Title: | PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data |
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Description: | Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). |
Authors: | Etienne A. Thevenot [aut, cre]
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Maintainer: | Etienne A. Thevenot <[email protected]> |
License: | CeCILL |
Version: | 1.37.4 |
Built: | 2024-08-31 03:19:31 UTC |
Source: | https://github.com/bioc/ropls |
Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). Input formats include matrix, data frame, SummarizedExperiment, MultiAssayExperiment, ExpressionSet, MultiDataSet. The package can be accessed via a user interface on the Workflow4Metabolomics online resource built upon the Galaxy environment.
Etienne A. Thevenot (CEA)
Maintainer: Etienne A. Thevenot <[email protected]>
Quantitative structure property relationship (QSPR)
A data frame with the following parameters:
AA amino acid
PIE lipophilicity constant of the AA side chain
PIF lipophilicity constant of the AA side chain
DGR free energy of transfer of an AA side chain from protein interior to water
SAC water-accessible surface area of AA's calculated by MOLSV
MR molecular refractivity
Lam polarity parameter
Vol molecular volume of AA's calculated by MOLSV
DDGTS free energy of unfolding of the tryptophane synthase a unit of bacteriophage T4 lysosome
Data frame (numeric type except the first column, which can be transformed into row names) with 19 rows and the 9 columns contaning information about amino acids. For details see the 'Format' section above.
'aminoacids' dataset.
Wold et al. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems. 58:109-130.
The data were collected at Akzo Nobel, Ornkoldsvik (Sweden). The raw material for their cellulose derivative process is delivered to the factory in form of cellulose sheets. Before entering the process the cellulose sheets are controlled by a viscosity measurement, which functions as a steering parameter for that particular batch. In this data set NIR spectra for 180 cellulose sheets were collected after the sheets had been sent through a grinding process. Hence the NIR spectra were measured on the cellulose raw material in powder form. Data are divided in two parts, one used for modeling and one part for testing.
A list with the following elements:
nirMN a matrix of 180 samples x 1201 wavelengths in the VIS-NIR region
viscoVn a vector (length = 180) of viscosity of cellulose powder
classVn a vector (length = 180) of class membership (1 or 2)
For details see the Format section above.
Multivariate calibration using spectral data. Simca tutorial. Umetrics.
Checking the consistency of a SummarizedExperiment or ExpressionSet instance with W4M format
checkW4M(x) ## S4 method for signature 'SummarizedExperiment' checkW4M(x) ## S4 method for signature 'ExpressionSet' checkW4M(x)
checkW4M(x) ## S4 method for signature 'SummarizedExperiment' checkW4M(x) ## S4 method for signature 'ExpressionSet' checkW4M(x)
x |
An S4 object of class |
Invisible TRUE logical in case of success (otherwise generates an error)
Etienne Thevenot, [email protected]
sacSet <- fromW4M(file.path(path.package("ropls"), "extdata")) print(checkW4M(sacSet))
sacSet <- fromW4M(file.path(path.package("ropls"), "extdata")) print(checkW4M(sacSet))
Coefficients of the (O)PLS(-DA) regression model
## S4 method for signature 'opls' coef(object)
## S4 method for signature 'opls' coef(object)
object |
An S4 object of class |
Numeric matrix of coefficients (number of rows equals the number of variables, and the number of columns equals the number of responses)
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) head(coef(sacurine.plsda)) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) head(coef(sacurine.plsda)) detach(sacurine)
Twelve mixture component proportions of the blend are analysed
A data frame with the following parameters:
num mixture number
x1 proportion of component 1
x2 proportion of component 2
x3 proportion of component 3
x4 proportion of component 4
x5 proportion of component 5
x6 proportion of component 6
x7 proportion of component 7 Note: the 7 variables are correlated since they sum up to 1
y octane (quantitative variable)
Data frame (numeric type only; the first column can be transformed into row names) with 12 rows and 9 columns corresponding to the 'num'ber of the mixture (column 1), the proportion of each of the 7 'x' components within the mixture (columns 2-8), and the octane indice 'y' (column 9). For details see the 'Format' section above.
Tenenhaus (1998), Table 6, page 78.
Tenenhaus (1998). La regression PLS: theorie et pratique. Paris: Editions Technip.
Returns predictions of the (O)PLS(-DA) model on the training dataset
## S4 method for signature 'opls' fitted(object)
## S4 method for signature 'opls' fitted(object)
object |
An S4 object of class |
Predictions (either a vector, factor, or matrix depending on the y response used for training the model)
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) fitted(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) fitted(sacurine.plsda) detach(sacurine)
The relative consumption of 20 food items was compiled for 16 countries. The values range between 0 and 100 percent and a high value corresponds to a high consumption. The dataset contains 3 missing data.
