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plsgenomics (version 1.5)

PLS Analyses for Genomics

Description

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: logit-SPLS; and an adaptive version of the sparse PLS.

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Version

Install

install.packages('plsgenomics')

Monthly Downloads

405

Version

1.5

License

GPL (>= 2)

Maintainer

Ghislain Durif

Last Published

August 30th, 2017

Functions in plsgenomics (1.5)

Colon

Gene expression data from Alon et al. (1999)
Ecoli

Ecoli gene expression and connectivity data from Kao et al. (2003)
logit.spls.cv

Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1, lambda.ridge) for the LOGIT-SPLS method
logit.spls.stab

Stability selection procedure to estimate probabilities of selection of covariates for the LOGIT-SPLS method
SRBCT

Gene expression data from Khan et al. (2001)
TFA.estimate

Prediction of Transcription Factor Activities using PLS
leukemia

Gene expression data from Golub et al. (1999)
logit.spls

Classification procedure for binary response based on a logistic model, solved by a combination of the Ridge Iteratively Reweighted Least Squares (RIRLS) algorithm and the Adaptive Sparse PLS (SPLS) regression
gsim

GSIM for binary data
gsim.cv

Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for binary data
pls.lda

Classification with PLS Dimension Reduction and Linear Discriminant Analysis
matrix.heatmap

Heatmap visualization for matrix
mgsim.cv

Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for categorical data
pls.lda.cv

Determination of the number of latent components to be used for classification with PLS and LDA
preprocess

preprocess for microarray data
rpls

Ridge Partial Least Square for binary data
mgsim

GSIM for categorical data
sample.cont

Generates design matrix X with correlated block of covariates and a continuous random reponse Y depening on X through gaussian linear model Y=XB+E
sample.multinom

Generates covariate matrix X with correlated block of covariates and a multi-label random reponse depening on X through a multinomial model
mrpls

Ridge Partial Least Square for categorical data
plsgenomics-deprecated

Deprecated function(s) in the 'plsgenomics' package
plsgenomics-internal

Internal Functions for the 'plsgenomics' package
stability.selection.heatmap

Heatmap visualization of estimated probabilities of selection for each covariate
variable.selection

Variable selection using the PLS weights
multinom.spls.cv

Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1, lambda.ridge) for the multinomial-SPLS method
multinom.spls.stab

Stability selection procedure to estimate probabilities of selection of covariates for the multinomial-SPLS method
pls.regression

Multivariate Partial Least Squares Regression
pls.regression.cv

Determination of the number of latent components to be used in PLS regression
spls.stab

Stability selection procedure to estimate probabilities of selection of covariates for the sparse PLS method
mrpls.cv

Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for categorical data
multinom.spls

Classification procedure for multi-label response based on a multinomial model, solved by a combination of the multinomial Ridge Iteratively Reweighted Least Squares (multinom-RIRLS) algorithm and the Adaptive Sparse PLS (SPLS) regression
rpls.cv

Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for binary data
sample.bin

Generates covariate matrix X with correlated block of covariates and a binary random reponse depening on X through a logistic model
spls

Adaptive Sparse Partial Least Squares (SPLS) regression
spls.cv

Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1) of the Adaptive Sparse PLS regression
stability.selection

Stability selection procedure to select covariates for the sparse PLS, LOGIT-SPLS and multinomial-SPLS methods