Robust regression analysis of copy number variation data based on a univariate score.
The discovery that copy number variants (CNVs) are widespread in the human genome has motivated development of numerous algorithms that attempt to detect CNVs from intensity data. However, all approaches are plagued by high false discovery rates. Further, because CNVs are characterized by two dimens...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3917847?pdf=render |
| _version_ | 1828844933271781376 |
|---|---|
| author | Glen A Satten Andrew S Allen Morna Ikeda Jennifer G Mulle Stephen T Warren |
| author_facet | Glen A Satten Andrew S Allen Morna Ikeda Jennifer G Mulle Stephen T Warren |
| author_sort | Glen A Satten |
| collection | DOAJ |
| description | The discovery that copy number variants (CNVs) are widespread in the human genome has motivated development of numerous algorithms that attempt to detect CNVs from intensity data. However, all approaches are plagued by high false discovery rates. Further, because CNVs are characterized by two dimensions (length and intensity) it is unclear how to order called CNVs to prioritize experimental validation.We developed a univariate score that correlates with the likelihood that a CNV is true. This score can be used to order CNV calls in such a way that calls having larger scores are more likely to overlap a true CNV. We developed cnv.beast, a computationally efficient algorithm for calling CNVs that uses robust backward elimination regression to keep CNV calls with scores that exceed a user-defined threshold. Using an independent dataset that was measured using a different platform, we validated our score and showed that our approach performed better than six other currently-available methods.cnv.beast is available at http://www.duke.edu/~asallen/Software.html. |
| first_indexed | 2024-12-12T21:17:04Z |
| format | Article |
| id | doaj.art-07e608a2070f4b559d301f11d52d4481 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T21:17:04Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-07e608a2070f4b559d301f11d52d44812022-12-22T00:11:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0192e8627210.1371/journal.pone.0086272Robust regression analysis of copy number variation data based on a univariate score.Glen A SattenAndrew S AllenMorna IkedaJennifer G MulleStephen T WarrenThe discovery that copy number variants (CNVs) are widespread in the human genome has motivated development of numerous algorithms that attempt to detect CNVs from intensity data. However, all approaches are plagued by high false discovery rates. Further, because CNVs are characterized by two dimensions (length and intensity) it is unclear how to order called CNVs to prioritize experimental validation.We developed a univariate score that correlates with the likelihood that a CNV is true. This score can be used to order CNV calls in such a way that calls having larger scores are more likely to overlap a true CNV. We developed cnv.beast, a computationally efficient algorithm for calling CNVs that uses robust backward elimination regression to keep CNV calls with scores that exceed a user-defined threshold. Using an independent dataset that was measured using a different platform, we validated our score and showed that our approach performed better than six other currently-available methods.cnv.beast is available at http://www.duke.edu/~asallen/Software.html.http://europepmc.org/articles/PMC3917847?pdf=render |
| spellingShingle | Glen A Satten Andrew S Allen Morna Ikeda Jennifer G Mulle Stephen T Warren Robust regression analysis of copy number variation data based on a univariate score. PLoS ONE |
| title | Robust regression analysis of copy number variation data based on a univariate score. |
| title_full | Robust regression analysis of copy number variation data based on a univariate score. |
| title_fullStr | Robust regression analysis of copy number variation data based on a univariate score. |
| title_full_unstemmed | Robust regression analysis of copy number variation data based on a univariate score. |
| title_short | Robust regression analysis of copy number variation data based on a univariate score. |
| title_sort | robust regression analysis of copy number variation data based on a univariate score |
| url | http://europepmc.org/articles/PMC3917847?pdf=render |
| work_keys_str_mv | AT glenasatten robustregressionanalysisofcopynumbervariationdatabasedonaunivariatescore AT andrewsallen robustregressionanalysisofcopynumbervariationdatabasedonaunivariatescore AT mornaikeda robustregressionanalysisofcopynumbervariationdatabasedonaunivariatescore AT jennifergmulle robustregressionanalysisofcopynumbervariationdatabasedonaunivariatescore AT stephentwarren robustregressionanalysisofcopynumbervariationdatabasedonaunivariatescore |