A data frame with the following parameters:
Country Name of the country
Gr_CoffeGround Coffee
Inst_Coffe Instant Coffee
Tea Tea
Sweetner Sweetner
Biscuits Biscuits
Pa_Soup Powder Soup
Ti_Soup Tin Soup
In_Potat Instant Potatoes
Fro_Fish Frozen Fish
Fro_Veg Frozen Vegetables
Apples Apples
Oranges Oranges
Ti_Fruit Tin Fruit
Jam Jam
Garlic Garlic
Butter Butter
Margarine Margarine
Olive_Oil Olive Oil
Yoghurt Yoghurt
Crisp_Brea Crisp Bread
Data frame (numeric type except the first column, which can be transformed into row names) with 16 rows and 21 columns, corresponding to the 'Country' (column 1), followed by the consumption of each of the 20 food items (columns 2-21). For details see the 'Format' section above.
Eriksson et al. (2006). Multi- and Megarvariate Data Analysis. Umetrics Academy. pp.10, 33, 48.
Creating a ExpressionSet object from the 3 'dataMatrix.tsv', 'sampleMetadata.tsv' and 'variableMetadata.tsv' tabulated files
fromW4M( dirC, namePatternC = "", fileTableNamesVc = c("dataMatrix", "sampleMetadata", "variableMetadata"), outputC = c("exp", "set")[2], verboseL = TRUE )
fromW4M( dirC, namePatternC = "", fileTableNamesVc = c("dataMatrix", "sampleMetadata", "variableMetadata"), outputC = c("exp", "set")[2], verboseL = TRUE )
dirC |
Character: directory containing the 3 .tsv files |
namePatternC |
Character: optional file name pattern common to all three file names (e.g., when you want to distinguish between two sets of files within the same directory) |
fileTableNamesVc |
Vector of characters: if your file names do not contain the standard 'dataMatrix', 'sampleMetadata', and 'variableMetadata' patterns (e.g. if you use 'profile', 'observation', and 'feature' instead), please indicate them here |
outputC |
character(1): either 'exp' for SummarizedExperiment (default) or 'set' for ExpressionSet output format |
verboseL |
Logical: should comments be printed? |
ExpressionSet instance
Etienne Thevenot, [email protected]
sacSet <- fromW4M(file.path(path.package("ropls"), "extdata"))
sacSet <- fromW4M(file.path(path.package("ropls"), "extdata"))
Extracts the complemented ExpressionSet when opls has been applied to an ExpressionSet
getEset(object) ## S4 method for signature 'opls' getEset(object)
getEset(object) ## S4 method for signature 'opls' getEset(object)
object |
An S4 object of class |
An S4 object of class ExpressionSet
which contains the dataMatrix (t(exprs(eset))),
and the sampleMetadata (pData(eset)) and variableMetadata (fData(eset)) with the additional columns
containing the scores, predictions, loadings, VIP, coefficients etc.
Etienne Thevenot, [email protected]
data(sacurine) sacSet <- sacurine[["eset"]] sacPlsda <- opls(sacSet, "gender") sacSet <- getEset(sacPlsda) head(Biobase::pData(sacSet)) head(Biobase::fData(sacSet))
data(sacurine) sacSet <- sacurine[["eset"]] sacPlsda <- opls(sacSet, "gender") sacSet <- getEset(sacPlsda) head(Biobase::pData(sacSet)) head(Biobase::fData(sacSet))
(Orthogonal) loadings of the PCA/(O)PLS(-DA) model
getLoadingMN(object, orthoL = FALSE) ## S4 method for signature 'opls' getLoadingMN(object, orthoL = FALSE)
getLoadingMN(object, orthoL = FALSE) ## S4 method for signature 'opls' getLoadingMN(object, orthoL = FALSE)
object |
An S4 object of class |
orthoL |
Logical: Should the orthogonal loading matrix be returned (default is FALSE and the predictive loading matrix is returned) |
Numeric matrix with a number of rows equal to the number of variables and a number of columns equal to the number of components
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getLoadingMN(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getLoadingMN(sacurine.plsda) detach(sacurine)
Extracts the complemented MultiDataSet when opls has been applied to a MultiDataSet
getMset(object) ## S4 method for signature 'oplsMultiDataSet' getMset(object)
getMset(object) ## S4 method for signature 'oplsMultiDataSet' getMset(object)
object |
An S4 object of class |
An S4 object of class MultiDataSet
.
