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Please note that, as of 18 July 2017, Microarrays has been renamed to High-Throughput and is now published here.
Open AccessArticle

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis

by Laura Astola 1,* and Jaap Molenaar 2,3
1
Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven 5612 AZ,The Netherlands
2
Biometris, Wageningen University and Research Centre, Wageningen 6708 PB, The Netherlands
3
Wageningen Centre for Systems Biology, Wageningen 6700 AC, The Netherlands
*
Author to whom correspondence should be addressed.
Microarrays 2014, 3(3), 203-211; https://doi.org/10.3390/microarrays3030203
Received: 24 March 2014 / Revised: 19 June 2014 / Accepted: 25 June 2014 / Published: 1 July 2014
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. View Full-Text
Keywords: quantile normalization; histogram matching; time series quantile normalization; histogram matching; time series
MDPI and ACS Style

Astola, L.; Molenaar, J. A New Modified Histogram Matching Normalization for Time Series Microarray Analysis. Microarrays 2014, 3, 203-211.

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