Exploring the functional landscape of gene expression: directed search of large microarray compendia
Exploring the functional landscape of gene expression: directed search of large microarray compendia
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DOI：
10.1093/bioinformatics/btm403
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发表时间：
2007-10-15
期刊：
影响因子：
5.8
通讯作者：
Troyanskaya, Olga G.
中科院分区：
文献类型：
--
作者：
Hibbs, Matthew A.;Hess, David C.;Troyanskaya, Olga G.
动机：基因表达微阵列技术的日益可用性导致了数千个微阵列基因表达数据集的出版，这些数据集调查了各种生物条件。由于缺乏对整个简编进行快速、准确探索的方法，这一庞大的储存库仍未得到充分利用。结果：我们收集了包含大约2400个实验条件的酿酒酵母基因表达微阵列数据。我们分析了这个集合的功能覆盖率，并设计了一个上下文敏感的搜索算法，用于快速探索纲要。使用我们的系统的研究人员提供了一小组查询基因来建立生物搜索上下文;基于此查询，我们对每个数据集与上下文的相关性进行加权，并在这些加权数据集中识别与查询集共表达的其他基因。与以前的大型聚类方法相比，我们的方法在概括已知生物学时的准确性平均提高了273%。此外，我们发现，我们的搜索范式识别新的生物预测，可以通过进一步的实验验证。我们的方法为生物研究人员提供了以有助于得出结论和制定假设的方式探索现有微阵列数据的能力，我们认为这对研究界是非常宝贵的。
Motivation: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium. Results: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community.