data(NCI60) nci.mds <- NCI60[["mds"]] # Restricting to the 'agilent' and 'hgu95' datasets nci.mds <- nci.mds[, c("agilent", "hgu95")] # Restricting to the 'ME' and 'LE' cancer types sampleNamesVc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancerTypeVc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sampleNamesVc[cancerTypeVc %in% c("ME", "LE")], ] # Principal Component Analysis of each data set nci.pca <- opls(nci.mds) # Getting the MultiDataSet with additional info. in pData and fData nci.mds <- getMset(nci.pca)
data(NCI60) nci.mds <- NCI60[["mds"]] # Restricting to the 'agilent' and 'hgu95' datasets nci.mds <- nci.mds[, c("agilent", "hgu95")] # Restricting to the 'ME' and 'LE' cancer types sampleNamesVc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancerTypeVc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sampleNamesVc[cancerTypeVc %in% c("ME", "LE")], ] # Principal Component Analysis of each data set nci.pca <- opls(nci.mds) # Getting the MultiDataSet with additional info. in pData and fData nci.mds <- getMset(nci.pca)
The models are extracted as a list
getOpls(object) ## S4 method for signature 'SummarizedExperiment' getOpls(object) ## S4 method for signature 'MultiAssayExperiment' getOpls(object)
getOpls(object) ## S4 method for signature 'SummarizedExperiment' getOpls(object) ## S4 method for signature 'MultiAssayExperiment' getOpls(object)
object |
An S4 object of class |
List of opls models contained in the SummarizedExperiment object(s)
Etienne Thevenot, [email protected]
# Getting the sacurine data set as a SummarizedExperiment data(sacurine) sac.se <- sacurine[["se"]] # Building the PLS-DA model sac.se <- opls(sac.se, "gender") # Getting the models sac_opls.ls <- getOpls(sac.se) names(sac_opls.ls) # Plotting the score plot from the PLS-DA model of the gender response plot(sac_opls.ls[["gender_PLSDA"]], typeVc = "x-score")
# Getting the sacurine data set as a SummarizedExperiment data(sacurine) sac.se <- sacurine[["se"]] # Building the PLS-DA model sac.se <- opls(sac.se, "gender") # Getting the models sac_opls.ls <- getOpls(sac.se) names(sac_opls.ls) # Plotting the score plot from the PLS-DA model of the gender response plot(sac_opls.ls[["gender_PLSDA"]], typeVc = "x-score")
Variance of the components (score vectors)
getPcaVarVn(object) ## S4 method for signature 'opls' getPcaVarVn(object)
getPcaVarVn(object) ## S4 method for signature 'opls' getPcaVarVn(object)
object |
An S4 object of class |
Numeric vector with the same length as the number of components
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.pca <- opls(dataMatrix) getPcaVarVn(sacurine.pca) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.pca <- opls(dataMatrix) getPcaVarVn(sacurine.pca) detach(sacurine)
(Orthogonal) scores of the (O)PLS(-DA) model
getScoreMN(object, orthoL = FALSE) ## S4 method for signature 'opls' getScoreMN(object, orthoL = FALSE)
getScoreMN(object, orthoL = FALSE) ## S4 method for signature 'opls' getScoreMN(object, orthoL = FALSE)
object |
An S4 object of class |
orthoL |
Logical: Should the orthogonal score matrix be returned (default is FALSE and the predictive score matrix is returned) |
Numeric matrix with a number of rows equal to the number of samples and a number of columns equal to the number of components
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getScoreMN(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getScoreMN(sacurine.plsda) detach(sacurine)
Extracts the indices of the samples used for building the model (when a subset argument has been specified)
getSubsetVi(object) ## S4 method for signature 'opls' getSubsetVi(object)
getSubsetVi(object) ## S4 method for signature 'opls' getSubsetVi(object)
object |
An S4 object of class |
Integer vector with the indices of the samples used for training
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) predictorMN <- dataMatrix responseFc <- sampleMetadata[, "gender"] sacurine.plsda <- opls(predictorMN, responseFc, subset = "odd") trainVi <- getSubsetVi(sacurine.plsda) table(responseFc[trainVi], fitted(sacurine.plsda)) detach(sacurine)
data(sacurine) attach(sacurine) predictorMN <- dataMatrix responseFc <- sampleMetadata[, "gender"] sacurine.plsda <- opls(predictorMN, responseFc, subset = "odd") trainVi <- getSubsetVi(sacurine.plsda) table(responseFc[trainVi], fitted(sacurine.plsda)) detach(sacurine)
Summary of model metrics
getSummaryDF(object) ## S4 method for signature 'opls' getSummaryDF(object)
getSummaryDF(object) ## S4 method for signature 'opls' getSummaryDF(object)
object |
An S4 object of class |
Data frame
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getSummaryDF(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getSummaryDF(sacurine.plsda) detach(sacurine)
(Orthogonal) VIP of the (O)PLS(-DA) model
getVipVn(object, orthoL = FALSE) ## S4 method for signature 'opls' getVipVn(object, orthoL = FALSE)
getVipVn(object, orthoL = FALSE) ## S4 method for signature 'opls' getVipVn(object, orthoL = FALSE)
object |
An S4 object of class |
orthoL |
Logical: Should the orthogonal VIP be returned (default is FALSE and the predictive VIP is returned) |
Numeric vector with a length equal to the number of variables and a number of columns equal to the number of components
Etienne Thevenot, [email protected]
Galindo-Prieto B., Eriksson L. and Trygg J. (2014). Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS). Journal of Chemometrics 28, 623-632.
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getVipVn(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getVipVn(sacurine.plsda) detach(sacurine)
(Orthogonal) weights of the (O)PLS(-DA) model
getWeightMN(object, orthoL = FALSE) ## S4 method for signature 'opls' getWeightMN(object, orthoL = FALSE)
getWeightMN(object, orthoL = FALSE) ## S4 method for signature 'opls' getWeightMN(object, orthoL = FALSE)
object |
An S4 object of class |
orthoL |
Logical: Should the orthogonal weight matrix be returned? (default is FALSE) |
Numeric matrix with a number of rows equal to the number of variables and a number of columns equal to the number of components
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getWeightMN(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) getWeightMN(sacurine.plsda) detach(sacurine)
Three physiological and three exercise variables are measured on twenty middle-aged men in a fitness club.
