Lowlevel Analysis of Microarray Data
Henrik Bengtsson
Centre for Mathematical Sciences
Mathematical Statistics
Lund Institute of Technology
2004
ISBN 9162862154
LUTFMS10242004

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 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.


Keywords:




