为使用病理生理指标的公共微阵列数据的质量提供了新的见解，并支持荟萃分析的新方向，包括描述疾病的共性（无论组织如何），以及创建疾病病理学的多组织系统模型使用公共数据。