A data frame with the following parameters:
num subject number
weight weight
waist waist
pulse pulse
pullUp pull-up
squat situp
jump jump
Data frame (numeric type only; the first column can be transformed into row names) with 20 rows and 7 columns corresponding to the subject's 'num'ber (column 1), the 3 physiological variables (columns 2-4), and the 3 exercise variables (columns 5-7). For details see the 'Format' section above.
'mixOmics' 'linnerud' dataset.
Tenenhaus (1998). La regression PLS: theorie et pratique. Paris: Editions Technip.
This example concerns the development of a polymer similar to that used in the plastic covering of mobile phones. The desired profile of the polymer was low warp and high strength. Four constituents (glas, crtp, mica, and amtp) were varied in the polymer formulation by means of a 17 run mixture design. For each new polymer, i.e., each new experiment in the mixture design, 14 responses relating to both warp and strength were measured on the product. The objective of the data analysis was to uncover which combination of factors (the four ingredients) gave polymers with low warp and high strength. The data set contains 10 missing values (NA).
A data frame with the following parameters:
num mixture number
glas glas constituent
crtp crtp constituent
mica mica constituent
amtp amtp constituent
wrp1 warp response 1
wrp2 warp response 2
wrp3 warp response 3
wrp4 warp response 4
wrp5 warp response 5
wrp6 warp response 6
wrp7 warp response 7
wrp8 warp response 8
st1 strength response 1
st2 strength response 2
st3 strength response 3
st4 strength response 4
st5 strength response 5
st6 strength response 6
Data frame (numeric type only; the first column can be transformed into row names) with 17 rows and 19 columns corresponding to the subject's 'num'ber (column 1), the 4 constituent variables (columns 2-5), the 8 warp responses (columns 6-13), and the 6 strength responses (columns 14-19). For details see the 'Format' section above.
Eriksson et al. (2006). Multi- and Megarvariate Data Analysis. Umetrics Academy. pp.16, 77, 209.
Examination marks obtained by French students in Mathematics, Physics, French and English
A data frame with the following parameters:
nom names of the students
math marks in mathematics
phys marks in physics
fran marks in french
angl marks in english
Data frame (numeric type except the first column, which can be transformed into row names) with 9 rows and 5 columns, corresponding to the name of the students (column 1), followed by the marks obtained in Maths, Physics, French and English (columns 2-5). For details see the 'Format' section above.
'mark' dataset.
Baccini (2010). Statistique Descriptive Multidimensionnelle (pour les nuls).
The 'NCI60_4arrays' dataset from the 'omicade4' package is provided here in the MultiAssayExperiment and MultiDataSet formats. The description of the dataset provided by the omicade4 package is as follows: 'The 60 human tumour cell lines are derived from patients with leukaemia, melanoma, lung, colon, central nervous system, ovarian, renal, breast and prostate cancers. The cell line panel is widely used in anti-cancer drug screen. In this dataset, a subset of microarray gene expression of the NCI 60 cell lines from four different platforms are provided.'
A list with the following elements:
mae dataset in the MultiAssayExperiment format
mds dataset in the MultiDataSet format
List containing the NCI60_4arrays dataset from the omicade4 package in the MultiAssayExperiment and MultiDataSet formats.
Reinhold WC, Sunshine M, Liu H, Varma S, Kohn KW, Morris J, Doroshow J, Pommier Y (2012). CellMiner: A Web-Based Suite of Genomic and Pharmacologic Tools to Explore Transcript and Drug Patterns in the NCI-60 Cell Line Set. Cancer Research. DOI: 10.1158/0008-5472.CAN-12-1370
PCA, PLS, and OPLS regression, classification, and cross-validation with the NIPALS algorithm
opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'matrix' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'data.frame' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'SummarizedExperiment' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiAssayExperiment' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'ExpressionSet' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiDataSet' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] )
opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'matrix' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'data.frame' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'SummarizedExperiment' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiAssayExperiment' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'ExpressionSet' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiDataSet' opls( x, y = NULL, predI = NA, orthoI = 0, algoC = c("default", "nipals", "svd")[1], crossvalI = 7, log10L = FALSE, permI = 20, scaleC = c("none", "center", "pareto", "standard")[4], subset = NULL, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] )
x |
Numerical matrix, (observations x variables; NAs are allowed), data.frame, SummarizedExperiment or ExpressionSet object |
y |
Response to be modelled: Either 1) 'NULL' for PCA (default) or 2) a numerical vector (same length as 'x' row number) for single response (O)PLS, or 3) a numerical matrix (same row number as 'x') for multiple response PLS, 4) a factor (same length as 'x' row number) for (O)PLS-DA, or 5) a character indicating the name of the column of the phenoData@data to be used, when x is a SummarizedExperiment or an ExpressionSet object. Note that, for convenience, character vectors are also accepted for (O)PLS-DA as well as single column numerical (resp. character) matrix for (O)PLS (respectively (O)PLS-DA). NAs are allowed in numeric responses. |
predI |
Integer: number of components (predictive componenents in case of PLS and OPLS) to extract; for OPLS, predI is (automatically) set to 1; if set to NA [default], autofit is performed: a maximum of 10 components are extracted until (i) PCA case: the variance is less than the mean variance of all components (note that this rule requires all components to be computed and can be quite time-consuming for large datasets) or (ii) PLS case: either R2Y of the component is < 0.01 (N4 rule) or Q2Y is < 0 (for more than 100 observations) or 0.05 otherwise (R1 rule) |
orthoI |
Integer: number of orthogonal components (for OPLS only); when set to 0 [default], PLS will be performed; otherwise OPLS will be peformed; when set to NA, OPLS is performed and the number of orthogonal components is automatically computed by using the cross-validation (with a maximum of 9 orthogonal components). |
algoC |
Default algorithm is 'svd' for PCA (in case of no missing values in 'x'; 'nipals' otherwise) and 'nipals' for PLS and OPLS; when asking to use 'svd' for PCA on an 'x' matrix containing missing values, NAs are set to half the minimum of non-missing values and a warning is generated |
crossvalI |
Integer: number of cross-validation segments (default is 7); The number of samples (rows of 'x') must be at least >= crossvalI |
log10L |
Should the 'x' matrix be log10 transformed? Zeros are set to 1 prior to transformation |
permI |
Integer: number of random permutations of response labels to estimate R2Y and Q2Y significance by permutation testing [default is 20 for single response models (without train/test partition), and 0 otherwise] |
scaleC |
Character: either no centering nor scaling ('none'), mean-centering only ('center'), mean-centering and pareto scaling ('pareto'), or mean-centering and unit variance scaling ('standard') [default] |
subset |
Integer vector: indices of the observations to be used for training (in a classification scheme); use NULL [default] for no partition of the dataset; use 'odd' for a partition of the dataset in two equal sizes (with respect to the classes proportions) |
plotSubC |
Character: Graphic subtitle |
fig.pdfC |
Character: File name with '.pdf' extension for the figure; if 'interactive' (default), figures will be displayed interactively; if 'none', no figure will be generated |
info.txtC |
Character: File name with '.txt' extension for the printed results (call to sink()'); if 'interactive' (default), messages will be printed on the screen; if 'none', no verbose will be generated |
An S4 object of class 'opls' containing the slots described below; in case x is a SummarizedExperiment, a SummarizedExperiment is returned, with the 'opls' object included in the metadata:
typeC Character: model type (PCA, PLS, PLS-DA, OPLS, or OPLS-DA)
descriptionMC Character matrix: Description of the data set (number of samples, variables, etc.)
modelDF Data frame with the model overview (number of components, R2X, R2X(cum), R2Y, R2Y(cum), Q2, Q2(cum), significance, iterations)
summaryDF Data frame with the model summary (cumulated R2X, R2Y and Q2); RMSEE is the square root of the mean error between the actual and the predicted responses
subsetVi Integer vector: Indices of observations in the training data set
pcaVarVn PCA: Numerical vector of variances of length: predI
vipVn PLS(-DA): Numerical vector of Variable Importance in Projection; OPLS(-DA): Numerical vector of Variable Importance for Prediction (VIP4,p from Galindo-Prieto et al, 2014)
