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Non-specific filtering of beta-distributed data.

Wang X, Laird PW, Hinoue T, Groshen S, Siegmund KD - BMC Bioinformatics (2014)

Bottom Line: Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data.We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions.We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, 2001 N Soto Street, Suite 202W, Los Angeles 90089-9239 California, USA. kims@usc.edu.

ABSTRACT

Background: Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias.

Results: We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets.

Conclusions: We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered.

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Related in: MedlinePlus

Smoothed scatter plots showing six filter statistics vs. the mean DNA methylation (Beta) value (22198 features, 26 colon cancer samples). A. SD-b: standard deviation of Beta values; B. SD-m: standard deviation of M-values; C. 1/Precision: inverse of precision parameter; D. BQ-GOF: Beta Quantile Goodness-Of-Fit; E. TM-GOF: Transformed Moment Goodness-Of-Fit; F. TQ-GOF: Transformed Quantile Goodness-Of-Fit. Red line in each figure indicates the median statistic values.
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Figure 1: Smoothed scatter plots showing six filter statistics vs. the mean DNA methylation (Beta) value (22198 features, 26 colon cancer samples). A. SD-b: standard deviation of Beta values; B. SD-m: standard deviation of M-values; C. 1/Precision: inverse of precision parameter; D. BQ-GOF: Beta Quantile Goodness-Of-Fit; E. TM-GOF: Transformed Moment Goodness-Of-Fit; F. TQ-GOF: Transformed Quantile Goodness-Of-Fit. Red line in each figure indicates the median statistic values.

Mentions: Figure 1 shows the relationships between six filter statistics and mean DNA methylation level in a study of 26 colon cancer tissues. In this collection of heterogeneous cancers (23% CIMP and 77% non-CIMP), we see a strong relationship between standard deviation and the mean value (Figure 1A), and selecting features with high variation (SD-b) biases the selection to those with mean near 0.5. This relationship is reduced for alternate filter statistics (Figure 1B-1E). Therefore, depending on the filter statistic employed, a different set of top ranked features may be retained for statistical evaluation.


Non-specific filtering of beta-distributed data.

Wang X, Laird PW, Hinoue T, Groshen S, Siegmund KD - BMC Bioinformatics (2014)

Smoothed scatter plots showing six filter statistics vs. the mean DNA methylation (Beta) value (22198 features, 26 colon cancer samples). A. SD-b: standard deviation of Beta values; B. SD-m: standard deviation of M-values; C. 1/Precision: inverse of precision parameter; D. BQ-GOF: Beta Quantile Goodness-Of-Fit; E. TM-GOF: Transformed Moment Goodness-Of-Fit; F. TQ-GOF: Transformed Quantile Goodness-Of-Fit. Red line in each figure indicates the median statistic values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4230495&req=5

Figure 1: Smoothed scatter plots showing six filter statistics vs. the mean DNA methylation (Beta) value (22198 features, 26 colon cancer samples). A. SD-b: standard deviation of Beta values; B. SD-m: standard deviation of M-values; C. 1/Precision: inverse of precision parameter; D. BQ-GOF: Beta Quantile Goodness-Of-Fit; E. TM-GOF: Transformed Moment Goodness-Of-Fit; F. TQ-GOF: Transformed Quantile Goodness-Of-Fit. Red line in each figure indicates the median statistic values.
Mentions: Figure 1 shows the relationships between six filter statistics and mean DNA methylation level in a study of 26 colon cancer tissues. In this collection of heterogeneous cancers (23% CIMP and 77% non-CIMP), we see a strong relationship between standard deviation and the mean value (Figure 1A), and selecting features with high variation (SD-b) biases the selection to those with mean near 0.5. This relationship is reduced for alternate filter statistics (Figure 1B-1E). Therefore, depending on the filter statistic employed, a different set of top ranked features may be retained for statistical evaluation.

Bottom Line: Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data.We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions.We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Preventive Medicine, USC Keck School of Medicine, University of Southern California, 2001 N Soto Street, Suite 202W, Los Angeles 90089-9239 California, USA. kims@usc.edu.

ABSTRACT

Background: Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias.

Results: We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets.

Conclusions: We found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered.

Show MeSH
Related in: MedlinePlus