Myeloid Leukemia

Classification of AML by DNA-Oligonucleotide Microarrays 227
Fig. 2. Overview of the data analysis workflow. After preparation of corresponding
data sets from the main master table, the data are analyzed, either unsupervised or
supervised. Unsupervised analyses are performed by hierarchical clustering or principal
component analysis. In the supervised analyses, differentially expressed genes can
be identified by various methods and selected for further interpretations, e.g., visualization
by hierarchical clustering, principal component analysis, plotting as bar graphs,
or generation of biological networks. In addition, differentially expressed genes can
be selected for classification tasks where several different machine-learning
approaches can be applied.
3.4. Microarray Data Analysis
A wide range of approaches are available for gleaning insights from the data
obtained from transcriptional profiling (8). Data analyses are performed by
two different approaches—supervised and unsupervised (Fig. 2). Unsupervised
analyses are used to test the hypothesis that specific characteristics, e.g., genetic
aberrations, are also reflected at the level of gene expression signatures. Supervised
analyses identify a minimal set of genes that could be used to stratify