Abstract
This work focuses on differential expression analysis of microarray datasets. One way to improve such statistical analyses is to
integrate biological information in the design of these analyses. In this paper, we will use the relationship between the level of gene
expression and variability. Using this biological information, we propose to integrate the information from multiple genes to get a better
estimate of individual gene variance, when a small number of replicates are available, to increase the power of the statistical analysis.
We describe a strategy named the "Window t test" that uses multiple genes which share a similar expression level to compute the
variance which is then incorporated a classic t test. The performances of this new method are evaluated by comparison with classic
and widely- used methods for differential expression analysis (the classic Student t test, the Regularized t test (reg t test), SAM, Limma,
LPE and Shrinkage t). In each case tested, the results obtained were at least equivalent to the best performing method and, in most
cases, outperformed it. Moreover, the Window t test relies on a very simple procedure requiring small computing power compared with
other methods designed for microarray differential expression analysis.
Original language | English |
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Pages (from-to) | 327-344 |
Number of pages | 18 |
Journal | Central European Journal of Biology |
Volume | 3 |
Issue number | 3 |
Publication status | Published - 2008 |
Keywords
- Differential expression Microarray Affymetrix Variance Window t test SAM Regularized t test LPE Limma Shrinkage t