I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data

• Published in: Bioinformatics Vol. 23; no. 21; pp. 2897 - 2902
• Main Authors: Talloen, Willem, Clevert, Djork-Arné, Hochreiter, Sepp, Amaratunga, Dhammika, Bijnens, Luc, Kass, Stefan, Göhlmann, Hinrich W.H.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01-11-2007; Oxford Publishing Limited (England)
• Subjects: Algorithms; Arabidopsis; Artificial Intelligence; Biological and medical sciences; Cluster Analysis; Data Interpretation, Statistical; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Multigene Family - physiology; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated - methods; Software; Filtering; Gene; Microarray; Bioinformatics
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btm478

Motivation: DNA microarray technology typically generates many measurements of which only a relatively small subset is informative for the interpretation of the experiment. To avoid false positive results, it is therefore critical to select the informative genes from the large noisy data before the actual analysis. Most currently available filtering techniques are supervised and therefore suffer from a potential risk of overfitting. The unsupervised filtering techniques, on the other hand, are either not very efficient or too stringent as they may mix up signal with noise. We propose to use the multiple probes measuring the same target mRNA as repeated measures to quantify the signal-to-noise ratio of that specific probe set. A Bayesian factor analysis with specifically chosen prior settings, which models this probe level information, is providing an objective feature filtering technique, named informative/non-informative calls (I/NI calls). Results: Based on 30 real-life data sets (including various human, rat, mice and Arabidopsis studies) and a spiked-in data set, it is shown that I/NI calls is highly effective, with exclusion rates ranging from 70% to 99%. Consequently, it offers a critical solution to the curse of high-dimensionality in the analysis of microarray data. Availability: This filtering approach is publicly available as a function implemented in the R package FARMS (www.bioinf.jku.at/software/farms/farms.html). Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.