Classifying gene expression profiles from pairwise mRNA comparisons

Abstract

We present a new approach to molecular classification based on mRNA comparisons. Our method, referred to as the top-scoring pair(s) (TSP) classifier, is motivated by current technical and practical limitations in using gene expression microarray data for class prediction, for example to detect disease, identify tumors or predict treatment response. Accurate statistical inference from such data is difficult due to the small number of observations, typically tens, relative to the large number of genes, typically thousands. Moreover, conventional methods from machine learning lead to decisions which are usually very difficult to interpret in simple or biologically meaningful terms. In contrast, the TSP classifier provides decision rules which i) involve very few genes and only relative expression values (e.g., comparing the mRNA counts within a single pair of genes); ii) are both accurate and transparent; and iii) provide specific hypotheses for follow-up studies. In particular, the TSP classifier achieves prediction rates with standard cancer data that are as high as those of previous studies which use considerably more genes and complex procedures. Finally, the TSP classifier is parameter-free, thus avoiding the type of over-fitting and inflated estimates of performance that result when all aspects of learning a predictor are not properly cross-validated.

Figure 1

The distribution of top scores for a random class label permutation analysis. The locations of the top score on the real Breast and Leukemia data sets are shown in red; the estimated p-values are 0.001 and 0 respectively. The top-score histogram for the Prostate data looks qualitatively the same as the one for Leukemia, and the maximum score achieved among all of the artificial data sets is 0.586; the score observed on the real data is Δ = 0.902.

Figure 2

Scatter plots for a top pair of genes for each study. The two classes are represented using red and blue, the axes represent the expression levels of the two genes and the dotted line y = x represents the decision boundary.