Factor Analysis for Robust Microarray Summarization (FARMS) is a model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. It 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. The comparison on the Affymetrix spiked-in bechmark data shows the excellent sensitivity and specificity performance of FARMS.
Our cn.FARMS algorithm for detecting copy number variations in microarray data can be found here.

Please cite:

Sepp Hochreiter, Djork-Arné Clevert, and Klaus Obermayer. "A new summarization method for affymetrix probe level data." Bioinformatics 2006 22(8):943-949; doi:10.1093/bioinformatics/btl033. Abstract

I/NI calls

Informative/ non-informative (I/NI) calls is an objective feature filtering technique for Affymetrix GeneChips. It uses the multiple probes measuring the same target mRNA as repeated measures to quantify the signal-to-noise ratio of that specific probe set. By incorporating probe level information to assess the noisy nature of probe sets, I/NI calls provide a highly powerful and objective tool for gene filtering. I/NI calls consequently offers a key solution to the main problem in the analysis of high-dimensional microarray data, being multiple testing and overfitting. I/NI calls can be used in combination with summarization techniques like FARMS, but also with any other summarization technique like MAS5 or (GC)RMA.

Please cite:

Willem Talloen, Djork-Arné Clevert, Sepp Hochreiter, Dhammika Amaratunga, Luc Bijnens, Stefan Kass, and Hinrich W.H. Göhlmann. "I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data." Bioinformatics 2007; doi:10.1093/bioinformatics/btm478. Abstract


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