Abstract
BackgroundIn gene expression studies a key role is played by the so called "pre-processing", a series of steps designed to extract the signal and account for the sources of variability due to the technology used rather than to biological differences between the RNA samples. At the moment there is no commonly agreed gold standard pre-processing method and each researcher has the responsibility to choose one method, incurring the risk of false positive and false negative features arising from the particular method chosen.ResultsWe propose a Bayesian calibration model that makes use of the information provided by several pre-processing methods and we show that this model gives a better assessment of the 'true' unknown differential expression between two conditions. We demonstrate how to estimate the posterior distribution of the differential expression values of interest from the combined information.ConclusionOn simulated data and on the spike-in Latin Square dataset from Affymetrix the Bayesian calibration model proves to have more power than each pre-processing method. Its biological interest is demonstrated through an experimental example on publicly available data.
Highlights
In gene expression studies, one of the first steps of the statistical analysis is to quantify the signal and correct the systematic noise through pre-processing, a series of actions designed to extract the signal and the sources of variability due to the technology used rather than to biological differences between the RNA samples
Bayesian calibration model A combined model for different pre-processing methods is characterised by two measurement error components: (i) a measure of the bias from the 'true' differential expression, that can assume additive or multiplicative form and (ii) a measure of variability around the mean gene expression
Complementary to this, we provide a quantitative measure of model fit, enabling a more direct comparison between different models by means of the Deviance Information Criterion (DIC) [28]
Summary
One of the first steps of the statistical analysis is to quantify the signal and correct the systematic noise through pre-processing, a series of actions designed to extract the signal and the sources of variability due to the technology used rather than to biological differences between the RNA samples. A simple alternative strategy is to perform the analysis using two different pre-processing methods and compare the results in terms of differential expression, focussing attention on the genes in the intersection. The former strategy is reductive while the latter relies on the arbitrary choice of two methods and on that of considering only their intersection. In gene expression studies a key role is played by the so called "pre-processing", a series of steps designed to extract the signal and account for the sources of variability due to the technology used rather than to biological differences between the RNA samples.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
