Bioinformatics Advance Access originally published online on January 29, 2004
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Bioinformatics 20(6) © Oxford University Press 2004; all rights reserved.
Comparative analysis of algorithms for signal quantitation from oligonucleotide microarrays
1 School of Computer Science and Engineering, Hebrew University, Jerusalem, 91904, Israel, 2 Genomic Research Center, Functional Genomics Unit, Sheba Medical Center, Tel Hashomer, 52620, Israel, 3 Health Science Center, University of Colorado, Denver, CO 80262, USA and 4 Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
Received on July 29, 2003
; revised on October 18, 2003
; accepted on October 20, 2003
Advance Access Publication January 29, 2004
Motivation: Recent years' exponential increase in DNA microarrays experiments has motivated the development of many signal quantitation (SQ) algorithms. These algorithms perform various transformations on the actual measurements aimed to enable researchers to compare readings of different genes quantitatively within one experiment and across separate experiments. However, it is relatively unclear whether there is a ‘best’ algorithm to quantitate microarray data. The ability to compare and assess such algorithms is crucial for any downstream analysis. In this work, we suggest a methodology for comparing different signal quantitation algorithms for gene expression data. Our aim is to enable researchers to compare the effect of different SQ algorithms on the specific dataset they are dealing with. We combine two kinds of tests to assess the effect of an SQ algorithm in terms of signal to noise ratio. To assess noise, we exploit redundancy within the experimental dataset to test the variability of a given SQ algorithm output. For the effect of the SQ on the signal we evaluate the overabundance of differentially expressed genes using various statistical significance tests.
Results: We demonstrate our analysis approach with three SQ algorithms for oligonucleotide microarrays. We compare the results of using the dChip software and the RMAExpress software to the ones obtained by using the standard Affymetrix MAS5 on a dataset containing pairs of repeated hybridizations. Our analysis suggests that dChip is more robust and stable than the MAS5 tools for about 60% of the genes while RMAExpress is able to achieve an even greater improvement in terms of signal to noise, for more than 95% of the genes.
Availability: The algorithms used to evaluate differentially expressed genes and their statistical overabundance can be found at http://www.cs.huji.ac.il/labs/compbio/scoregenes/
Contact: kamiskin{at}upmc.edu
* To whom correspondence should be addressed.
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