orthoVipVn OPLS(-DA): Numerical vector of Variable Importance for Orthogonal Modeling (VIP4,o from Galindo-Prieto et al, 2014)
xMeanVn Numerical vector: variable means of the 'x' matrix
xSdVn Numerical vector: variable standard deviations of the 'x' matrix
yMeanVn (O)PLS: Numerical vector: variable means of the 'y' response (transformed into a dummy matrix in case it is of 'character' mode initially)
ySdVn (O)PLS: Numerical vector: variable standard deviations of the 'y' response (transformed into a dummy matrix in case it is of 'character' mode initially)
xZeroVarVi Numerical vector: indices of variables with variance < 2.22e-16 which were excluded from 'x' before building the model
scoreMN Numerical matrix of x scores (T; dimensions: nrow(x) x predI) X = TP' + E; Y = TC' + F
loadingMN Numerical matrix of x loadings (P; dimensions: ncol(x) x predI) X = TP' + E
weightMN (O)PLS: Numerical matrix of x weights (W; same dimensions as loadingMN)
orthoScoreMN OPLS: Numerical matrix of orthogonal scores (Tortho; dimensions: nrow(x) x number of orthogonal components)
orthoLoadingMN OPLS: Numerical matrix of orthogonal loadings (Portho; dimensions: ncol(x) x number of orthogonal components)
orthoWeightMN OPLS: Numerical matrix of orthogonal weights (same dimensions as orthoLoadingMN)
cMN (O)PLS: Numerical matrix of Y weights (C; dimensions: number of responses or number of classes in case of qualitative response) x number of predictive components; Y = TC' + F
coMN) (O)PLS: Numerical matrix of Y orthogonal weights; dimensions: number of responses or number of classes in case of qualitative response with more than 2 classes x number of orthogonal components
uMN (O)PLS: Numerical matrix of Y scores (U; same dimensions as scoreMN); Y = UC' + G
weightStarMN Numerical matrix of projections (W*; same dimensions as loadingMN); whereas columns of weightMN are derived from successively deflated matrices, columns of weightStarMN relate to the original 'x' matrix: T = XW*; W*=W(P'W)inv
suppLs List of additional objects to be used internally by the 'print', 'plot', and 'predict' methods
Etienne Thevenot, [email protected]
Eriksson et al. (2006). Multi- and Megarvariate Data Analysis. Umetrics Academy. Rosipal and Kramer (2006). Overview and recent advances in partial least squares Tenenhaus (1990). La regression PLS : theorie et pratique. Technip. Wehrens (2011). Chemometrics with R. Springer. Wold et al. (2001). PLS-regression: a basic tool of chemometrics
## PCA data(foods) ## see Eriksson et al. (2001); presence of 3 missing values (NA) head(foods) foodMN <- as.matrix(foods[, colnames(foods) != "Country"]) rownames(foodMN) <- foods[, "Country"] head(foodMN) foo.pca <- opls(foodMN) ## PLS with a single response data(cornell) ## see Tenenhaus, 1998 head(cornell) cornell.pls <- opls(as.matrix(cornell[, grep("x", colnames(cornell))]), cornell[, "y"]) ## Complementary graphics plot(cornell.pls, typeVc = c("outlier", "predict-train", "xy-score", "xy-weight")) #### PLS with multiple (quantitative) responses data(lowarp) ## see Eriksson et al. (2001); presence of NAs head(lowarp) lowarp.pls <- opls(as.matrix(lowarp[, c("glas", "crtp", "mica", "amtp")]), as.matrix(lowarp[, grepl("^wrp", colnames(lowarp)) | grepl("^st", colnames(lowarp))])) ## PLS-DA data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) ## OPLS-DA sacurine.oplsda <- opls(dataMatrix, sampleMetadata[, "gender"], predI = 1, orthoI = NA) detach(sacurine) ## Application to a SummarizedExperiment sac.se <- sacurine[["se"]] sac.se <- opls(sac.se, "gender") SummarizedExperiment::colData(sac.se) SummarizedExperiment::rowData(sac.se) sac_gender.plsda <- sac.se@metadata[["opls"]][["gender_PLSDA"]] plot(sac_gender.plsda, typeVc = "x-score") ## Application to a MultiAssayExperiment data(NCI60) nci.mae <- NCI60[["mae"]] # Restricting to the 'ME' and 'LE' cancer types and to the 'agilent' and 'hgu95' datasets library(MultiAssayExperiment) nci.mae <- nci.mae[, nci.mae$cancer %in% c("ME", "LE"), c("agilent", "hgu95")] # Principal Component Analysis of each data set nci.mae <- opls(nci.mae) # Coloring the score plots according to cancer types for (set.c in names(nci.mae)) plot(getOpls(nci.mae)[[set.c]][["PCA"]], parAsColFcVn = MultiAssayExperiment::colData(nci.mae)[, "cancer"], typeVc = "x-score", plotSubC = set.c) # Building PLS-DA models for the cancer type, and getting back the updated MultiDataSet nci.mae <- opls(nci.mae, "cancer", predI = 2) # Viewing the new variable metadata (including VIP and coefficients) lapply(names(nci.mae), function(set.c) head(SummarizedExperiment::rowData(nci.mae[[set.c]]))) ## Application to an ExpressionSet sacSet <- sacurine[["eset"]] sacPlsda <- opls(sacSet, "gender") sacSet <- getEset(sacPlsda) head(Biobase::pData(sacSet)) head(Biobase::fData(sacSet)) ## Application to a MultiDataSet data(NCI60) nci.mds <- NCI60[["mds"]] # Restricting to the 'agilent' and 'hgu95' datasets nci.mds <- nci.mds[, c("agilent", "hgu95")] # Restricting to the 'ME' and 'LE' cancer types sampleNamesVc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancerTypeVc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sampleNamesVc[cancerTypeVc %in% c("ME", "LE")], ] # Principal Component Analysis of each data set nci.pca <- opls(nci.mds) # Coloring the Score plot according to cancer types plot(nci.pca, parAsColFcVn = Biobase::pData(nci.mds[["agilent"]])[, "cancer"], typeVc = "x-score") # Getting the updated MultiDataSet (now including scores and loadings) nci.mds <- getMset(nci.pca) # Building PLS-DA models for the cancer type, and getting back the updated MultiDataSet nci.plsda <- opls(nci.mds, "cancer", predI = 2) nci.mds <- getMset(nci.plsda) # Viewing the new variable metadata (including VIP and coefficients) lapply(Biobase::fData(nci.mds), head)
## PCA data(foods) ## see Eriksson et al. (2001); presence of 3 missing values (NA) head(foods) foodMN <- as.matrix(foods[, colnames(foods) != "Country"]) rownames(foodMN) <- foods[, "Country"] head(foodMN) foo.pca <- opls(foodMN) ## PLS with a single response data(cornell) ## see Tenenhaus, 1998 head(cornell) cornell.pls <- opls(as.matrix(cornell[, grep("x", colnames(cornell))]), cornell[, "y"]) ## Complementary graphics plot(cornell.pls, typeVc = c("outlier", "predict-train", "xy-score", "xy-weight")) #### PLS with multiple (quantitative) responses data(lowarp) ## see Eriksson et al. (2001); presence of NAs head(lowarp) lowarp.pls <- opls(as.matrix(lowarp[, c("glas", "crtp", "mica", "amtp")]), as.matrix(lowarp[, grepl("^wrp", colnames(lowarp)) | grepl("^st", colnames(lowarp))])) ## PLS-DA data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) ## OPLS-DA sacurine.oplsda <- opls(dataMatrix, sampleMetadata[, "gender"], predI = 1, orthoI = NA) detach(sacurine) ## Application to a SummarizedExperiment sac.se <- sacurine[["se"]] sac.se <- opls(sac.se, "gender") SummarizedExperiment::colData(sac.se) SummarizedExperiment::rowData(sac.se) sac_gender.plsda <- sac.se@metadata[["opls"]][["gender_PLSDA"]] plot(sac_gender.plsda, typeVc = "x-score") ## Application to a MultiAssayExperiment data(NCI60) nci.mae <- NCI60[["mae"]] # Restricting to the 'ME' and 'LE' cancer types and to the 'agilent' and 'hgu95' datasets library(MultiAssayExperiment) nci.mae <- nci.mae[, nci.mae$cancer %in% c("ME", "LE"), c("agilent", "hgu95")] # Principal Component Analysis of each data set nci.mae <- opls(nci.mae) # Coloring the score plots according to cancer types for (set.c in names(nci.mae)) plot(getOpls(nci.mae)[[set.c]][["PCA"]], parAsColFcVn = MultiAssayExperiment::colData(nci.mae)[, "cancer"], typeVc = "x-score", plotSubC = set.c) # Building PLS-DA models for the cancer type, and getting back the updated MultiDataSet nci.mae <- opls(nci.mae, "cancer", predI = 2) # Viewing the new variable metadata (including VIP and coefficients) lapply(names(nci.mae), function(set.c) head(SummarizedExperiment::rowData(nci.mae[[set.c]]))) ## Application to an ExpressionSet sacSet <- sacurine[["eset"]] sacPlsda <- opls(sacSet, "gender") sacSet <- getEset(sacPlsda) head(Biobase::pData(sacSet)) head(Biobase::fData(sacSet)) ## Application to a MultiDataSet data(NCI60) nci.mds <- NCI60[["mds"]] # Restricting to the 'agilent' and 'hgu95' datasets nci.mds <- nci.mds[, c("agilent", "hgu95")] # Restricting to the 'ME' and 'LE' cancer types sampleNamesVc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancerTypeVc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sampleNamesVc[cancerTypeVc %in% c("ME", "LE")], ] # Principal Component Analysis of each data set nci.pca <- opls(nci.mds) # Coloring the Score plot according to cancer types plot(nci.pca, parAsColFcVn = Biobase::pData(nci.mds[["agilent"]])[, "cancer"], typeVc = "x-score") # Getting the updated MultiDataSet (now including scores and loadings) nci.mds <- getMset(nci.pca) # Building PLS-DA models for the cancer type, and getting back the updated MultiDataSet nci.plsda <- opls(nci.mds, "cancer", predI = 2) nci.mds <- getMset(nci.plsda) # Viewing the new variable metadata (including VIP and coefficients) lapply(Biobase::fData(nci.mds), head)
An S4 class to store PCA and (O)PLS(-DA) models: Objects can be created by calls of the form
new("opls")
or by calling the opls
function
typeC
character: model type (PCA, PLS, PLS-DA, OPLS, or OPLS-DA)
descriptionMC
character matrix: Description of the data set (number of samples, variables, etc.)
