Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Methodology article

The top-scoring ‘N’ algorithm: a generalized relative expression classification method from small numbers of biomolecules

Andrew T Magis12 and Nathan D Price12*

Author affiliations

1 Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA

2 Center for Biophysics and Computational Biology, University of Illinois, Urbana, IL, 61801, USA

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2012, 13:227  doi:10.1186/1471-2105-13-227

Published: 11 September 2012

Abstract

Background

Relative expression algorithms such as the top-scoring pair (TSP) and the top-scoring triplet (TST) have several strengths that distinguish them from other classification methods, including resistance to overfitting, invariance to most data normalization methods, and biological interpretability. The top-scoring ‘N’ (TSN) algorithm is a generalized form of other relative expression algorithms which uses generic permutations and a dynamic classifier size to control both the permutation and combination space available for classification.

Results

TSN was tested on nine cancer datasets, showing statistically significant differences in classification accuracy between different classifier sizes (choices of N). TSN also performed competitively against a wide variety of different classification methods, including artificial neural networks, classification trees, discriminant analysis, k-Nearest neighbor, naïve Bayes, and support vector machines, when tested on the Microarray Quality Control II datasets. Furthermore, TSN exhibits low levels of overfitting on training data compared to other methods, giving confidence that results obtained during cross validation will be more generally applicable to external validation sets.

Conclusions

TSN preserves the strengths of other relative expression algorithms while allowing a much larger permutation and combination space to be explored, potentially improving classification accuracies when fewer numbers of measured features are available.

Keywords:
Classification; Top-scoring pair; Relative expression; Cross validation; Support vector machine; Graphics processing unit; Microarray