GiANT-package {GiANT}R Documentation

Enrichment analysis

Description

Toolbox for gene set analysis of uncertain gene sets.

Details

Package: GiANT
Type: Package
Version: 1.3
Date: 2020-04-29
License: Artistic-2.0
LazyLoad: yes

This package provides an approach for evaluating the fuzziness of a gene set. This is done by repeatedly performing gene set analyses on slightly modified versions of the gene set and comparing their enrichment scores. A utility for such uncertainty tests is provided in the evaluateGeneSetUncertainty function.

The package also comprises a generic framework for different types of enrichment analyses (Ackermann and Strimmer). It establishes a customizeable pipeline that typically consists of the following steps:

The package represents such analysis pipelines as configuration objects that can be created using the function gsAnalysis. For predefined state-of-the-art methods, such as Gene Set Enrichment Analysis (Subramanian et al), Overrepresentation Analysis or Global Ancova (Hummel et al), it provides predefined configurations (see predefinedAnalyses).

The main function for standard gene set analyses, geneSetAnalysis, performs enrichment analyses based on pipeline configuration objects.

Author(s)

Florian Schmid, Christoph Müssel, Johann M. Kraus, Hans A. Kestler

Maintainer: Hans A. Kestler <hans.kestler@uni-ulm.de>

References

Ackermann, M., Strimmer, K. (2009) A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 10(1), 47.

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., Mesirov, J. P. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Science of the United States of America, 102, 15545-15550.

Hummel, M., Meister, R., Mansmann, U. (2008) Globalancova: exploration and assessment of gene group effects. Bioinformatics, 24(1), 78–85.

Examples


  data(exampleData)
  ##################################
  # Example 1: gene set analysis   #
  ##################################
  res <- geneSetAnalysis(  
    # parameters for geneSetAnalysis
    dat = countdata,
    geneSets = pathways[1],
    analysis = analysis.averageCorrelation(),
    adjustmentMethod = "fdr",
    # additional parameters for analysis.averageCorrelation
    labs = labels,
    method = "pearson",
    numSamples = 50)
  
  summary(res, mode="table")
  
  ####################################
  # Example 2: uncertainty analysis  #
  ####################################
  resUncertainty <- evaluateGeneSetUncertainty(  
    # parameters for evaluateGeneSetUncertainty
    dat = countdata,
    geneSet = pathways[[3]],
    analysis = analysis.averageCorrelation(),
    numSamplesUncertainty = 5,
    blockSize = 1,
    k = seq(0.1,0.9,by=0.1),
    # additional parameters for analysis.averageCorrelation
    labs = labels,
    numSamples = 5)
  
  plot(resUncertainty, main = names(pathways[3]))


[Package GiANT version 1.3.4 Index]