Modern high-throughput technologies like DNA microarrays are powerful
tools that are widely used in biomedical research. They target a
variety of genomics applications ranging from gene expression
profiling over DNA genotyping to gene regulation studies. However, the
recent discovery of false positives among prominent research findings
indicates a lack of awareness or understanding of the non-biological
factors negatively affecting the accuracy of data produced using these
technologies. The aim of this thesis is to study the origins, effects
and potential correction methods for selected methodical biases in
microarray data.
The two-species Langmuir model serves as the basal physicochemical
model of microarray hybridization describing the fluorescence signal
response of oligonucleotide probes. The so-called hook method allows
to estimate essential model parameters and to compute summary
parameters characterizing a particular microarray sample. We show that
this method can be applied successfully to various types of
microarrays which share the same basic mechanism of multiplexed
nucleic acid hybridization.
Using appropriate modifications of the model we study RNA quality and
sequence effects using publicly available data from Affymetrix
GeneChip expression arrays. Varying amounts of hybridized RNA result
in systematic changes of raw intensity signals and appropriate
indicator variables computed from these. Varying RNA quality strongly
affects intensity signals of probes which are located at the 3\' end of
transcripts. We develop new methods that help assessing the RNA
quality of a particular microarray sample. A new metric for
determining RNA quality, the degradation index, is proposed which
improves previous RNA quality metrics. Furthermore, we present a
method for the correction of the 3\' intensity bias. These
functionalities have been implemented in the freely available program
package AffyRNADegradation.
We show that microarray probe signals are affected by sequence effects
which are studied systematically using positional-dependent
nearest-neighbor models. Analysis of the resulting sensitivity
profiles reveals that specific sequence patterns such as runs of
guanines at the solution end of the probes have a strong impact on the
probe signals. The sequence effects differ for different chip- and
target-types, probe types and hybridization modes. Theoretical and
practical solutions for the correction of the introduced sequence bias
are provided.
Assessment of RNA quality and sequence biases in a representative
ensemble of over 8000 available microarray samples reveals that RNA
quality issues are prevalent: about 10% of the samples have
critically low RNA quality. Sequence effects exhibit considerable
variation within the investigated samples but have limited impact on
the most common patterns in the expression space. Variations in RNA
quality and quantity in contrast have a significant impact on the
obtained expression measurements.
These hybridization biases should be considered and controlled in
every microarray experiment to ensure reliable results. Application of
rigorous quality control and signal correction methods is strongly
advised to avoid erroneous findings. Also, incremental refinement of
physicochemical models is a promising way to improve signal
calibration paralleled with the opportunity to better understand the
fundamental processes in microarray hybridization.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:15-qucosa-116957 |
| Date | 01 July 2013 |
| Creators | Fasold, Mario |
| Contributors | Universität Leipzig, Fakultät für Mathematik und Informatik, PD Dr. rer. nat. habil. Hans Binder, Professor Dr. Peter Stadler, Dr. Andrew Harrison |
| Publisher | Universitätsbibliothek Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf |
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