Abstract:
: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.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Chuchana P,Marchand D,Nugoli M,Rodriguez C,Molinari N,Garcia-Sanz JAdoi
10.1093/nar/gkm093subject
Has Abstractpub_date
2007-01-01 00:00:00pages
e71issue
9eissn
0305-1048issn
1362-4962pii
gkm093journal_volume
35pub_type
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