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qusage (version 2.4.0)

qusage: Quantitative Set Analysis for Gene Expression

Description

This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu)

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Version

Version

2.4.0

License

GPL (>= 2)

Maintainer

Christopher Bolen

Last Published

February 15th, 2017

Functions in qusage (2.4.0)

plotCombinedPDF

Plot combined PDF for an individual pathway
plotCIsGenes

Plot Gene Mean and Confidence Intervals
qsTable

Summary of QSarray Results
qusage

Run qusage on an expression dataset
read.gmt

Read in gene set information from .gmt files
QSarray-class

Class "QSarray"
GeneSets

Example Gene Sets
aggregateGeneSet

Calculate Pathway Activation
plotCIs

Plot Pathway Mean and Confidence Intervals
getXcoords

Get the X coordinates for the points of the PDF
combinePDFs

Combine PDFs from multiple QuSAGE comparisons
calcBayesCI

Calculate pathway Confidence Intervals
newQSarray

The qusage Array Object
calcVIF

Calculate Variance Inflation Factor
qgen

Run qusage while incoprating generalized least squares and linear mixed model analysis at the gene level to account for repeated measures, continous covariates, and confounder adjusting.
pVal

Calculate p-values for gene set activity
plotGeneSetDistributions

Plot gene and gene set PDFs
fluVaccine

Gene expression sets from Flu Vaccine trials
makeComparison

Compare Genes Between Two Groups
plotDensityCurves

Plot gene set PDFs
fluExample

Example gene expression set