遗传点阵列图像的鲁棒点拟合

Horng-Yang Chen, Norbert Brändle, H. Bischof, H. Lapp
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引用次数: 4

摘要

解决了在基因表达实验中获得的高密度点阵列图像的参数和半参数模型的可靠拟合问题。目的是测量特定地点遗传物质的数量。许多点可以用高斯形状精确地建模。为了处理高度重叠的点,作者使用了稳健的m估计量。当参数方法失效时,他们采用了一种新颖的、鲁棒的半参数方法,可以准确地处理不同形状的斑点。他们给出了实际数据的结果,并比较了两种方法的复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust spot fitting for genetic spot array images
Addresses the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in gene expression experiments. The goal is to measure the amount of genetic material at specific spot locations. Many spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots the authors use robust M-estimators. When the parametric method fails, they use a novel, robust semi-parametric method which can handle spots of different shapes accurately. They present the results for real data and compare the complexity of the two methods.
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