Tissue Classi cation with Gene Expression Pro les
Constantly improving gene expression pro ling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to signi cantly aid in the development of ef cient cancer diagnosis and classi cation platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The  rst set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al., 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classi cation methods to assess the classi cation power of complete expression pro les. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classi er, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clusteringbased classi cation technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modi ed sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classi cation, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.