Interpreting patterns and analysis of acute leukemia gene expression data by multivariate statistical analysis

DNA microarray technologies are leading to an explosion in available gene expression data which simultaneously monitor the expression pattern of thousands of genes. Gene expression data are characterized by a very high dimensionality (genes), a relatively small number of samples (observations), irrelevant features, and it leads to a collinearity and multivariate problem. In this paper, we propose a systematic approach to gene selection based on discriminant partial least squares (DPLS) and fuzzy clustering methods. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression.