Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.
Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.
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DOI：
10.1371/journal.pone.0003911
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发表时间：
2008
期刊：
影响因子：
3.7
通讯作者：
De Las Rivas, Javier
中科院分区：
文献类型：
--
作者：
Prieto, Carlos;Risueno, Alberto;Fontanillo, Celia;De Las Rivas, Javier
使用全基因组微阵列分析基因表达数据是基因组研究中经常使用的一种技术，用于寻找共表达模式和定位共转录的基因组。然而，在全球“经济”规模上进行的大多数研究并不专注于人类样本，当它们对应于人类时，往往包括不同的数据集，将正常样本与疾病改变的样本混合在一起。此外，全基因组表达微阵列中存在的技术噪声是另一个众所周知的问题，许多时候没有用稳健的统计方法来解决，也没有提供对数据中的误差的估计。来自一组正常-健康组织的人类全基因组表达数据被用来构建一个自信的人类基因共表达网络，避免了病理和技术噪音。为了实现这一点，我们描述了一种新的方法，它结合了几种统计和计算策略：稳健的归一化和表达信号计算；通过参数和非参数方法获得的相关系数；随机交叉验证；以及对数据的统计精度和覆盖率的估计。所有这些方法都提供了一系列共表达数据集，在这些数据集中可以测量并调整误差水平。为了定义误差，通过分配给生物路径来计算真阳性的比率。这些结果提供了一个有信心的人类基因共表达网络，包括3,327个基因节点和15841个共表达链接，比较分析表明，与以前发表的数据集相比，有很好的改进。对子集核心网络的进一步功能分析，通过两种独立的方法验证，显示了共享共同转录因子的连贯的生物模块。该网络揭示了以明确定义的功能星座组织的共表达聚类图。该网络中的两个主要区域对应于参与核和线粒体代谢的基因，对其功能分配的研究表明，超过60%是管家和必需基因。该网络显示了新的未描述的基因关联，并允许根据一些未知的未分配基因与已知基因家族的相互作用将其放置在功能上下文中。识别稳定可靠的人类基因对基因共表达网络对于在基因组水平上揭示人类基因之间的相互作用和功能相关性是至关重要的。这项工作有助于实现这一目标，我们正在向科学界提供经过验证的人类基因共表达网络，以便对该网络或某些特定基因关联进行进一步分析。这些数据可以在http://bioinfow.dep.usal.es/coexpression/.上免费在线获得
Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global “omic” scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families. The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations. The data are available free online at http://bioinfow.dep.usal.es/coexpression/.
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影响因子：
7
作者：
Lee, HK;Hsu, AK;Pavlidis, P
通讯作者：
Pavlidis, P
影响因子：
5.3
作者：
Falvo, JV;Parekh, BS;Maniatis, T
通讯作者：
Maniatis, T
影响因子：
14.9
作者：
Barnes M;Freudenberg J;Thompson S;Aronow B;Pavlidis P
通讯作者：
Pavlidis P
影响因子：
3
作者：
Bader, GD;Hogue, CW
通讯作者：
Hogue, CW
影响因子：
4.8
作者：
Poree, Benoit;Kypriotou, Magdalini;Galera, Philippe
通讯作者：
Galera, Philippe