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Phantom: investigating heterogeneous gene sets in time-course data
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Bioinformatics, 33, 2017, 18, S. 2957-2959 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Gu, Jinghua Wang, Xuan Chan, Jinyan Baldwin, Nicole E Turner, Jacob A Gu, Jinghua Wang, Xuan Chan, Jinyan Baldwin, Nicole E Turner, Jacob A |
|---|---|
| author |
Gu, Jinghua Wang, Xuan Chan, Jinyan Baldwin, Nicole E Turner, Jacob A |
| spellingShingle |
Gu, Jinghua Wang, Xuan Chan, Jinyan Baldwin, Nicole E Turner, Jacob A Bioinformatics Phantom: investigating heterogeneous gene sets in time-course data Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
gu, jinghua |
| spelling |
Gu, Jinghua Wang, Xuan Chan, Jinyan Baldwin, Nicole E Turner, Jacob A 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btx348 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom. R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> Phantom: investigating heterogeneous gene sets in time-course data Bioinformatics |
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10.1093/bioinformatics/btx348 |
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| title |
Phantom: investigating heterogeneous gene sets in time-course data |
| title_unstemmed |
Phantom: investigating heterogeneous gene sets in time-course data |
| title_full |
Phantom: investigating heterogeneous gene sets in time-course data |
| title_fullStr |
Phantom: investigating heterogeneous gene sets in time-course data |
| title_full_unstemmed |
Phantom: investigating heterogeneous gene sets in time-course data |
| title_short |
Phantom: investigating heterogeneous gene sets in time-course data |
| title_sort |
phantom: investigating heterogeneous gene sets in time-course data |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/btx348 |
| publishDate |
2017 |
| physical |
2957-2959 |
| description |
<jats:title>Abstract</jats:title>
<jats:sec>
<jats:title>Motivation</jats:title>
<jats:p>Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Results</jats:title>
<jats:p>We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Availability and implementation</jats:title>
<jats:p>Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom. R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html.</jats:p>
</jats:sec>
<jats:sec>
<jats:title>Supplementary information</jats:title>
<jats:p>Supplementary data are available at Bioinformatics online.</jats:p>
</jats:sec> |
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| author | Gu, Jinghua, Wang, Xuan, Chan, Jinyan, Baldwin, Nicole E, Turner, Jacob A |
| author_facet | Gu, Jinghua, Wang, Xuan, Chan, Jinyan, Baldwin, Nicole E, Turner, Jacob A, Gu, Jinghua, Wang, Xuan, Chan, Jinyan, Baldwin, Nicole E, Turner, Jacob A |
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| description | <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom. R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> |
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| imprint_str_mv | Oxford University Press (OUP), 2017 |
| institution | DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14 |
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| spelling | Gu, Jinghua Wang, Xuan Chan, Jinyan Baldwin, Nicole E Turner, Jacob A 1367-4803 1367-4811 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btx348 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom. R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> Phantom: investigating heterogeneous gene sets in time-course data Bioinformatics |
| spellingShingle | Gu, Jinghua, Wang, Xuan, Chan, Jinyan, Baldwin, Nicole E, Turner, Jacob A, Bioinformatics, Phantom: investigating heterogeneous gene sets in time-course data, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Phantom: investigating heterogeneous gene sets in time-course data |
| title_full | Phantom: investigating heterogeneous gene sets in time-course data |
| title_fullStr | Phantom: investigating heterogeneous gene sets in time-course data |
| title_full_unstemmed | Phantom: investigating heterogeneous gene sets in time-course data |
| title_short | Phantom: investigating heterogeneous gene sets in time-course data |
| title_sort | phantom: investigating heterogeneous gene sets in time-course data |
| title_unstemmed | Phantom: investigating heterogeneous gene sets in time-course data |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/btx348 |