Dissecting complex transcriptional responses using pathway-level scores based on prior information.
Dissecting complex transcriptional responses using pathway-level scores based on prior information.
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DOI：
10.1186/1471-2105-8-s6-s6
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发表时间：
2007-09-27
影响因子：
3
通讯作者：
Boorsma A
中科院分区：
文献类型：
--
作者：
Bussemaker HJ;Ward LD;Boorsma A
使用 DNA 微阵列测量的全基因组 mRNA 表达变化模式通常是多种调控途径对细胞环境变化的反应的复杂叠加。使用先验信息，无论是关于每个基因编码的蛋白质的功能，还是关于调节因子和控制其表达的序列之间的物理相互作用，已成为剖析复杂转录反应的有力方法。我们回顾了两种不同的方法，将多个个体基因的噪声表达水平组合成强大的通路水平差异表达评分。第一个是基于预定义基因组内的基因表达水平分布与基因组中所有其他基因的表达水平分布之间的比较。第二个方法是根据序列信息或直接测量蛋白质-DNA 相互作用来估计全基因组调控网络连接的强度，并使用回归分析来估计基因调控途径的活性。详细解释了所使用的统计方法。通过避免对单个基因进行阈值化，基于先验信息的差异表达的通路水平分析可以比基因水平分析对基因表达的细微变化更加敏感。这些方法在技术上简单明了，产生的结果在生物学和统计学上都很容易解释。
The genomewide pattern of changes in mRNA expression measured using DNA microarrays is typically a complex superposition of the response of multiple regulatory pathways to changes in the environment of the cells. The use of prior information, either about the function of the protein encoded by each gene, or about the physical interactions between regulatory factors and the sequences controlling its expression, has emerged as a powerful approach for dissecting complex transcriptional responses. We review two different approaches for combining the noisy expression levels of multiple individual genes into robust pathway-level differential expression scores. The first is based on a comparison between the distribution of expression levels of genes within a predefined gene set and those of all other genes in the genome. The second starts from an estimate of the strength of genomewide regulatory network connectivities based on sequence information or direct measurements of protein-DNA interactions, and uses regression analysis to estimate the activity of gene regulatory pathways. The statistical methods used are explained in detail. By avoiding the thresholding of individual genes, pathway-level analysis of differential expression based on prior information can be considerably more sensitive to subtle changes in gene expression than gene-level analysis. The methods are technically straightforward and yield results that are easily interpretable, both biologically and statistically.