A new summarization method for affymetrix probe level data

Motivation: We propose a new model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. The new summarization method is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under t...

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Bibliographic Details
Published in:Bioinformatics 2006-04, Vol.22 (8), p.943-949
Main Authors: Hochreiter, Sepp, Clevert, Djork-Arné, Obermayer, Klaus
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Computer Simulation
DNA Probes - genetics
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling
General aspects
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Oligonucleotide Array Sequence Analysis - methods
Sequence Analysis, DNA - methods
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Summary:Motivation: We propose a new model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. The new summarization method is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. Thereafter, the RNA concentration is estimated from the model. In contrast to previous methods our new method called ‘Factor Analysis for Robust Microarray Summarization (FARMS)’ supplies both P-values indicating interesting information and signal intensity values. Results: We compare FARMS on Affymetrix's spike-in and Gene Logic's dilution data to established algorithms like Affymetrix Microarray Suite (MAS) 5.0, Model Based Expression Index (MBEI), Robust Multi-array Average (RMA). Further, we compared FARMS with 43 other methods via the ‘Affycomp II’ competition. The experimental results show that FARMS with default parameters outperforms previous methods if both sensitivity and specificity are simultaneously considered by the area under the receiver operating curve (AUC). We measured two quantities through the AUC: correctly detected expression changes versus wrongly detected (fold change) and correctly detected significantly different expressed genes in two sets of arrays versus wrongly detected (P-value). Furthermore FARMS is computationally less expensive then RMA, MAS and MBEI. Availability: The FARMS R package is available from Contact:hochreit@bioinf.jku.at Supplementary information:
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btl033