Disease signatures are robust across tissues and experiments.
Disease signatures are robust across tissues and experiments.
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DOI：
10.1038/msb.2009.66
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发表时间：
2009
影响因子：
9.9
通讯作者：
Butte AJ
中科院分区：
文献类型：
--
作者：
Dudley JT;Tibshirani R;Deshpande T;Butte AJ
荟萃分析结合基因表达微阵列实验提供了新的见解，从个别实验不明显的疾病的分子病理生理学。尽管微阵列的技术重现性已被确立为荟萃分析的基础，但实验间的病理生理学重现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疾病和组织类型中的一致性。在评估429个实验，代表238种疾病和122个组织从8435微阵列，我们发现的证据一般，病理生理学一致性实验测量相同的疾病状况。此外，我们发现跨组织疾病的分子特征总体上比跨疾病的组织表达的特征更突出。这些结果提供了新的见解，公共微阵列数据的质量，使用病理生理学指标，并支持新的方向，在荟萃分析，包括表征疾病的共性，无论组织，以及创建多组织系统模型的疾病病理使用公共数据。
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.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
30.8
作者：
Segal, E;Friedman, N;Regev, A
通讯作者：
Regev, A
影响因子：
14.9
作者：
Parkinson H;Kapushesky M;Kolesnikov N;Rustici G;Shojatalab M;Abeygunawardena N;Berube H;Dylag M;Emam I;Farne A;Holloway E;Lukk M;Malone J;Mani R;Pilicheva E;Rayner TF;Rezwan F;Sharma A;Williams E;Bradley XZ;Adamusiak T;Brandizi M;Burdett T;Coulson R;Krestyaninova M;Kurnosov P;Maguire E;Neogi SG;Rocca-Serra P;Sansone SA;Sklyar N;Zhao M;Sarkans U;Brazma A
通讯作者：
Brazma A
来自多个微阵列实验的基因表达数据的荟萃分析的潜在变量方法。
影响因子：
3
作者：
Choi, Hyungwon;Shen, Ronglai;Chinnaiyan, Arul M;Ghosh, Debashis
通讯作者：
Ghosh, Debashis
影响因子：
64.8
作者：
Alizadeh, AA;Eisen, MB;Staudt, LM
通讯作者：
Staudt, LM