An adaptation of the LMS method to determine expression variations in profiling data.

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 Res

journal_title

Nucleic acids research

authors

Chuchana P,Marchand D,Nugoli M,Rodriguez C,Molinari N,Garcia-Sanz JA

doi

10.1093/nar/gkm093

subject

Has Abstract

pub_date

2007-01-01 00:00:00

pages

e71

issue

9

eissn

0305-1048

issn

1362-4962

pii

gkm093

journal_volume

35

pub_type

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