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5 years ago Hi @gabrielodom
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The DESCRIPTION file for this package is:
Package: pathwayPCA
Type: Package
Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection
Version: 0.99.1
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")))
Maintainer: Gabriel Odom <gabriel.odom@med.miami.edu>
Description: Apply the Supervised PCA and Adaptive, Elastic-Net, Sparse PCA
methods to extract principal components from each pathway. Use these pathway-
specific principal components as the design matrix relating the response to
each pathway. Return the model fit statistic p-values, and adjust these values
for False Discovery Rate. Return a data frame of the pathways sorted by their
adjusted p-values. This package has corresponding vignettes hosted in the
''User Guides'' page of <https://gabrielodom.github.io/pathwayPCA/index.html>,
and the website for the development information is hosted at
<https://github.com/gabrielodom/pathwayPCA>.
License: GPL-3
Depends: R (>= 3.5)
Imports:
lars,
methods,
parallel,
stats,
survival,
utils
Suggests:
knitr,
reshape2,
rmarkdown,
survminer,
tidyverse,
circlize
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_pcOut.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'
'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_adjust_and_sort_pValues.R'
'utils_load_test_data_onto_PCs.R'
'utils_multtest_pvalues.R'
'utils_read_gmt.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
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Comments:
Package size: we are cleaning up the .git/ directory now, in order to reduce the overall size of the package. We have one data file in
inst/extdatathat is 5.6Mb.library()calls within functions: because we use parallel computing architecture that is compatible across platforms, we useclusterEvalQ(cl, library(package)).[x] My package addresses statistical or bioinformatic issues related to the analysis and comprehension of high throughput genomic data.
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