08Tests: Hypothesis Testing for Linear Models
In richierocks/limma2: Linear Models for Microarray Data
Description
Multiple testing across genes and contrasts
Gene Set Tests
Global Tests
Author(s)
Description
LIMMA provides a number of functions for multiple testing across both contrasts and genes.
The starting point is an MArrayLM object, called fit say, resulting from fitting a linear model and running eBayes and, optionally, contrasts.fit.
See 06.LinearModels or 07.SingleChannel for details.
Multiple testing across genes and contrasts
The key function is decideTests.
This function writes an object of class TestResults, which is basically a matrix of -1, 0 or 1 elements, of the same dimension as fit$coefficients, indicating whether each coefficient is significantly different from zero.
A number of different multiple testing strategies are provided.
The function calls other functions classifyTestsF, classifyTestsP and classifyTestsT which implement particular strategies.
The function FStat provides an alternative interface to classifyTestsF to extract only the overall moderated F-statistic.
selectModel chooses between linear models for each probe using AIC or BIC criteria.
This is an alternative to hypothesis testing and can choose between non-nested models.
A number of other functions are provided to display the results of decideTests.
The functions heatDiagram (or the older version heatdiagram displays the results in a heat-map style display.
This allows visual comparison of the results across many different conditions in the linear model.
The functions vennCounts and vennDiagram provide Venn diagram style summaries of the results.
Summary and show method exists for objects of class TestResults.
The results from decideTests can also be included when the results of a linear model fit are written to a file using write.fit.
Gene Set Tests
Competitive gene set testing for an individual gene set is provided by wilcoxGST or geneSetTest, which permute genes.
The gene set can be displayed using barcodeplot.
Self-contained gene set testing for an individual set is provided by roast, which uses rotation technology, analogous to permuting arrays.
Gene set enrichment analysis for a large database of gene sets is provided by romer.
topRomer is used to rank results from romer.
The functions alias2Symbol and alias2SymbolTable are provided to help match gene sets with microarray probes by way of official gene symbols.
Global Tests
The function genas can test for associations between two contrasts in a linear model.
Given a set of p-values, the function convest can be used to estimate the proportion of true null hypotheses.
When evaluating test procedures with simulated or known results, the utility function auROC can be used to compute the area under the Receiver Operating Curve for the test results for a given probe.
Author(s)
Gordon Smyth
richierocks/limma2 documentation built on May 27, 2019, 8:47 a.m.
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Description Multiple testing across genes and contrasts Gene Set Tests Global Tests Author(s)
Description
LIMMA provides a number of functions for multiple testing across both contrasts and genes.
The starting point is an MArrayLM object, called fit say, resulting from fitting a linear model and running eBayes and, optionally, contrasts.fit.
See 06.LinearModels or 07.SingleChannel for details.
Multiple testing across genes and contrasts
The key function is decideTests.
This function writes an object of class TestResults, which is basically a matrix of -1, 0 or 1 elements, of the same dimension as fit$coefficients, indicating whether each coefficient is significantly different from zero.
A number of different multiple testing strategies are provided.
The function calls other functions classifyTestsF, classifyTestsP and classifyTestsT which implement particular strategies.
The function FStat provides an alternative interface to classifyTestsF to extract only the overall moderated F-statistic.
selectModel chooses between linear models for each probe using AIC or BIC criteria.
This is an alternative to hypothesis testing and can choose between non-nested models.
A number of other functions are provided to display the results of decideTests.
The functions heatDiagram (or the older version heatdiagram displays the results in a heat-map style display.
This allows visual comparison of the results across many different conditions in the linear model.
The functions vennCounts and vennDiagram provide Venn diagram style summaries of the results.
Summary and show method exists for objects of class TestResults.
The results from decideTests can also be included when the results of a linear model fit are written to a file using write.fit.
Gene Set Tests
Competitive gene set testing for an individual gene set is provided by wilcoxGST or geneSetTest, which permute genes.
The gene set can be displayed using barcodeplot.
Self-contained gene set testing for an individual set is provided by roast, which uses rotation technology, analogous to permuting arrays.
Gene set enrichment analysis for a large database of gene sets is provided by romer.
topRomer is used to rank results from romer.
The functions alias2Symbol and alias2SymbolTable are provided to help match gene sets with microarray probes by way of official gene symbols.
Global Tests
The function genas can test for associations between two contrasts in a linear model.
Given a set of p-values, the function convest can be used to estimate the proportion of true null hypotheses.
When evaluating test procedures with simulated or known results, the utility function auROC can be used to compute the area under the Receiver Operating Curve for the test results for a given probe.
Author(s)
Gordon Smyth
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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