一种用于时间序列微阵列分析的改进直方图匹配归一化方法。

Microarrays Pub Date : 2014-07-01 DOI:10.3390/microarrays3030203

Laura Astola, Jaap Molenaar

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引用次数: 1

摘要

微阵列数据通常用于推断调控网络。分位数归一化(QN)是一种常用的减少数组间差异的方法。我们表明，在时间序列测量的背景下，QN可能不是这个任务的最佳选择，特别是如果推理是基于连续时间ODE模型的。我们提出了一种更适合于从时间序列数据进行网络推断的替代归一化方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

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A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

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来源期刊

Microarrays

自引率

0.00%

发文量

审稿时长

11 weeks

期刊介绍： High-Throughput (formerly Microarrays, ISSN 2076-3905) is a multidisciplinary peer-reviewed scientific journal that provides an advanced forum for the publication of studies reporting high-dimensional approaches and developments in Life Sciences, Chemistry and related fields. Our aim is to encourage scientists to publish their experimental and theoretical results based on high-throughput techniques as well as computational and statistical tools for data analysis and interpretation. The full experimental or methodological details must be provided so that the results can be reproduced. There is no restriction on the length of the papers. High-Throughput invites submissions covering several topics, including, but not limited to: Microarrays, DNA Sequencing, RNA Sequencing, Protein Identification and Quantification, Cell-based Approaches, Omics Technologies, Imaging, Bioinformatics, Computational Biology/Chemistry, Statistics, Integrative Omics, Drug Discovery and Development, Microfluidics, Lab-on-a-chip, Data Mining, Databases, Multiplex Assays.