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This article has not been peer reviewed.

Deposited research article

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

Suresh Gopalan

Author Affiliations

3207 Stearns Hill Road, Waltham, MA 02451, USA

Genome Biology 2004, 5:P14  doi:10.1186/gb-2004-5-11-p14

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

Published: 28 September 2004



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.


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.


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.