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A New Summarization Method for Affymetrix Probe Level Data
Citation key Hochreiter2006
Author Hochreiter, S. and Clevert, D-A. and Obermayer, K.
Pages 943 – 949
Year 2006
DOI 10.1093/bioinformatics/btl033
Journal Bioinformatics
Volume 22
Publisher Oxford Journals
Abstract 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 Affymetrixchar65533s spike-in and Gene Logicchar65533s 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 to 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 vs. wrongly detected (fold change) and correctly detected significantly different expressed genes in two sets of arrays vs. wrongly detected (p-value). Furthermore FARMS is computationally less expensive then RMA, MAS, and MBEI.
Bibtex Type of Publication Selected:applications
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