Lowlevel analysis of microarray data
(2004) Abstract
 This thesis consists of an extensive introduction followed by seven papers (AF) on lowlevel analysis of microarray data. Focus is on calibration and normalization of observed data. The introduction gives a brief background of the microarray technology and its applications in order for anyone not familiar with the field to read the thesis. Formal definitions of calibration and normalization are given.
Paper A illustrates a typical statistical analysis of microarray data with background correction, normalization, and identification of differentially expressed genes (among thousands of candidates). A small analysis on the final results for different number of replicates and different image analysis software is also... (More)  This thesis consists of an extensive introduction followed by seven papers (AF) on lowlevel analysis of microarray data. Focus is on calibration and normalization of observed data. The introduction gives a brief background of the microarray technology and its applications in order for anyone not familiar with the field to read the thesis. Formal definitions of calibration and normalization are given.
Paper A illustrates a typical statistical analysis of microarray data with background correction, normalization, and identification of differentially expressed genes (among thousands of candidates). A small analysis on the final results for different number of replicates and different image analysis software is also given.
Paper B introduces a novel way for displaying microarray data called the printorder plot, which displays data in the order the corresponding spots were printed to the array. Utilizing these, so called (microtiter) plate effects are identified. Then, based on a simple variability measure for replicated spots across arrays, different normalization sequences are tested and evidence for the existence of plate effects are claimed.
Paper C presents an objectoriented extension with transparent reference variables to the R language. It is provides the necessary foundation in order to implement the microarray analysis package described in Paper F.
Paper D is on affine transformations of twochannel microarray data and their effects on the logratio logintensity transform. Affine transformations, that is, the existence of channel biases, can explain commonly observed intensitydependent effects in the logratios. In the light of the affine transformation, several normalization methods are revisited. At the end of the paper, a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis.
Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the imageanalysis methods introduce. Observed data strongly support this method. In addition, multiscancalibrated data has an extended dynamical range and higher signaltonoise levels. This is realworld evidence for the existence of affine transformations of microarray data.
Paper F describes the aroma package – An R Objectoriented Microarray Analysis environment – implemented in R and that provides easy access to our and others lowlevel analysis methods.
Paper G provides an calibration method for spotted microarrays with dilution series or spikeins. The method is based on a heteroscedastic affine stochastic model. The parameter estimates are robust against model misspecification. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/467374
 author
 Bengtsson, Henrik ^{LU}
 opponent

 Dr Huber, Wolfgang, Division of Molecular Genome Analysis, German Cancer Research Center, Heidelberg, Germany.
 organization
 publishing date
 2004
 type
 Thesis
 publication status
 published
 subject
 keywords
 programmering, aktuariematematik, Statistik, operationsanalys, Statistics, operations research, programming, actuarial mathematics
 pages
 257 pages
 publisher
 Centre for Mathematical Sciences, Lund University
 defense location
 Room MH:C, Centre for Mathematical Science, Lund Institute of Technology
 defense date
 20041001 10:15
 external identifiers

 scopus:16544386180
 ISBN
 9162862154
 language
 English
 LU publication?
 yes
 id
 262abc43bf654cb689ec80920d569d53 (old id 467374)
 date added to LUP
 20070925 20:10:00
 date last changed
 20170723 05:05:00
@phdthesis{262abc43bf654cb689ec80920d569d53, abstract = {This thesis consists of an extensive introduction followed by seven papers (AF) on lowlevel analysis of microarray data. Focus is on calibration and normalization of observed data. The introduction gives a brief background of the microarray technology and its applications in order for anyone not familiar with the field to read the thesis. Formal definitions of calibration and normalization are given.<br/><br> <br/><br> Paper A illustrates a typical statistical analysis of microarray data with background correction, normalization, and identification of differentially expressed genes (among thousands of candidates). A small analysis on the final results for different number of replicates and different image analysis software is also given.<br/><br> <br/><br> Paper B introduces a novel way for displaying microarray data called the printorder plot, which displays data in the order the corresponding spots were printed to the array. Utilizing these, so called (microtiter) plate effects are identified. Then, based on a simple variability measure for replicated spots across arrays, different normalization sequences are tested and evidence for the existence of plate effects are claimed.<br/><br> <br/><br> Paper C presents an objectoriented extension with transparent reference variables to the R language. It is provides the necessary foundation in order to implement the microarray analysis package described in Paper F.<br/><br> <br/><br> Paper D is on affine transformations of twochannel microarray data and their effects on the logratio logintensity transform. Affine transformations, that is, the existence of channel biases, can explain commonly observed intensitydependent effects in the logratios. In the light of the affine transformation, several normalization methods are revisited. At the end of the paper, a new robust affine normalization is suggested that relies on iterative reweighted principal component analysis.<br/><br> <br/><br> Paper E suggests a multiscan calibration method where each array is scanned at various sensitivity levels in order to uniquely identify the affine transformation of signals that the scanner and the imageanalysis methods introduce. Observed data strongly support this method. In addition, multiscancalibrated data has an extended dynamical range and higher signaltonoise levels. This is realworld evidence for the existence of affine transformations of microarray data.<br/><br> <br/><br> Paper F describes the aroma package – An R Objectoriented Microarray Analysis environment – implemented in R and that provides easy access to our and others lowlevel analysis methods.<br/><br> <br/><br> Paper G provides an calibration method for spotted microarrays with dilution series or spikeins. The method is based on a heteroscedastic affine stochastic model. The parameter estimates are robust against model misspecification.}, author = {Bengtsson, Henrik}, isbn = {9162862154}, keyword = {programmering,aktuariematematik,Statistik,operationsanalys,Statistics,operations research,programming,actuarial mathematics}, language = {eng}, pages = {257}, publisher = {Centre for Mathematical Sciences, Lund University}, school = {Lund University}, title = {Lowlevel analysis of microarray data}, year = {2004}, }