An Alternative Dimension Reduction Approach to Supervised Principal Components Analysis in High Dimensional Survival Data.

Objective: This study aims at comparing the performances of supervised principal component analysis (SPCA) which is used for dimension reduction and an alternatively proposed approach of nonlinear principal component analysis using artificial neural networks performed by gene selection with survival tree (survival tree based NLPCA- NN). Material and Methods: Gene expression data set from Rosenwald et al.(2002) pertaining to 240 patients with diffuse large B-cell lymphoma (DLBCL) is used. While Cox scores are used for determining important genes from high dimensional gene expression data in SPCA, in survival tree based NLPCA-NN approach, importance values of the survival tree are used. Important genes according to the Cox scores are reduced to three principal components by singular value decomposition. Important genes determined by the survival tree are taken as input variables in neural networks and reduced to three principal components. The performances of SPCA and survival tree based NLPCA-NN are compared by using Cox regression models (CRM). C index is calculated to compare obtained Cox regression models. Results: According to Cox scores, 121 genes are determined; according to importance values of survival tree 114 genes are determined. The percentages of variances explained by SPCA and survival tree based NLPCA-NN were 18.2% and 35.1% respectively. Harrell's C indexes are calculated as 0.726 for CRM-1, 0.687 for CRM-2. Conclusion: As a result, while SPCA takes only the linear relationships into consideration, survival tree based NLPCA-NN also takes non-linear relationships into account and has more variance explanation and NLPCA-NN can be evaluated as an alternative method to SPCA. [ABSTRACT FROM AUTHOR]