Abstract:
BACKGROUND:MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample. RESULTS:Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace. CONCLUSION:Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
González JR,Carrasco JL,Armengol L,Villatoro S,Jover L,Yasui Y,Estivill Xdoi
10.1186/1471-2105-9-261subject
Has Abstractpub_date
2008-06-04 00:00:00pages
261issn
1471-2105pii
1471-2105-9-261journal_volume
9pub_type
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