Abstract:
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. CONCLUSION: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.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Lemieux Sdoi
10.1186/1471-2105-7-391subject
Has Abstractpub_date
2006-08-25 00:00:00pages
391issn
1471-2105pii
1471-2105-7-391journal_volume
7pub_type
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