An adaptation of the LMS method to determine expression variations in profiling data
An adaptation of the LMS method to determine expression variations in profiling data
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DOI：
10.1093/nar/gkm093
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发表时间：
2007-05-01
影响因子：
14.9
通讯作者：
Garcia-Sanz, Jose A.
中科院分区：
文献类型：
--
作者：
Chuchana, Paul;Marchand, Dorian;Garcia-Sanz, Jose A.
表达谱分析中的主要问题之一仍然是概述确定差异表达的适当阈值，同时避免假阳性。问题是方差与信号强度的对数成反比。为了解决这个问题，我们描述了一个模型，表达变异（EV），基于LMS方法，它允许数据归一化和构建基因表达的置信带，拟合三次样条曲线的Box -考克斯变换。拟合到数据的实际方差的置信带包括没有显著变化的基因，并且允许基于置信带宽来计算EV。根据分散空间（DS）定位每个离群值，并统计计算P值以确定EV。该模型的结果方差稳定。使用两个Affytek生成的数据集，比较使用EV和其他经典方法选择的差异表达基因的集合。分析表明，EV是更强大的方差稳定和选择从罕见和强表达基因的差异表达。
One of the major issues in expression profiling analysis still is to outline proper thresholds to determine differential expression, while avoiding false positives. The problem being that the variance is inversely proportional to the log of signal intensities. Aiming to solve this issue, we describe a model, expression variation ( EV), based on the LMS method, which allows data normalization and to construct confidence bands of gene expression, fitting cubic spline curves to the Box - Cox transformation. The confidence bands, fitted to the actual variance of the data, include the genes devoid of significant variation, and allow, based on the confidence bandwidth, to calculate EVs. Each outlier is positioned according to the dispersion space ( DS) and a P- value is statistically calculated to determine EV. This model results in variance stabilization. Using two Affymetrix- generated datasets, the sets of differentially expressed genes selected using EV and other classical methods were compared. The analysis suggests that EV is more robust on variance stabilization and on selecting differential expression from both rare and strongly expressed genes.