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Both supervised and unsupervised classification algorithms can be used in microarray data analysis [5].
However, more appropriate distance measures accounting for the discreet, Poisson-distributed structure of SAGE data have been shown to produce better clustering results than those achieved with conventional Euclidian or Pearson similarity measures routinely used in microarray data clustering [12].
6, 17 Hierarchical clustering techniques are extensively used in microarray data analysis, which combines all data points into a single set which are placed adjacent to each other in the feature space.
However, the procedure for generating RNA-seq data is fundamentally different from that for microarray data, the normalization methods used in microarray data are therefore not directly applicable in RNA-seq data.
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RF-based variable importance measures were previously used in microarray expression data analyses to select a relatively small set of informative genes for disease/sample classification [ 14, 15].
It has been used previously in microarray data analysis [ 12- 15].
One extensively used method in microarray data analysis is called SAM for "Significance Analysis of Microarray" [ 2].
A second set of functions is tailored towards Ensembl and include commonly used queries in microarray data analysis.
As part of SAM, Storey's FDR-controlling method has been proven to be more accurate than Benjamini and Hochberg's procedure and has been used extensively in microarray data analysis [ 4].
To validate Messina's results, we measured expression of S100A2 protein by immunohistochemistry upon a separate patient cohort from that used in the microarray data [8].
Biclustering algorithms are extensively used in DNA microarray data analysis.
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