Send to

Choose Destination
See comment in PubMed Commons below
BMC Bioinformatics. 2013 Mar 5;14:84. doi: 10.1186/1471-2105-14-84.

Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples.

Author information

Genome Analysis Platform, CIC bioGUNE & CIBERehd, Technologic Park of Bizkaia, Building 502, 48160 Derio, Spain.



SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation.


In the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH.


On a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.

[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for BioMed Central Icon for PubMed Central
    Loading ...
    Support Center