Cross-platform microarray data normalisation for regulatory network inference.
Cross-platform microarray data normalisation for regulatory network inference.
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用于监管网络推理的跨平台微阵列数据标准化。
DOI：
10.1371/journal.pone.0013822
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发表时间：
2010-11-12
期刊：
影响因子：
3.7
通讯作者：
Crane M
中科院分区：
文献类型：
--
作者：
Sîrbu A;Ruskin HJ;Crane M
Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences. We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets. Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.
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DOI：
10.1073/pnas.011404098
发表时间：
2001-01-02
影响因子：
11.1
作者：
Li, C;Wong, WH
通讯作者：
Wong, WH
影响因子：
64.8
作者：
Orlando, David A.;Lin, Charles Y.;Haase, Steven B.
通讯作者：
Haase, Steven B.
影响因子：
10.5
作者：
Pramila, Tata;Wu, Wei;Breeden, Linda L.
通讯作者：
Breeden, Linda L.
影响因子：
--
作者：
Fomekong-Nanfack, Yves;Postma, Marten;Kaandorp, Jaap A.
通讯作者：
Kaandorp, Jaap A.
影响因子：
3.3
作者：
Spellman, PT;Sherlock, G;Futcher, B
通讯作者：
Futcher, B