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Deposited research article

ResurfP: a response surface aided parametric test for identifying differentials in GeneChip based oligonucleotide array experiments

Suresh Gopalan

3207 Stearns Hill Road, Waltham, MA 02451, USA

author email corresponding author email

Genome Biology 2004, 5:P14doi:10.1186/gb-2004-5-11-p14

Published:	28 September 2004

This was the first version of this article to be made available publicly.

Subject areas: Methods, Bioinformatics

Abstract

Background

Transcripts in a GeneChip type microarray is represented by multiple independent short oligonucleotide probes. One widely used approach is to compute a model based unified expression index for the transcript which is subsequently used for comparative data analysis. Alternative approach is to analyze the data at the probe-level. A good understanding of the effect of the number of probe-pairs included at different statistical threshold used for selection should aid optimal selection of differentials. A test dataset with known differentials was used to study this property in comparisons involving two datasets.

Results

A response surface was plotted by formulating an equation that captures the effect varying threshold of probe-pairs and t-statistic on true positives and false positives identified. The resulting response surface indicate that a wide range of probe-pair and t-statistic combinations yield comparative results. The toplology of the surface was used to define one form of additive cost-based approach - involving t and number of probe-pairs used - to determine the optimum threshold to achieve a good balance of true positives and false positives when comparing two datasets at the probe-level. In addition a data scaling approach was used to study the impact of a selected threshold on the number of false negatives of differing magnitude of differentials in a given dataset.

Conclusions

The results indicate that this response surface assisted approach (termed ResurfP) would be effective in determining optimal data-specific threshold for number of probe-pairs used and of the t-statistic when analyzing differentials between two datasets using probe-level data.