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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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(A) A representative plot of the power for detecting a recurring aberration as a function of the aberration size and kernel width for the SNR fixed at 1. In this experiment, we added only a single recurring aberration per experiment and fixed  at 5%. The black line indicates the maximum allowed kernel width at which an aberration can be detected if we apply filtering with  in the multi-scale methodology. See Supplementary Figure S3 for similar plots at different SNRs. (B) The empirical FWER. The green regions indicate that the measured FWER is within 1 standard deviation of the expected 5% FWER.
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gkt155-F6: (A) A representative plot of the power for detecting a recurring aberration as a function of the aberration size and kernel width for the SNR fixed at 1. In this experiment, we added only a single recurring aberration per experiment and fixed at 5%. The black line indicates the maximum allowed kernel width at which an aberration can be detected if we apply filtering with in the multi-scale methodology. See Supplementary Figure S3 for similar plots at different SNRs. (B) The empirical FWER. The green regions indicate that the measured FWER is within 1 standard deviation of the expected 5% FWER.

Mentions: Figure 6.A depicts a typical power plot as a function of aberration size and kernel width—for an elaborate collection of these plots for different SNRs, see Supplementary Figure S3. This plot shows how the power changes (for the analytical FWER fixed at 5%) for detecting recurring aberrations of different sizes (one event per simulation) while varying the kernel width. We can observe that for a fixed kernel width, the power decreases as the aberration size decreases. In fact, there is an abrupt drop in power when the aberration size equals the kernel width, as indicated by the diagonal ridge in the panel. In general, we can conclude that as long as the aberration is larger than the kernel width (region above the diagonal line), we have more power to detect the aberration. Figure 6B shows that the measured FWER (the chance of detecting one or more false-positives) is close to that predicted by , as expected. From these simulations it is clear that for any recurrent aberration of a fixed width, a fixed kernel width can be selected to gain optimal power. If the kernel width becomes too large, we observe a drastic loss in power, as indicated by the lower right corner in Figure 6A. Note that in contrast, Figure 2 suggests that larger kernels increase the power, but if we extend Figure 2 to show even larger kernels, the significance levels will drop drastically.Figure 6.


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)

(A) A representative plot of the power for detecting a recurring aberration as a function of the aberration size and kernel width for the SNR fixed at 1. In this experiment, we added only a single recurring aberration per experiment and fixed  at 5%. The black line indicates the maximum allowed kernel width at which an aberration can be detected if we apply filtering with  in the multi-scale methodology. See Supplementary Figure S3 for similar plots at different SNRs. (B) The empirical FWER. The green regions indicate that the measured FWER is within 1 standard deviation of the expected 5% FWER.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt155-F6: (A) A representative plot of the power for detecting a recurring aberration as a function of the aberration size and kernel width for the SNR fixed at 1. In this experiment, we added only a single recurring aberration per experiment and fixed at 5%. The black line indicates the maximum allowed kernel width at which an aberration can be detected if we apply filtering with in the multi-scale methodology. See Supplementary Figure S3 for similar plots at different SNRs. (B) The empirical FWER. The green regions indicate that the measured FWER is within 1 standard deviation of the expected 5% FWER.
Mentions: Figure 6.A depicts a typical power plot as a function of aberration size and kernel width—for an elaborate collection of these plots for different SNRs, see Supplementary Figure S3. This plot shows how the power changes (for the analytical FWER fixed at 5%) for detecting recurring aberrations of different sizes (one event per simulation) while varying the kernel width. We can observe that for a fixed kernel width, the power decreases as the aberration size decreases. In fact, there is an abrupt drop in power when the aberration size equals the kernel width, as indicated by the diagonal ridge in the panel. In general, we can conclude that as long as the aberration is larger than the kernel width (region above the diagonal line), we have more power to detect the aberration. Figure 6B shows that the measured FWER (the chance of detecting one or more false-positives) is close to that predicted by , as expected. From these simulations it is clear that for any recurrent aberration of a fixed width, a fixed kernel width can be selected to gain optimal power. If the kernel width becomes too large, we observe a drastic loss in power, as indicated by the lower right corner in Figure 6A. Note that in contrast, Figure 2 suggests that larger kernels increase the power, but if we extend Figure 2 to show even larger kernels, the significance levels will drop drastically.Figure 6.

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