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- S. Hochreiter, D-A.
Clevert, and K. Obermayer. A new summarization method for Affymetrix probe
level data.
.
Bioinformatics, 22:943-949, 2006.
(PDF)
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 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.
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