| dc.creator |
Chuchana, Paul |
|
| dc.creator |
Marchand, Dorian |
|
| dc.creator |
Nugoli, Mélanie |
|
| dc.creator |
Rodríguez, Carmen |
|
| dc.creator |
Molinari, Nicolas |
|
| dc.creator |
García-Sanz, José A. |
|
| dc.date |
2008-02-06T12:57:01Z |
|
| dc.date |
2008-02-06T12:57:01Z |
|
| dc.date |
2007-04-25 |
|
| dc.date.accessioned |
2017-01-31T00:59:58Z |
|
| dc.date.available |
2017-01-31T00:59:58Z |
|
| dc.identifier |
Nucleic Acids Research 2007 May; 35(9): e71. |
|
| dc.identifier |
PMCID: 1888829 |
|
| dc.identifier |
http://hdl.handle.net/10261/2877 |
|
| dc.identifier |
10.1093/nar/gkm093 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/2877 |
|
| dc.description |
The authors are indebted to Dr Irene Lopez-Vidriero (CNB-CSIC, Madrid) for help with the Latin square dataset analysis with MAS5. Drs Alain Henaut and Ulrich Mansmann for critical reading of the manuscript and useful propositions and comments. We would like to acknowledge Dr Andrew Kramar for his help in improving the English. |
|
| dc.description |
One of the major issues in expression profiling analysis still is to outline proper thresholds to determine differential expression, while avoiding false positives. The problem being that the variance is inversely proportional to the log of signal intensities. Aiming to solve this issue, we describe a model, expression variation (EV), based on the LMS method, which allows data normalization and to construct confidence bands of gene expression, fitting cubic spline curves to the Box–Cox transformation. The confidence bands, fitted to the actual variance of the data, include the genes devoid of significant variation, and allow, based on the confidence bandwidth, to calculate EVs. Each outlier is positioned according to the dispersion space (DS) and a P-value is statistically calculated to determine EV. This model results in variance stabilization. Using two Affymetrix-generated datasets, the sets of differentially expressed genes selected using EV and other classical methods were compared. The analysis suggests that EV is more robust on variance stabilization and on selecting differential expression from both rare and strongly expressed genes. |
|
| dc.description |
This work was supported by funds from the Association pour la Recherche sur le Cancer (contract ARC 5122) and the CIT program of the Ligue Nationale Contre le Cancer; work in JAGS’ lab was financed by grant number DAMD17-02-1-0339 from the USAMRAA breast cancer program and SAF2003-00519 from the Spanish Ministry of Education and Science. The package has been developed in Excel (Microsoft) and is available upon request. Funding to pay the Open Access publication charges for this article was provided by the Spanish Ministry of Education and Science. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
3417185 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Oxford University Press |
|
| dc.relation |
http://dx.doi.org/doi:10.1093/nar/gkm093 |
|
| dc.rights |
openAccess |
|
| dc.title |
An adaptation of the LMS method to determine expression variations in profiling data |
|
| dc.type |
Artículo |
|