Space Oriented Rank-Based Data Integration
Space Oriented Rank-Based Data Integration
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DOI：
10.2202/1544-6115.1534
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发表时间：
2010-01-01
影响因子：
0.9
通讯作者：
Lin, Shili
中科院分区：
文献类型：
--
作者：
Lin, Shili
整合来自多个组学平台的数据已成为研究复杂系统和性状的主要挑战。为了集成来自多个平台的数据，排名靠前的元素所来自的底层空间可能是不同的。因此，明确考虑到底层空间是重要的，因为不这样做将导致数据使用效率低下，并可能产生偏差和/或次优结果。我们提出了两个面向空间的类的启发式算法集成排名表从omic规模的数据。这些算法要么是Borda启发的，要么是基于马尔可夫链的，它们明确地考虑了各个排名列表的底层空间。我们将这组算法应用于许多问题，其中包括一个旨在聚合来自三个cDNA和两个Affytek基因表达研究的结果，其中Affytek和cDNA平台之间的潜在空间明显不同。
Integration of data from multiple omics platforms has become a major challenge in studying complex systems and traits. For integrating data from multiple platforms, the underlying spaces from which the top ranked elements come from are likely to be different. Thus, taking the underlying spaces into consideration explicitly is important, as failure to do so would lead to inefficient use of data and might render biases and/or sub-optimal results. We propose two space oriented classes of heuristic algorithms for integrating ranked lists from omic scale data. These algorithms are either Borda inspired or Markov chain based that take the underlying spaces of the individual ranked lists into account explicitly. We applied this set of algorithms to a number of problems, including one that aims at aggregating results from three cDNA and two Affymetrix gene expression studies in which the underlying spaces between Affymetrix and cDNA platforms are clearly different.