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

van Dyk E, Reinders MJ, Wessels LF - Nucleic Acids Res. (2013)

Bottom Line: 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.Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Statistics group, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.

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.

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Related in: MedlinePlus

Probe-based versus event-based FDR control. Illustration on how controlling the probe-based FDR (expected proportion of detected probes that are false-positives) can introduce an unexpected proportion of focal events simply due to the presence of broad chromosomal recurring aberrations.
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gkt155-F4: Probe-based versus event-based FDR control. Illustration on how controlling the probe-based FDR (expected proportion of detected probes that are false-positives) can introduce an unexpected proportion of focal events simply due to the presence of broad chromosomal recurring aberrations.

Mentions: This methodology is different from that performed in GISTIC2.0. GISTIC2.0 regards each probe as an independent test (owing to the random permutation scheme) and uses the methodology proposed by Benjamini and Hochberg (16) to control the probe-based FDR (i.e. the proportion of false-positive probes). In contrast, ADMIRE performs event-based FDR, and this subtle, yet profound, difference is illustrated in Figure 4.Figure 4.


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

van Dyk E, Reinders MJ, Wessels LF - Nucleic Acids Res. (2013)

Probe-based versus event-based FDR control. Illustration on how controlling the probe-based FDR (expected proportion of detected probes that are false-positives) can introduce an unexpected proportion of focal events simply due to the presence of broad chromosomal recurring aberrations.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3643574&req=5

gkt155-F4: Probe-based versus event-based FDR control. Illustration on how controlling the probe-based FDR (expected proportion of detected probes that are false-positives) can introduce an unexpected proportion of focal events simply due to the presence of broad chromosomal recurring aberrations.
Mentions: This methodology is different from that performed in GISTIC2.0. GISTIC2.0 regards each probe as an independent test (owing to the random permutation scheme) and uses the methodology proposed by Benjamini and Hochberg (16) to control the probe-based FDR (i.e. the proportion of false-positive probes). In contrast, ADMIRE performs event-based FDR, and this subtle, yet profound, difference is illustrated in Figure 4.Figure 4.

Bottom Line: 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.Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Statistics group, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.

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.

Show MeSH
Related in: MedlinePlus