Limits...
CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations.

van Houte BP, Binsl TW, Hettling H, Pirovano W, Heringa J - BMC Genomics (2009)

Bottom Line: Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies.Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers.We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, the Netherlands.

ABSTRACT

Background: Array comparative genomic hybridization (aCGH) is a popular technique for detection of genomic copy number imbalances. These play a critical role in the onset of various types of cancer. In the analysis of aCGH data, normalization is deemed a critical pre-processing step. In general, aCGH normalization approaches are similar to those used for gene expression data, albeit both data-types differ inherently. A particular problem with aCGH data is that imbalanced copy numbers lead to improper normalization using conventional methods.

Results: In this study we present a novel method, called CGHnormaliter, which addresses this issue by means of an iterative normalization procedure. First, provisory balanced copy numbers are identified and subsequently used for normalization. These two steps are then iterated to refine the normalization. We tested our method on three well-studied tumor-related aCGH datasets with experimentally confirmed copy numbers. Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies. Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers.

Conclusion: We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers. This also leads to a more reliable assessment of aberrations. An R-package containing the implementation of CGHnormaliter is available at http://www.ibi.vu.nl/programs/cghnormaliterwww.

Show MeSH

Related in: MedlinePlus

Example where CGHnormaliter achieves better results than popLowess due to more sophisticated clustering of the intensity ratios. Calling results on a GIST tumor sample (sample 57) after (A) popLowess and (B) CGHnormaliter normalization are shown. FISH analyses revealed losses in 2 chromosomes (14 and 15). All losses are identified largely by both methods but CGHnormaliter scores better in specificity and precision. The figure format is explained under Figure 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2748095&req=5

Figure 4: Example where CGHnormaliter achieves better results than popLowess due to more sophisticated clustering of the intensity ratios. Calling results on a GIST tumor sample (sample 57) after (A) popLowess and (B) CGHnormaliter normalization are shown. FISH analyses revealed losses in 2 chromosomes (14 and 15). All losses are identified largely by both methods but CGHnormaliter scores better in specificity and precision. The figure format is explained under Figure 3.

Mentions: Results on the GIST dataset are summarized in Figure 2B. CGHnormaliter performs best on all evaluation criteria (sensitivity = 0.72, specificity = 0.94 and precision = 0.45). The method by Chen et al. is second best and scores 4 percentage points lower on average. Furthermore global-median and popLowess show similar performances but on average 10 percentage points lower than CGHnormaliter. In Figure 4 we further elucidate the differences in performance between popLowess and CGHnormaliter. For this tumor sample, losses were experimentally verified in 2 out of 24 chromosomes. Although both methods are able to identify a considerable part of them, CGHnormaliter scores considerably better in specificity and precision (plus 8 and 19 percentage points, respectively). This can be explained by the observation that popLowess identifies only a fraction of the 'true' normals as normals during its clustering step, so only normals with high M values are used for normalization. As a consequence the LOWESS regression line becomes too high and the normalized M values too low, leading to an overestimation of the number of losses. In CGHnormaliter the normals are better recognized, yielding a more accurate centralization of the M values. The difference between normalization methods in terms of the resulting M values is substantial though less pronounced relative to the ALL dataset (Table 2).


CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations.

van Houte BP, Binsl TW, Hettling H, Pirovano W, Heringa J - BMC Genomics (2009)

Example where CGHnormaliter achieves better results than popLowess due to more sophisticated clustering of the intensity ratios. Calling results on a GIST tumor sample (sample 57) after (A) popLowess and (B) CGHnormaliter normalization are shown. FISH analyses revealed losses in 2 chromosomes (14 and 15). All losses are identified largely by both methods but CGHnormaliter scores better in specificity and precision. The figure format is explained under Figure 3.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2748095&req=5

Figure 4: Example where CGHnormaliter achieves better results than popLowess due to more sophisticated clustering of the intensity ratios. Calling results on a GIST tumor sample (sample 57) after (A) popLowess and (B) CGHnormaliter normalization are shown. FISH analyses revealed losses in 2 chromosomes (14 and 15). All losses are identified largely by both methods but CGHnormaliter scores better in specificity and precision. The figure format is explained under Figure 3.
Mentions: Results on the GIST dataset are summarized in Figure 2B. CGHnormaliter performs best on all evaluation criteria (sensitivity = 0.72, specificity = 0.94 and precision = 0.45). The method by Chen et al. is second best and scores 4 percentage points lower on average. Furthermore global-median and popLowess show similar performances but on average 10 percentage points lower than CGHnormaliter. In Figure 4 we further elucidate the differences in performance between popLowess and CGHnormaliter. For this tumor sample, losses were experimentally verified in 2 out of 24 chromosomes. Although both methods are able to identify a considerable part of them, CGHnormaliter scores considerably better in specificity and precision (plus 8 and 19 percentage points, respectively). This can be explained by the observation that popLowess identifies only a fraction of the 'true' normals as normals during its clustering step, so only normals with high M values are used for normalization. As a consequence the LOWESS regression line becomes too high and the normalized M values too low, leading to an overestimation of the number of losses. In CGHnormaliter the normals are better recognized, yielding a more accurate centralization of the M values. The difference between normalization methods in terms of the resulting M values is substantial though less pronounced relative to the ALL dataset (Table 2).

Bottom Line: Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies.Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers.We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Integrative Bioinformatics VU (IBIVU), VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, the Netherlands.

ABSTRACT

Background: Array comparative genomic hybridization (aCGH) is a popular technique for detection of genomic copy number imbalances. These play a critical role in the onset of various types of cancer. In the analysis of aCGH data, normalization is deemed a critical pre-processing step. In general, aCGH normalization approaches are similar to those used for gene expression data, albeit both data-types differ inherently. A particular problem with aCGH data is that imbalanced copy numbers lead to improper normalization using conventional methods.

Results: In this study we present a novel method, called CGHnormaliter, which addresses this issue by means of an iterative normalization procedure. First, provisory balanced copy numbers are identified and subsequently used for normalization. These two steps are then iterated to refine the normalization. We tested our method on three well-studied tumor-related aCGH datasets with experimentally confirmed copy numbers. Results were compared to a conventional normalization approach and two more recent state-of-the-art aCGH normalization strategies. Our findings show that, compared to these three methods, CGHnormaliter yields a higher specificity and precision in terms of identifying the 'true' copy numbers.

Conclusion: We demonstrate that the normalization of aCGH data can be significantly enhanced using an iterative procedure that effectively eliminates the effect of imbalanced copy numbers. This also leads to a more reliable assessment of aberrations. An R-package containing the implementation of CGHnormaliter is available at http://www.ibi.vu.nl/programs/cghnormaliterwww.

Show MeSH
Related in: MedlinePlus