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Bioinformatics strategies for cDNA-microarray data processing
Umeå University, Faculty of Social Sciences, Department of Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).
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2009 (English)In: Batch effects and noise in microarray experiments: sources and solutions / [ed] Scherer, Andreas, Wiley and Sons , 2009, 1, , 272 p.61-74 p.Chapter in book (Other academic)
Abstract [en]

Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper pre-processing 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 andits 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.

Place, publisher, year, edition, pages
Wiley and Sons , 2009, 1. , 272 p.61-74 p.
, Wiley series in probability and statistics
National Category
Computational Mathematics
Research subject
Mathematical Statistics
URN: urn:nbn:se:umu:diva-30827ISBN: 978-0-470-74138-2OAI: diva2:287318
Available from: 2010-01-18 Created: 2010-01-18 Last updated: 2015-04-29Bibliographically approved
In thesis
1. Essays on spatial point processes and bioinformatics
Open this publication in new window or tab >>Essays on spatial point processes and bioinformatics
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis consists of two separate parts. The first part consists of one paper and considers problems concerning spatial point processes and the second part includes three papers in the field of bioinformatics.

The first part of the thesis is based on a forestry problem of estimating the number of trees in a region by using the information in an aerial photo, showing the area covered by the trees. The positions of the trees are assumed to follow either a binomial point process or a hard-core Strauss process. Furthermore, discs of equal size are used to represent the tree-crowns. We provide formulas for the expectation and the variance of the relative vacancy for both processes. The formulas are approximate for the hard-core Strauss process. Simulations indicate that the approximations are accurate. 

The second part of this thesis focuses on pre-processing of microarray data. The microarray technology can be used to measure the expression of thousands of genes simultaneously in a single experiment. The technique is used to identify genes that are differentially expressed between two populations, e.g. diseased versus healthy individuals. This information can be used in several different ways, for example as diagnostic tools and in drug discovery.

The microarray technique involves a number of complex experimental steps, where each step introduces variability in the data. Pre-processing aims to reduce this variation and is a crucial part of the data analysis. Paper II gives a review over several pre-processing methods. Spike-in data are used to describe how the different methods affect the sensitivity and bias of the experi­ment.

An important step in pre-processing is dye-normalization. This normalization aims to re­move the systematic differences due to the use of different dyes for coloring the samples. In Paper III a novel dye-normalization, the MC-normalization, is proposed. The idea behind this normaliza­tion is to let the channels’ individual intensities determine the cor­rection, rather than the aver­age intensity which is the case for the commonly used MA-normali­zation. Spike-in data showed that  the MC-normalization reduced the bias for the differentially expressed genes compared to the MA-normalization.

The standard method for preserving patient samples for diagnostic purposes is fixation in formalin followed by embedding in paraffin (FFPE). In Paper IV we used tongue-cancer micro­RNA-microarray data to study the effect of FFPE-storage. We suggest that the microRNAs are not equally affected by the storage time and propose a novel procedure to remove this bias. The procedure improves the ability of the analysis to detect differentially expressed microRNAs.

Place, publisher, year, edition, pages
Umeå: Statistiska institutionen, 2010. 32 p.
Statistical studies, ISSN 1100-8989 ; 42
Coverage process, vacancy, microarray, pre-processing, sensitivity, bias, dye-normalization, FFPE, storage time effects
National Category
Probability Theory and Statistics
Research subject
urn:nbn:se:umu:diva-33452 (URN)978-91-7264-966-8 (ISBN)
Public defence
2010-05-21, Samhällsvetarhuset, hörsal D, Umeå universitet, Umeå, 10:00 (English)
Available from: 2010-04-29 Created: 2010-04-26 Last updated: 2010-04-29Bibliographically approved

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Fahlén, JessicaLandfors, MattiasFreyhult, EvaTrygg, JohanHvidsten, TorgeirRydén, Patrik
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