Bioinformatic Strategies for cDNA-Microarray Data Processing. Fahln, J., Landfors, M., Freyhult, E., Bylesj, M., Trygg, J., Hvidsten, T. R, & Rydn, P. In Batch Effects and Noise in Microarray Experiments, pages 61–74. John Wiley & Sons, Ltd, Chichester, UK, October, 2009. ![Bioinformatic Strategies for cDNA-Microarray Data Processing [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper preprocessing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis, background correction, normalization, and filtering. Spike-in data are used to illustrate how different procedures affect the analytical ability to detect differentially expressed genes and estimate their regulation. The result shows that pre-processing has a major impact on both the experiment’s sensitivity and its bias. However, general recommendations are hard to give, since pre-processing consists of several actions that are highly dependent on each other. Furthermore, it is likely that pre-processing have a major impact on downstream analysis, such as clustering and classification, and pre-processing methods should be developed and evaluated with this in mind.
@incollection{scherer_bioinformatic_2009,
address = {Chichester, UK},
title = {Bioinformatic {Strategies} for {cDNA}-{Microarray} {Data} {Processing}},
isbn = {978-0-470-68598-3 978-0-470-74138-2},
url = {http://doi.wiley.com/10.1002/9780470685983.ch6},
abstract = {Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper preprocessing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis, background correction, normalization, and filtering. Spike-in data are used to illustrate how different procedures affect the analytical ability to detect differentially expressed genes and estimate their regulation. The result shows that pre-processing has a major impact on both the experiment’s sensitivity and its bias. However, general recommendations are hard to give, since pre-processing consists of several actions that are highly dependent on each other. Furthermore, it is likely that pre-processing have a major impact on downstream analysis, such as clustering and classification, and pre-processing methods should be developed and evaluated with this in mind.},
language = {en},
urldate = {2021-06-08},
booktitle = {Batch {Effects} and {Noise} in {Microarray} {Experiments}},
publisher = {John Wiley \& Sons, Ltd},
author = {Fahln, Jessica and Landfors, Mattias and Freyhult, Eva and Bylesj, Max and Trygg, Johan and Hvidsten, Torgeir R and Rydn, Patrik},
editor = {Scherer, Andreas},
month = oct,
year = {2009},
doi = {10.1002/9780470685983.ch6},
pages = {61--74},
}
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