Selection bias in plots of microarray or other data that have been sampled from a high-dimensional space
For data that have many more features than observations, finding a low-dimensional representation that accurately reflects known prior groupings is non-trivial. Microarray gene expression data, used to create a "signature" or discrimination rule that distinguishes cancer tissues that are classified according to type of cancer, is an important special case. The optimal number of features is suitably determined using cross-validation, in which each of several parts of the data becomes in turn the...[Show more]
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