Minguez Pablo, Al-Shahrour Fátima, Dopazo Joaquín
Bioinformatics Department, Centro de Investigación Príncipe Felipe (CIPF), Autopista del Saler 16, E46013, Valencia, Spain.
Genome Inform. 2006;17(2):57-66.
The interpretation of microarray experiments is commonly addressed by means a two-step approach in which the relevant genes are firstly selected uniquely on the basis of their experimental values (ignoring their coordinate behaviors) and in a second step their functional properties are studied to hypothesize about the biological roles they are fulfilling in the cell. Recently, different methods (e.g. GSEA or FatiScan) have been proposed to study the coordinate behavior of blocks of functionally-related genes. These methods study the distribution of functional information across lists of genes ranked according their different experimental values in a static situation, such as the comparison between two classes (e.g. healthy controls versus diseased cases). Nevertheless there is no an equivalent way of studying a dynamic situation from a functional point of view. We present a method for the functional analysis of microarrays series in which the experiments display autocorrelation between successive points (e.g. time series, dose-response experiments, etc.) The method allows to recover the dynamics of the molecular roles fulfilled by the genes along the series which provides a novel approach to functional interpretation of such experiments. The method finds blocks of functionally-related genes which are significantly and coordinately over-expressed at different points of the series. This method draws inspiration from systems biology given that the analysis does not focus on individual properties of genes but on collective behaving blocks of functionally-related genes. The FatiScan algorithm used in the method proposed is available at: http://fatiscan.bioinfo.cipf.es}, or within the Babelomics suite: http://www.babelomics.org. Additional material is available at: http://bioinfo.cipf.es/data/plasmodium.
微阵列实验的解读通常采用两步法，即首先仅根据相关基因的实验值（忽略其协同行为）来唯一地选择这些基因，然后在第二步中研究它们的功能特性，以推测它们在细胞中所发挥的生物学作用。最近，已经提出了不同的方法（例如基因集富集分析（GSEA）或FatiScan）来研究功能相关基因块的协同行为。这些方法研究在静态情况下（例如两类之间的比较，如健康对照与患病病例）根据不同实验值排序的基因列表中功能信息的分布。然而，从功能角度来看，尚无研究动态情况的等效方法。我们提出了一种用于微阵列系列功能分析的方法，其中实验在连续点之间显示出自相关性（例如时间序列、剂量反应实验等）。该方法能够恢复基因在整个系列中所发挥的分子作用的动态变化，从而为这类实验的功能解读提供了一种新方法。该方法可找到在系列的不同点上显著且协同过度表达的功能相关基因块。鉴于该分析不关注基因的个体特性，而是关注功能相关基因的集体行为块，所以该方法从系统生物学中获得了灵感。所提出的方法中使用的FatiScan算法可在以下网址获取：http://fatiscan.bioinfo.cipf.es ；或者在Babelomics套件中获取：http://www.babelomics.org 。其他材料可在以下网址获取：http://bioinfo.cipf.es/data/plasmodium 。
