Reverter Antonio, Chan Eva K F
CSIRO Livestock Industries, Queensland Bioscience Precinct, 306 Carmody Road, Brisbane, Queensland 4067, Australia.
Bioinformatics. 2008 Nov 1;24(21):2491-7. doi: 10.1093/bioinformatics/btn482. Epub 2008 Sep 10.
We present PCIT, an algorithm for the reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN. The properties of PCIT are examined in the context of the topology of the reconstructed network including connectivity structure, clustering coefficient and sensitivity.
We apply PCIT to a series of simulated datasets with varying levels of complexity in terms of number of genes and experimental conditions, as well as to three real datasets. Results show that, as opposed to the constant cutoff approach commonly used in the literature, the PCIT algorithm can identify and allow for more moderate, yet not less significant, estimates of correlation (r) to still establish a connection in the GCN. We show that PCIT is more sensitive than established methods and capable of detecting functionally validated gene-gene interactions coming from absolute r values as low as 0.3. These bona fide associations, which often relate to genes with low variation in expression patterns, are beyond the detection limits of conventional fixed-threshold methods, and would be overlooked by studies relying on those methods.
FORTRAN 90 source code to perform the PCIT algorithm is available as Supplementary File 1.
我们提出了PCIT，一种用于重建基因共表达网络（GCN）的算法，该算法将偏相关系数的概念与信息论相结合，以识别在GCN重建中定义边的显著基因间关联。在重建网络的拓扑结构（包括连通性结构、聚类系数和敏感性）的背景下研究了PCIT的特性。
我们将PCIT应用于一系列在基因数量和实验条件方面具有不同复杂程度的模拟数据集，以及三个真实数据集。结果表明，与文献中常用的固定截止方法不同，PCIT算法可以识别并允许对相关性（r）进行更适度但并非不显著的估计，以便在GCN中仍然建立连接。我们表明，PCIT比已建立的方法更敏感，能够检测出来自低至0.3的绝对r值的功能验证基因间相互作用。这些真实关联通常涉及表达模式变化较小的基因，超出了传统固定阈值方法的检测范围，并且会被依赖这些方法的研究所忽略。
执行PCIT算法的FORTRAN 90源代码作为补充文件1提供。
