„Calibration of microarray hybridisation readout for genome-scale profiling of gene expression with improved
specificity“
Andreas Sommer
FH-HauptbetreuerIn:
Dr. Anton Beyer
1 Introduction
The quality of microarray measurements is affected by a multitude of parameters covering the entire process from
details of protocols in the wet lab to subsequent image analysis steps. Recent developments by members of the
Chair of Bioinformatics, University of Natural Resources and Applied Life Sciences, Vienna, have yielded a novel
quantitative approach for the comparative assessment of the specific information content of multisample hybridisation signals, allowing an informed adaptation of process parameters and the comparison of available technologies.
In this thesis, the method was used to evaluate the performance of four commercial microarray scanner models:
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Axon GenePix 4000A (Molecular Devices Corporation, Sunnyvale,CA, USA)
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Agilent G2565BA (Agilent Technologies, Santa Clara, CA, USA)
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Axon GenePix 4000B (Molecular Devices Corporation, Sunnyvale,CA, USA)
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Tecan LS Reloaded (Tecan Group Ltd., Grödig, Austria)
Additionally, we assayed the impact of dynamic range extension through multiple scans. The first and lower scan
was performed at the specific PMT(Photomultiplier)-gain at which the first spots started to show saturation. The
second scan was performed at highest possible PMT settings. Additionally, each slide was scanned in two orientations. First, using the standard alignment and second, after rotating the slide by 180°. The second scan mitigates
systematic spatial effects introduced by the scanner and enhance the statistical analysis by increasing the number
of analysed scans to 8 measurements per slide. After scanning, the data of the low and the high PMT scan were
combined by extrapolation of the saturated spots after calculating a linear regression using the scan at the lower
PMT-gain, thus creating a new combined data set..
Four commonly used normalisation methods were applied in order to asses the influence of normalisation on scanner performance:
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Location Normalisation: Adjustment of the mean intensity value for a series of experiments.
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Location and Scale Normalisation: Adjustment of mean values and scaling of standard deviation over the whole
intensity range.
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Quantile Normalisation: Forces microarray data of several experiments into the same distribution including adjustment to a general baseline.
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VSN: Variance Stabilizing Normalisation : Data transformation using a parametrised asinh function which renders variance approximately independent of the mean intensity.
2 Results
The Agilent scanner was found to perform best when comparing results for vsn and quantile normalisation.
Agilent's quantile normalised low PMT image yielded maximal sample separability information. The GenePix 4000B
resulted second when using the mentioned normalisation methods and performed better than the Agilent scanner
when applying location and location/scale normalisations. Independent of the normalisation method used, the Tecan scanner performed always worst.
Quantile normalisation produced best results for all scanners followed by vsn normalisation, while localisation normalisation, being the most conservative transformation, showed the smallest improvements. Quantile and vsn
normalisation also reduced significantly the median absolute deviation, indicating a potent calibration over all measurements.
While the combination of low and high PMT results would not yield any improvement when applied to untransformed data, better results were obtained when normalised data were analysed. Respecting the two less conservative
normalisations, quantile and vsn, the combined results showed in most cases (7 out of 8) an improvement over the
high PMT data and often (5 out of 8) an improvement over low and high data.
3 Discussion
In contrast to most published calibration studies we chose an approach that would quantify the information content
of microarray images. The applied method proved exceptionally useful in detecting performance differences of the
different scanner models.
The obtained results reveal strong performance differences between the four scanner models, favouring the Agilent
G2565BA scanner. Observed aberrations were discussed with scientists in charge of each scanner, leading to improved scanning settings.
The performance improvements yielded by four popular normalisation methods highlighted the importance of normalisation in microarray analysis. Quantile normalisation had the strongest positive impact on the data, followed
by variance stabilizing normalisation. As quantile normalisation is thought to be an aggressive transformation,
eventually distorting data structure, we recommend its use only in combination with careful data exploration,
whereas vsn normalisation could be more suitable for routine analysis.
Extending dynamic range by repeated scanning and combination of the data, resulted in many cases in performance improvement, this being especially true for the GenePix 4000B and the Tecan LS reloaded. However, the
fact that best results were not always achieved by data combination as well as the observation that low PMT scans
would yield more information than high PMT scans raised serious questions about scanner characteristics at high
PMT settings and will be adressed by future analysis.
In summary, this thesis presented an exemplary application of a calibration method based on sample separability
information. As platform establishment goes on at the host group, several parameters including hybridisation temperature, buffer compositions and process durations will be calibrated using the data analysis pipeline designed in
the course of this thesis.
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