qvalue - Q-value estimation for false discovery rate control
This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining.
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multiplecomparisons
14.67 score 122 stars 149 dependents 4.3k scripts 44k downloadsedge - Extraction of Differential Gene Expression
The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.
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multiplecomparisondifferentialexpressiontimecourseregressiongeneexpressiondataimport
7.76 score 21 stars 92 scripts 550 downloadssnm - Supervised Normalization of Microarrays
SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.
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microarrayonechanneltwochannelmultichanneldifferentialexpressionexonarraygeneexpressiontranscriptionmultiplecomparisonpreprocessingqualitycontrol
4.85 score 1 dependents 59 scripts 538 downloads