A very fast and accurate method for calling aberrations in array-CGH data-Reference-Cited by-同舟云学术

A very fast and accurate method for calling aberrations in array-CGH data

Published:2010-07-01 Issue:3 Volume:11 Page:515-518
ISSN:1468-4357
Container-title:Biostatistics
language:en
Short-container-title:

Author:

Benelli Matteo¹,Marseglia Giuseppina¹,Nannetti Genni²,Paravidino Roberta³,Zara Federico⁴,Bricarelli Franca Dagna³,Torricelli Francesca⁵,Magi Alberto⁶

Affiliation:

1. Diagnostic Genetic Unit, Careggi Hospital, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence 50141, Italy and Center for the Study of Complex Dynamics, University of Florence, Florence 50019, Italy matteo.benelli@gmail.com

2. Diagnostic Genetic Unit, Careggi Hospital, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence 50141, Italy

3. Laboratory Of Genetics, Ente Ospedaliero Ospedali Galliera, Genova 16128, Italy

4. Muscular and Neurodegenerative Disease Unit, Institute Gaslini, University of Genova, Genova 16147, Italy

5. Diagnostic Genetic Unit, Careggi Hospital, Azienda Ospedaliera Universitaria Careggi, University of Florence, Florence 50141, Italy and Center for the Study of Complex Dynamics, University of Florence, Florence 50019, Italy

6. Diagnostic Genetic Unit, Careggi Hospital, Azienda Ospedaliera Universitaria Careggi, Department of Medical and Surgical Critical Care, University of Florence, Florence 50141, Italy and Center for the Study of Complex Dynamics, University of Florence, Florence 50019, Italy

Abstract

Abstract Array comparative genomic hybridization (aCGH) is a microarray technology that allows one to detect and map genomic alterations. The standard workflow of the aCGH data analysis consists of 2 steps: detecting the boundaries of the regions of changed copy number by means of a segmentation algorithm (break point identification) and then labeling each region as loss, neutral, or gain with a probabilistic framework (calling procedure). In this paper, we introduce a novel calling procedure based on a mixture of truncated normal distributions, named FastCall, that aims to give aberration probabilities to segmented aCGH data in a very fast and accurate way. Both on synthetic and real aCGH data, FastCall obtains excellent performances in terms of classification accuracy and running time.

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

http://academic.oup.com/biostatistics/article-pdf/11/3/515/36274833/biostatistics_11_3_515.pdf

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