Package: pathwayPCA
Type: Package
Title: Integrative Pathway Analysis with Modern PCA Methodology and
        Gene Selection
Version: 1.2.0
Authors@R: c(person("Gabriel", "Odom", email = "gabriel.odom@med.miami.edu", role = c("aut","cre")),
  person("James", "Ban", email = "yuguang.ban@med.miami.edu", role = c("aut")),
  person("Lizhong", "Liu", email = "lxl816@miami.edu", role = c("aut")),
  person("Lily", "Wang", email = "lily.wang@med.miami.edu", role = c("aut")),
  person("Steven", "Chen", email = "steven.chen@med.miami.edu", role = c("aut")))
Description: pathwayPCA is an integrative analysis tool that implements the
  principal component analysis (PCA) based pathway analysis approaches described
  in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows
  users to: (1) Test pathway association with binary, continuous, or survival
  phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and
  AES-PCA approaches. (3) Compute principal components (PCs) based on the
  selected genes. These estimated latent variables represent pathway activities
  for individual subjects, which can then be used to perform integrative pathway
  analysis, such as multi-omics analysis. (4) Extract relevant genes that drive
  pathway significance as well as data corresponding to these relevant genes for
  additional in-depth analysis. (5) Perform analyses with enhanced computational
  efficiency with parallel computing and enhanced data safety with S4-class data
  objects. (6) Analyze studies with complex experimental designs, with multiple
  covariates, and with interaction effects, e.g., testing whether pathway
  association with clinical phenotype is different between male and female
  subjects.
  Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; 
  Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and
  Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.
License: GPL-3
Depends: R (>= 3.6)
Imports: lars, methods, parallel, stats, survival, utils
Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2,
        rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse
biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP,
        Transcription, GenePrediction, GeneSetEnrichment,
        GeneSignaling, GeneTarget, GenomeWideAssociation,
        GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics,
        Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology,
        Transcriptomics, Classification, DimensionReduction,
        FeatureExtraction, PrincipalComponent, Regression, Survival,
        MultipleComparison, Pathways
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Collate: 'CreatePathwayCollection.R' 'createClass_OmicsPath.R'
        'createClass_validOmics.R' 'accessClass_OmicsPath.R'
        'createClass_OmicsSurv.R' 'accessClass_OmicsSurv.R'
        'accessClass_OmicsRegCateg.R' 'createClass_OmicsCateg.R'
        'createClass_OmicsReg.R' 'accessClass_OmicsPathData.R'
        'accessClass_pathwayCollection.R'
        'accessClass_pathwayCollection_which.R' 'accessClass_pcOut.R'
        'accessClass_pcOutpVals.R' 'aesPC_calculate_AESPCA.R'
        'aesPC_calculate_LARS.R' 'aesPC_extract_OmicsPath_PCs.R'
        'aesPC_permtest_CoxPH.R' 'aesPC_permtest_GLM.R'
        'aesPC_permtest_LM.R' 'aesPC_unknown_matrixNorm.R'
        'aesPC_wrapper.R' 'createOmics_All.R'
        'createOmics_CheckAssay.R'
        'createOmics_CheckPathwayCollection.R'
        'createOmics_CheckSampleIDs.R' 'createOmics_JoinPhenoAssay.R'
        'createOmics_TrimPathwayCollection.R' 'createOmics_Wrapper.R'
        'data_colonSubset.R' 'data_genesetSubset.R'
        'data_wikipathways.R' 'data_wikipathways_symbols.R'
        'pathwayPCA.R' 'printClass_Omics_All.R'
        'printClass_pathwayCollection.R' 'superPC_model_CoxPH.R'
        'superPC_model_GLM.R' 'superPC_model_LS.R'
        'superPC_model_tStats.R' 'superPC_model_train.R'
        'superPC_modifiedSVD.R' 'superPC_optimWeibullParams.R'
        'superPC_optimWeibull_pValues.R' 'superPC_pathway_tControl.R'
        'superPC_pathway_tScores.R' 'superPC_pathway_tValues.R'
        'superPC_permuteSamples.R' 'superPC_wrapper.R'
        'utils_Contains.R' 'utils_adjust_and_sort_pValues.R'
        'utils_load_test_data_onto_PCs.R' 'utils_multtest_pvalues.R'
        'utils_read_gmt.R' 'utils_stdExpr_2_tidyAssay.R'
        'utils_transpose_assay.R' 'utils_write_gmt.R'
VignetteBuilder: knitr
URL: <https://gabrielodom.github.io/pathwayPCA/>
        <https://github.com/gabrielodom/pathwayPCA>
BugReports: https://github.com/gabrielodom/pathwayPCA/issues
git_url: https://git.bioconductor.org/packages/pathwayPCA
git_branch: RELEASE_3_10
git_last_commit: 3ee316f
git_last_commit_date: 2019-10-29
Date/Publication: 2019-10-29
NeedsCompilation: no
Packaged: 2019-10-30 04:56:34 UTC; biocbuild
Author: Gabriel Odom [aut, cre],
  James Ban [aut],
  Lizhong Liu [aut],
  Lily Wang [aut],
  Steven Chen [aut]
Maintainer: Gabriel Odom <gabriel.odom@med.miami.edu>
Built: R 3.6.1; ; 2019-10-30 13:52:30 UTC; windows
