The introduction of microarrays for gene expression profiling now offers a new approach to molecular cytogenetics. Measurement of the expression of all the genes in a range of tissues or cell types allows the determination of the transcriptional status of the cell, identifying which genes are active and which are silent. Microarrays for expression profiling consist of systematic arrays of cDNA or oligonucleotides of known sequence that are spotted or synthesized at discrete loci on a glass or silicon surface. They allow the simultaneous analysis of a large number of genes at high resolution following the hybridization of labeled cDNA or cRNA derived from the samples to be examined. Microarray output is represented by a large number of individual data points that must be analyzed by a data-mining program in order to correlate the data, and to group them together in a meaningful manner. In recent years, the use of DNA microarrays has been largely devoted to the genetic profiling of tumor subtypes, with the aims of defining new classes with prognostic and diagnostic relevance and of increasing our knowledge of the mechanisms underlying the biology of these diseases. The pathological diagnosis and classification of human neoplasia is based on well-defined morphological, cytochemical, immunophenotypic and clinical criteria. For leukemia and lymphoma, the relevance of cytogenetics as one of the most valuable prognostic determinants at diagnosis, has come from the analysis of the leukemia karyotype. This has identified non-random, somatically acquired translocations, inversions and deletions, which are often associated with specific morphological subtypes. However, leukemias with apparently normal karyotypes do exist and constitute the largest single subgroup (up to 40% of cases). Thus, the application of microarray analysis may improve the classification of leukemias and offer clues to the underlying etiology. A molecular classification would have the potential to define new subgroups with more prognostic and therapeutic significance, linking the expression profile to the outcome. It could offer many advantages over conventional classification methods, including the possibility of deducing chromosomal data from non-dividing cells.
Three basic steps for efficient and effective data analysis are necessary: data normalization, data filtering, and pattern identification. To compare expression values directly, it is necessary to apply some sort of normalization strategy to the data, either between paired samples or across a set of experiments. To 'normalize' in the context of DNA microarrays means to standardize the data so as to be able to differentiate between real (biological) variations in gene expression levels and variations due to the measurement process. Gene expression data can then be subjected to a variation filter, which excludes uninformative genes, i.e. genes showing minimal variation across the samples, and genes expressed below or
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