A low false discovery rate at detection of copy-number aberrations in microarray data
Sprache des Titels:
HGV 2011 Proceedings
A low false discovery rate (FDR) at the detection of copy-number aberrations
(CNAs) in microarray data ensures sufficient detection power and prevents failures in CNAdisease
association studies. A high FDR means many falsely discovered aberrations, which are
not associated with the disease, though correction for multiple testing must take them into
account. Thus, a high FDR not only decreases the discovery power of studies but also the
significance level of the remaining discoveries after correction for multiple testing.
Methods: We obtain a low FDR at the detection of CNAs in microarray data by a probabilistic
latent variable model, called 'cn.FARMS'. The model is optimized by Bayesian maximum a
posteriori approach, where a Laplace prior prefers models, which represent the null hypothesis
of observing a constant copy number 2 for all samples. The posterior can only deviate from this
prior by strong (deviation from copy number 2 intensities) and consistent signals in the data,
which hints at a CNA - the alternative hypothesis. The information gain of the posterior over the
prior gives the informative/non-informative (I/NI) call that serves as a filter for CNA candidate
regions. I/NI call filtering reduces the FDR, because a region with a large I/NI call is unlikely to
be a falsely detected CNA, which would neither have strong nor consistent measurements. It
can be shown that the I/NI call filter applied to null hypotheses of the association study is
independent of the test statistic which in turn guarantees that a type I error rate control by
correction for multiple testing is still possible after filtering. I/NI-calls perform well for the usually
rare CNAs that are seen at few samples only, where variance-based filtering approaches fail.
Results: cn.FARMS clearly outperformed prevalent methods for CNA detection with respect to
sensitivity and especially with respect to FDR on different HapMap benchmark data sets.