plsgenomics: PLS Analyses for Genomics

Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: RIRLS-SPLS; and an adaptive version of the sparse PLS.

Install the latest version of this package by entering the following in R:
install.packages("plsgenomics")
AuthorAnne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>, Ghislain Durif <ghislain.durif@univ-lyon1.fr>, Sophie Lambert-Lacroix <Sophie.Lambert@imag.fr>, Julie Peyre <Julie.Peyre@imag.fr>, and Korbinian Strimmer <strimmer@uni-leipzig.de>.
Date of publication2015-05-11 17:05:40
MaintainerGhislain Durif <ghislain.durif@univ-lyon1.fr>
LicenseGPL (>= 2)
Version1.3-1
http://cran.r-project.org/web/packages/plsgenomics/index.html

View on CRAN

Man pages

Colon: Gene expression data from Alon et al. (1999)

Ecoli: Ecoli gene expression and connectivity data from Kao et al....

gsim: GSIM for binary data

gsim.cv: Determination of the ridge regularization parameter and the...

leukemia: Gene expression data from Golub et al. (1999)

mgsim: GSIM for categorical data

mgsim.cv: Determination of the ridge regularization parameter and the...

mrpls: Ridge Partial Least Square for categorical data

mrpls.cv: Determination of the ridge regularization parameter and the...

plsgenomics-internal: Internal plsgenomics Functions

pls.lda: Classification with PLS Dimension Reduction and Linear...

pls.lda.cv: Determination of the number of latent components to be used...

pls.regression: Multivariate Partial Least Squares Regression

pls.regression.cv: Determination of the number of latent components to be used...

preprocess: preprocess for microarray data

rirls.spls: Classification by Ridge Iteratively Reweighted Least Squares...

rirls.spls.tune: Tuning parameters (ncomp, lambda.l1, lambda.ridge) for Ridge...

rpls: Ridge Partial Least Square for binary data

rpls.cv: Determination of the ridge regularization parameter and the...

sample.bin: Generates design matrix X with correlated block of covariates...

sample.cont: Generates design matrix X with correlated block of covariates...

spls.adapt: Classification by Ridge Iteratively Reweighted Least Squares...

spls.adapt.tune: Tuning parameters (ncomp, lambda.l1) for Adaptive Sparse PLS...

SRBCT: Gene expression data from Khan et al. (2001)

TFA.estimate: Prediction of Transcription Factor Activities using PLS

variable.selection: Variable selection using the PLS weights

Functions

Colon Man page
Ecoli Man page
gsim Man page
gsim.aux Man page
gsim.cv Man page
hplugin Man page
leukemia Man page
mgsim Man page
mgsimaux Man page
mgsim.cv Man page
mrpls Man page
mrplsaux Man page
mrpls.cv Man page
mwirrls Man page
pls.lda Man page
pls.lda.cv Man page
pls.lda.sample Man page
pls.regression Man page
pls.regression.cv Man page
pls.regression.sample Man page
preprocess Man page
rirls.spls Man page
rirls.spls.aux Man page
rirls.spls.tune Man page
rpls Man page
rplsaux Man page
rpls.cv Man page
sample.bin Man page
sample.cont Man page
spls.adapt Man page
spls.adapt.aux Man page
spls.adapt.tune Man page
SRBCT Man page
standard.simpls Man page
TFA.estimate Man page
transformy Man page
unitr.simpls Man page
ust Man page
ust.adapt Man page
variable.selection Man page
wirrls Man page
wpls Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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