Package: plsgenomics 1.5-3

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

Authors:Anne-Laure Boulesteix <[email protected]>, Ghislain Durif <[email protected]>, Sophie Lambert-Lacroix <[email protected]>, Julie Peyre <[email protected]>, and Korbinian Strimmer <[email protected]>.

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plsgenomics.pdf |plsgenomics.html✨
plsgenomics/json (API)

# Install 'plsgenomics' in R:
install.packages('plsgenomics', repos = c('https://gdurif.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/gdurif/plsgenomics/issues

Datasets:
  • Colon - Gene expression data from Alon et al.
  • Ecoli - Ecoli gene expression and connectivity data from Kao et al.
  • SRBCT - Gene expression data from Khan et al.
  • leukemia - Gene expression data from Golub et al.

On CRAN:

5.53 score 2 packages 141 scripts 747 downloads 8 mentions 63 exports 19 dependencies

Last updated 7 months agofrom:810155ee02. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winOKOct 27 2024
R-4.5-linuxOKOct 27 2024
R-4.4-winOKOct 27 2024
R-4.4-macOKOct 27 2024
R-4.3-winOKOct 27 2024
R-4.3-macOKOct 27 2024

Exports:gsimgsim.auxgsim.cvhpluginlogit.plslogit.pls.cvlogit.splslogit.spls.auxlogit.spls.cvlogit.spls.stabm.rirls.splsm.rirls.spls.stabm.rirls.spls.tunematrix.heatmapmgsimmgsim.cvmgsimauxmrplsmrpls.cvmrplsauxmultinom.splsmultinom.spls.auxmultinom.spls.cvmultinom.spls.stabmwirrlspls.ldapls.lda.cvpls.lda.samplepls.regressionpls.regression.cvpls.regression.samplepreprocessrirls.splsrirls.spls.stabrirls.spls.tunerplsrpls.cvrplsauxsafeExpsafeExpMatsafeSumsample.binsample.contsample.multinomsoftMaxsplsspls.adaptspls.adapt.tunespls.auxspls.cvspls.inspls.stabstability.selectionstability.selection.heatmapstandard.simplsTFA.estimatetransformyunitr.simplsustust.adaptvariable.selectionwirrlswpls

Dependencies:bootclidotCall64fieldsgluelifecyclemagrittrmapsMASSplyrRcppreshape2RhpcBLASctlrlangspamstringistringrvctrsviridisLite

Readme and manuals

Help Manual

Help pageTopics
Gene expression data from Alon et al. (1999)Colon
Ecoli gene expression and connectivity data from Kao et al. (2003)Ecoli
GSIM for binary datagsim
Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for binary datagsim.cv
Gene expression data from Golub et al. (1999)leukemia
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) regressionlogit.spls
Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1, lambda.ridge) for the LOGIT-SPLS methodlogit.spls.cv
Stability selection procedure to estimate probabilities of selection of covariates for the LOGIT-SPLS methodlogit.spls.stab
Heatmap visualization for matrixmatrix.heatmap
GSIM for categorical datamgsim
Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for categorical datamgsim.cv
Ridge Partial Least Square for categorical datamrpls
Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for categorical datamrpls.cv
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) regressionmultinom.spls
Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1, lambda.ridge) for the multinomial-SPLS methodmultinom.spls.cv
Stability selection procedure to estimate probabilities of selection of covariates for the multinomial-SPLS methodmultinom.spls.stab
Classification with PLS Dimension Reduction and Linear Discriminant Analysispls.lda
Determination of the number of latent components to be used for classification with PLS and LDApls.lda.cv
Multivariate Partial Least Squares Regressionpls.regression
Determination of the number of latent components to be used in PLS regressionpls.regression.cv
Deprecated function(s) in the 'plsgenomics' packagem.rirls.spls m.rirls.spls.stab m.rirls.spls.tune plsgenomics-deprecated rirls.spls rirls.spls.stab rirls.spls.tune spls.adapt spls.adapt.tune
preprocess for microarray datapreprocess
Ridge Partial Least Square for binary datalogit.pls rpls
Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for binary datalogit.pls.cv rpls.cv
Generates covariate matrix X with correlated block of covariates and a binary random reponse depening on X through a logistic modelsample.bin
Generates design matrix X with correlated block of covariates and a continuous random reponse Y depening on X through gaussian linear model Y=XB+Esample.cont
Generates covariate matrix X with correlated block of covariates and a multi-label random reponse depening on X through a multinomial modelsample.multinom
Adaptive Sparse Partial Least Squares (SPLS) regressionspls
Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1) of the Adaptive Sparse PLS regressionspls.cv
Stability selection procedure to estimate probabilities of selection of covariates for the sparse PLS methodspls.stab
Gene expression data from Khan et al. (2001)SRBCT
Stability selection procedure to select covariates for the sparse PLS, LOGIT-SPLS and multinomial-SPLS methodsstability.selection
Heatmap visualization of estimated probabilities of selection for each covariatestability.selection.heatmap
Prediction of Transcription Factor Activities using PLSTFA.estimate
Variable selection using the PLS weightsvariable.selection