Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data.
Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the n...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC4368642?pdf=render |
| _version_ | 1818940427181490176 |
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| author | Alan L Hutchison Mark Maienschein-Cline Andrew H Chiang S M Ali Tabei Herman Gudjonson Neil Bahroos Ravi Allada Aaron R Dinner |
| author_facet | Alan L Hutchison Mark Maienschein-Cline Andrew H Chiang S M Ali Tabei Herman Gudjonson Neil Bahroos Ravi Allada Aaron R Dinner |
| author_sort | Alan L Hutchison |
| collection | DOAJ |
| description | Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms. |
| first_indexed | 2024-12-20T06:39:28Z |
| format | Article |
| id | doaj.art-d96871aab9664919afe2d107e18e9760 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-20T06:39:28Z |
| publishDate | 2015-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-d96871aab9664919afe2d107e18e97602022-12-21T19:49:54ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-03-01113e100409410.1371/journal.pcbi.1004094Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data.Alan L HutchisonMark Maienschein-ClineAndrew H ChiangS M Ali TabeiHerman GudjonsonNeil BahroosRavi AlladaAaron R DinnerRobust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function. To this end, several analytic methods have been developed for detecting periodic patterns. We improve one such method, JTK_CYCLE, by explicitly calculating the null distribution such that it accounts for multiple hypothesis testing and by including non-sinusoidal reference waveforms. We term this method empirical JTK_CYCLE with asymmetry search, and we compare its performance to JTK_CYCLE with Bonferroni and Benjamini-Hochberg multiple hypothesis testing correction, as well as to five other methods: cyclohedron test, address reduction, stable persistence, ANOVA, and F24. We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate. Our analysis also provides insight into experimental design and we find that, for a fixed number of samples, better sensitivity and specificity are achieved with higher numbers of replicates than with higher sampling density. Application of the methods to detecting circadian rhythms in a metadataset of microarrays that quantify time-dependent gene expression in whole heads of Drosophila melanogaster reveals annotations that are enriched among genes with highly asymmetric waveforms. These include a wide range of oxidation reduction and metabolic genes, as well as genes with transcripts that have multiple splice forms.http://europepmc.org/articles/PMC4368642?pdf=render |
| spellingShingle | Alan L Hutchison Mark Maienschein-Cline Andrew H Chiang S M Ali Tabei Herman Gudjonson Neil Bahroos Ravi Allada Aaron R Dinner Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data. PLoS Computational Biology |
| title | Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data. |
| title_full | Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data. |
| title_fullStr | Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data. |
| title_full_unstemmed | Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data. |
| title_short | Improved statistical methods enable greater sensitivity in rhythm detection for genome-wide data. |
| title_sort | improved statistical methods enable greater sensitivity in rhythm detection for genome wide data |
| url | http://europepmc.org/articles/PMC4368642?pdf=render |
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