A scale-space method for detecting recurrent DNA copy number changes with analytical false discovery rate control.

Abstract:

:Tumor formation is partially driven by DNA copy number changes, which are typically measured using array comparative genomic hybridization, SNP arrays and DNA sequencing platforms. Many techniques are available for detecting recurring aberrations across multiple tumor samples, including CMAR, STAC, GISTIC and KC-SMART. GISTIC is widely used and detects both broad and focal (potentially overlapping) recurring events. However, GISTIC performs false discovery rate control on probes instead of events. Here we propose Analytical Multi-scale Identification of Recurrent Events, a multi-scale Gaussian smoothing approach, for the detection of both broad and focal (potentially overlapping) recurring copy number alterations. Importantly, false discovery rate control is performed analytically (no need for permutations) on events rather than probes. The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization. An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales. We perform extensive simulations and showcase its utility on a glioblastoma SNP array dataset. Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

van Dyk E,Reinders MJ,Wessels LF

doi

10.1093/nar/gkt155

subject

Has Abstract

pub_date

2013-05-01 00:00:00

pages

e100

issue

9

eissn

0305-1048

issn

1362-4962

pii

gkt155

journal_volume

41

pub_type

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