Determination of strongly overlapping signaling activity from microarray data.
Determination of strongly overlapping signaling activity from microarray data.
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DOI：
10.1186/1471-2105-7-99
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发表时间：
2006-02-28
影响因子：
3
通讯作者：
Ochs MF
中科院分区：
文献类型：
--
作者：
Bidaut G;Suhre K;Claverie JM;Ochs MF
由于许多疾病都涉及信号转导错误，现代疗法通常针对参与细胞信号转导的蛋白质。解释疾病发展或治疗干预期间信号通路的活性将有助于药物开发、治疗设计和靶标识别。微阵列提供了细胞反应的全局测量，然而将这些反应与信号通路联系起来需要一种适合基础生物学的分析方法。微阵列模式识别中的一个持续存在的问题是如何确定用于数据解释的模式（或簇）的数量，这是一个关键问题，因为基因本体或通路中统计显着性的测量依赖于将基因正确分成组。在这里，我们介绍了一种依赖于基因注释与全局基因表达数据的分解分析相结合的方法，该方法使我们能够估计强耦合信号通路的特定活性，以及在某些情况下特定信号蛋白的活性。我们使用 Rosetta 酵母缺失突变体数据集、贝叶斯分解进行分解分析以及使用 ClutrFree 进行注释分析来演示该技术。我们通过对跨多个潜在维度的模式中基因持久性的测量确定，15 个基向量为解释数据提供了正确的维度。利用基因本体论和酵母基因组数据库中的基因调控数据，我们确定了酵母中几种细胞过程的转录特征，包括细胞壁形成、核糖体破坏、蛋白质合成的化学阻断，以及重要的是，强耦合交配和丝化途径的个体特征。这项工作表明，微阵列数据可以通过使用基因本体或转录因子数据库来提供通路活性的下游指标。这可用于研究靶向治疗的特异性和成功性，以及阐明正常和疾病过程中的信号传导活动。
As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups. Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, criticially, individual signatures of the strongly coupled mating and filamentation pathways. This works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.
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影响因子：
56.9
作者：
Carninci, P;Kasukawa, T;Hayashizaki, Y
通讯作者：
Hayashizaki, Y
DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
7
作者：
Griffin, TJ;Gygi, SP;Aebersold, R
通讯作者：
Aebersold, R
影响因子：
14.9
作者：
Christie, KR;Weng, S;Cherry, JM
通讯作者：
Cherry, JM