R Graphical Manual

Last data update: 2014.03.03

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Results 1 - 3 of 3 found.
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SigCheck : Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata

Package: SigCheck
Type: Package
Title: Check a gene signature's prognostic performance against random
signatures, known signatures, and permuted data/metadata
Version: 2.4.0
Author: Rory Stark <rory.stark@cruk.cam.ac.uk> and
Justin Norden
Maintainer: Rory Stark <rory.stark@cruk.cam.ac.uk>
Description: While gene signatures are frequently used to predict phenotypes
(e.g. predict prognosis of cancer patients), it it not always
clear how optimal or meaningful they are (cf David Venet,
Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene
Expression Signatures Are Significantly Associated with Breast
Cancer Outcome"). Based on suggestions in that paper,
SigCheck accepts a data set (as an ExpressionSet) and a gene
signature, and compares its performance on survival and/or
classification tasks against
a) random gene signatures of the same length;
b) known, related and unrelated gene signatures;
and c) permuted data and/or metadata.
License: Artistic-2.0
LazyLoad: yes
Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel,
survival
Imports: graphics, stats, utils, methods
Suggests: BiocStyle, breastCancerNKI, qusage
biocViews: GeneExpression, Classification, GeneSetEnrichment
NeedsCompilation: no
Packaged: 2016-05-04 05:51:45 UTC; biocbuild

● Data Source: BioConductor
● BiocViews: Classification, GeneExpression, GeneSetEnrichment
● 16 images, 8 functions, 3 datasets
● Reverse Depends: 0

a4Classif : Automated Affymetrix Array Analysis Classification Package

Package: a4Classif
Type: Package
Title: Automated Affymetrix Array Analysis Classification Package
Version: 1.20.0
Date: 2011-05-21
Author: Willem Talloen, Tobias Verbeke
Maintainer: Tobias Verbeke <tobias.verbeke@openanalytics.eu>, Willem
Ligtenberg <willem.ligtenberg@openanalytics.eu>
Description: Automated Affymetrix Array Analysis Classification Package
Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet,
varSelRF
Imports: a4Core
Suggests: ALL
License: GPL-3
biocViews: Microarray
NeedsCompilation: no
Packaged: 2016-05-04 04:01:51 UTC; biocbuild

● Data Source: BioConductor
● BiocViews: Microarray
● 3 images, 4 functions, 0 datasets
● Reverse Depends: 1

pRoloc : A unifying bioinformatics framework for spatial proteomics

Package: pRoloc
Type: Package
Title: A unifying bioinformatics framework for spatial proteomics
Version: 1.12.4
Authors@R: c(person(given = "Laurent", family = "Gatto",
email = "lg390@cam.ac.uk",
role = c("aut","cre")),
person(given = "Lisa", family ="Breckels",
email = "lms79@cam.ac.uk",
role = "aut"),
person(given = "Samuel", family ="Wieczorek",
email = "samuel.wieczorek@cea.fr",
role = "ctb"))
Author: Laurent Gatto and Lisa M. Breckels with contributions from
Thomas Burger and Samuel Wieczorek
Maintainer: Laurent Gatto <lg390@cam.ac.uk>
Description: This package implements pattern recognition techniques on
quantitiative mass spectrometry data to infer protein
sub-cellular localisation.
Depends: R (>= 2.15), MSnbase (>= 1.19.20), MLInterfaces (>= 1.37.1),
methods, Rcpp (>= 0.10.3), BiocParallel
Imports: Biobase, mclust (>= 4.3), caret, e1071, sampling, class,
kernlab, lattice, nnet, randomForest, proxy, FNN, BiocGenerics,
stats, RColorBrewer, scales, MASS, knitr, mvtnorm, gtools,
plyr, ggplot2, biomaRt, utils, grDevices, graphics
Suggests: testthat, pRolocdata (>= 1.9.4), roxygen2, synapter, xtable,
tsne, BiocStyle, hpar, dplyr, GO.db, AnnotationDbi
LinkingTo: Rcpp, RcppArmadillo
License: GPL-2
VignetteBuilder: knitr
Video:
https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow
URL: https://github.com/lgatto/pRoloc
BugReports: https://github.com/lgatto/pRoloc/issues
biocViews: Proteomics, MassSpectrometry, Classification, Clustering,
QualityControl
Collate: AllGenerics.R machinelearning-framework.R
machinelearning-framework-theta.R machinelearning-utils.R
machinelearning-functions-knn.R
machinelearning-functions-ksvm.R machinelearning-functions-nb.R
machinelearning-functions-nnet.R
machinelearning-functions-PerTurbo.R
machinelearning-functions-plsda.R
machinelearning-functions-rf.R machinelearning-functions-svm.R
machinelearning-functions-knntl.R belief.R distances.R
markers.R pRolocmarkers.R chi2.R MLInterfaces.R
clustering-framework.R MSnSet.R clustering-kmeans.R
perTurbo-algorithm.R phenodisco.R plotting.R plotting2.R
environment.R utils.R lopims.R annotation.R goenv.R go.R
makeGoSet.R vis.R MartInterface.R dynamics.R zzz.R
goannotations.R clusterdist-functions.R clusterdist-framework.R
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-06-15 03:40:44 UTC; biocbuild

● Data Source: BioConductor
● BiocViews: Classification, Clustering, MassSpectrometry, Proteomics, QualityControl
● 108 images, 71 functions, 0 datasets
● Reverse Depends: 1