Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression.
BACKGROUND
The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication.


RESULTS
On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition.


CONCLUSIONS
The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition.
Lemieux
Sébastien
S
Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Canada. s.lemieux@umontreal.ca
D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D016014:Linear Models; D008957:Models, Genetic; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015345:Oligonucleotide Probes; D012333:RNA, Messenger / Q000032:analysis; D012372:ROC Curve; D015203:Reproducibility of Results
D015345:Oligonucleotide Probes; D012333:RNA, Messenger