Statistical modelling of transcript profiles of differentially regulated genes.
Statistical modelling of transcript profiles of differentially regulated genes.
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差异调节基因的转录谱的统计模型。
DOI：
10.1186/1471-2199-9-66
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发表时间：
2008-07-23
影响因子：
--
通讯作者：
Burton, Kerry S.
中科院分区：
文献类型：
--
作者：
Eastwood, Daniel C.;Mead, Andrew;Sergeant, Martin J.;Burton, Kerry S.
微阵列研究中产生的大量基因表达谱数据以及更精确的定量 PCR 通常没有充分发挥其潜力进行统计分析。先前的研究使用简单的描述性统计、基本方差分析 (ANOVA) 以及基于随时间拟合其表达谱的简单模型的基因聚类来总结基因表达谱。我们报告了统计非线性回归建模技术的新颖应用，用于描述真菌双孢蘑菇（通过 PCR 定量）以及大肠杆菌和褐家鼠（使用微阵列技术）的表达谱形状。参数非线性回归模型的使用提供了对表达谱的更精确的描述，减少了原始数据的“噪声”以产生由拟合曲线给出的清晰的“信号”，并用少量的生物学可解释的参数描述每个谱。然后，这种方法可以直接比较和聚类基因之间的响应模式的形状，并有可能更好地探索和解释驱动基因表达的生物过程。对定量逆转录酶 PCR 衍生的基因时程数据进行了建模。 “分割线”或“断棒”回归确定了基因上调的初始时间，从而能够将基因分类为具有初级反应和次级反应的基因。使用面向生物学的临界指数曲线 y(t) = A + (B + Ct)Rt + ε 对五天曲线进行建模。这种非线性回归方法允许在曲线形状、最大转录水平的时间以及下降和渐近响应水平方面比较不同基因的表达模式。对于所研究的五个基因，确定了三种不同的调控模式。将回归建模方法应用于微阵列衍生的时程数据，可以通过指数函数拟合 11% 的大肠杆菌特征，并通过临界指数模型描述 25% 的褐家鼠特征，所有这些都具有 p < 0.05 的统计显着性。本研究中提出的统计非线性回归方法使用生物变量数据生成一组定义参数，为个体基因表达谱提供了详细的生物导向描述。这些方法可应用于建模和对通过各种平台（例如微阵列）获得的概况进行更好的解释。通过仔细选择适当的模型形式，这种统计回归方法可以改进基因表达谱的比较，并可以提供一种更好地理解基因之间共同调控机制的方法。
The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA) and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Split-line" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t) = A + (B + Ct)Rt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data allowed 11% of the Escherichia coli features to be fitted by an exponential function, and 25% of the Rattus norvegicus features could be described by the critical exponential model, all with statistical significance of p < 0.05. The statistical non-linear regression approaches presented in this study provide detailed biologically oriented descriptions of individual gene expression profiles, using biologically variable data to generate a set of defining parameters. These approaches have application to the modelling and greater interpretation of profiles obtained across a wide range of platforms, such as microarrays. Through careful choice of appropriate model forms, such statistical regression approaches allow an improved comparison of gene expression profiles, and may provide an approach for the greater understanding of common regulatory mechanisms between genes.
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影响因子：
14.9
作者：
Liss, B
通讯作者：
Liss, B
DOI：
10.1198/016214505000000187
发表时间：
2006-03-01
影响因子：
3.7
作者：
Heard, NA;Holmes, CC;Stephens, DA
通讯作者：
Stephens, DA
影响因子：
--
作者：
Eastwood, DC;Kingsnorth, CS;Burton, KS
通讯作者：
Burton, KS
影响因子：
4.1
作者：
HAMMOND, JBW;NICHOLS, R
通讯作者：
NICHOLS, R
DOI：
10.1124/jpet.103.053256
发表时间：
2003-10-01
影响因子：
3.5
作者：
Jin, JY;Almon, RR;Jusko, WJ
通讯作者：
Jusko, WJ