Bayesian DNA copy number analysis

doi:10.1186/1471-2105-10-10

BMC Bioinformatics
Volume 10

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Rancoita PM
Hutter M
Bertoni F
Kwee I

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Bertoni F
Kwee I
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Research article

Bayesian DNA copy number analysis

Paola MV Rancoita¹^,2^,3 , Marcus Hutter⁴ , Francesco Bertoni² and Ivo Kwee¹^,2

¹Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Galleria 2, 6928 Manno-Lugano, Switzerland

²Laboratory of Experimental Oncology, Oncology Institute of Southern Switzerland (IOSI), via Vela 6, 6500 Bellinzona, Switzerland

³Dipartimento di Matematica, Università degli Studi di Milano, via Saldini 50, 20137 Milano, Italy

⁴RSISE, ANU and SML, NICTA, Canberra, ACT, 0200, Australia

author email corresponding author email

BMC Bioinformatics 2009, 10:10doi:10.1186/1471-2105-10-10


Published:	8 January 2009

Abstract

Background

Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents.

Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments.

Results

We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data.

Conclusion

We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.