GenePattern suite repository |
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ClusteringSuite
Clustering modules partition a gene expression dataset into
clusters such that the gene expression data in each cluster share
common expression traits based on the distance measure used. |
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GeneListSelectionSuite
Gene List Selection modules include several algorithms to
determine features (genes) that are most closely correlated with known
class templates (e.g. tumour vs normal) and the significance of the correlation. |
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LuGetzMiska.Nature.2005.Suite
Pipelines used in the Lu, Getz, Miska paper "MicroRNA expression profiles classify human cancers" available at http://www.nature.com/nature/journal/v435/n7043/full/nature03702.html |
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MultiplotSuite
consists three Multiplot modules: MultiplotPreprocess, Multiplot and MultiplotExtractor. |
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PredictionSuite
Prediction modules perform two step analyses on gene
expression datasets where the first step is to create a model based
on data of known class (train dataset) and the second step is to
predict the class of additional samples (test dataset). Most methods require seperate test and
train datasets while cross validation (XValidation) methods use
a single dataset to create a model using leave-one-out
cross-validation by iteratively leaving one sample out and
constructing a training model on the remaining data and then testing
the model on the left-out sample. |
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ProteomicsSuite
Peak detection, noise subtraction, peak matching, and more for advanced analysis of MALDI, SELDI, and LC-MS data. |
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SNP Analysis
The SNP Analysis suite contains modules for the preprocessing, analysis and visualization of SNP data. |
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