modelDF
data frame with the model overview (number of components, R2X, R2X(cum), R2Y, R2Y(cum), Q2, Q2(cum), significance, iterations)
summaryDF
data frame with the model summary (cumulated R2X, R2Y and Q2); RMSEE is the square root of the mean error between the actual and the predicted responses
subsetVi
Integer vector: Indices of observations in the training data set
pcaVarVn
PCA: Numerical vector of variances of length: predI
vipVn
PLS(-DA): Numerical vector of Variable Importance in Projection; OPLS(-DA): Numerical vector of Variable Importance for Prediction (VIP4,p from Galindo-Prieto et al, 2014)
orthoVipVn
OPLS(-DA): Numerical vector of Variable Importance for Orthogonal Modeling (VIP4,o from Galindo-Prieto et al, 2014)
coefficientMN
(O)PLS(-DA): Numerical matrix of regression coefficients (B; dimensions: ncol(x) x number of responses; B = W*C' and Y = XB + F
xMeanVn
Numerical vector: variable means of the 'x' matrix
xSdVn
Numerical vector: variable standard deviations of the 'x' matrix
yMeanVn
(O)PLS: Numerical vector: variable means of the 'y' response (transformed into a dummy matrix in case it is of 'character' mode initially)
ySdVn
(O)PLS: Numerical vector: variable standard deviations of the 'y' response (transformed into a dummy matrix in case it is of 'character' mode initially)
xZeroVarVi
Numerical vector: indices of variables with variance < 2.22e-16 which were excluded from 'x' before building the model
scoreMN
Numerical matrix of x scores (T; dimensions: nrow(x) x predI) X = TP' + E; Y = TC' + F
loadingMN
Numerical matrix of x loadings (P; dimensions: ncol(x) x predI) X = TP' + E
weightMN
(O)PLS: Numerical matrix of x weights (W; same dimensions as loadingMN)
orthoScoreMN
OPLS: Numerical matrix of orthogonal scores (Tortho; dimensions: nrow(x) x number of orthogonal components)
orthoLoadingMN
OPLS: Numerical matrix of orthogonal loadings (Portho; dimensions: ncol(x) x number of orthogonal components)
orthoWeightMN
OPLS: Numerical matrix of orthogonal weights (same dimensions as orthoLoadingMN)
cMN
(O)PLS: Numerical matrix of Y weights (C); dimensions: number of responses or number of classes in case of qualitative response with more than 2 classes x number of predictive components; Y = TC' + F
coMN
(O)PLS: Numerical matrix of Y orthogonal weights; dimensions: number of responses or number of classes in case of qualitative response with more than 2 classes x number of orthogonal components
uMN
(O)PLS: Numerical matrix of Y scores (U; same dimensions as scoreMN); Y = UC' + G
weightStarMN
Numerical matrix of projections (W*; same dimensions as loadingMN); whereas columns of weightMN are derived from successively deflated matrices, columns of weightStarMN relate to the original 'x' matrix: T = XW*; W*=W(P'W)inv
suppLs
List of additional objects to be used internally by the 'print', 'plot', and 'predict' methods
eset
ExpressionSet: when 'opls' has been applied to an ExpressionSet, the instance with additional columns in pData containing the scores and predictions, and in fData containing the loadings, VIP, coefficients is stored here
Objects can be created by calls of the form
new("opls")
or by calling the opls
function
Etienne Thevenot, [email protected]
#### PCA data(foods) ## see Eriksson et al. (2001); presence of 3 missing values (NA) head(foods) foodMN <- as.matrix(foods[, colnames(foods) != "Country"]) rownames(foodMN) <- foods[, "Country"] head(foodMN) foo.pca <- opls(foodMN) #### PLS with a single response data(cornell) ## see Tenenhaus, 1998 head(cornell) cornell.pls <- opls(as.matrix(cornell[, grep("x", colnames(cornell))]), cornell[, "y"]) ## Complementary graphics plot(cornell.pls, typeVc = c("outlier", "predict-train", "xy-score", "xy-weight")) #### PLS with multiple (quantitative) responses data(lowarp) ## see Eriksson et al. (2001); presence of NAs head(lowarp) lowarp.pls <- opls(as.matrix(lowarp[, c("glas", "crtp", "mica", "amtp")]), as.matrix(lowarp[, grepl("^wrp", colnames(lowarp)) | grepl("^st", colnames(lowarp))])) #### PLS-DA data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) #### OPLS-DA sacurine.oplsda <- opls(dataMatrix, sampleMetadata[, "gender"], predI = 1, orthoI = NA) detach(sacurine)
#### PCA data(foods) ## see Eriksson et al. (2001); presence of 3 missing values (NA) head(foods) foodMN <- as.matrix(foods[, colnames(foods) != "Country"]) rownames(foodMN) <- foods[, "Country"] head(foodMN) foo.pca <- opls(foodMN) #### PLS with a single response data(cornell) ## see Tenenhaus, 1998 head(cornell) cornell.pls <- opls(as.matrix(cornell[, grep("x", colnames(cornell))]), cornell[, "y"]) ## Complementary graphics plot(cornell.pls, typeVc = c("outlier", "predict-train", "xy-score", "xy-weight")) #### PLS with multiple (quantitative) responses data(lowarp) ## see Eriksson et al. (2001); presence of NAs head(lowarp) lowarp.pls <- opls(as.matrix(lowarp[, c("glas", "crtp", "mica", "amtp")]), as.matrix(lowarp[, grepl("^wrp", colnames(lowarp)) | grepl("^st", colnames(lowarp))])) #### PLS-DA data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) #### OPLS-DA sacurine.oplsda <- opls(dataMatrix, sampleMetadata[, "gender"], predI = 1, orthoI = NA) detach(sacurine)
An S4 class to store PCA and (O)PLS(-DA) models generated by the application
of opls
to a MultiDataSet
oplsLs
List: List of instances from the 'opls' class corresponding to the models built on each ExpresssionSet
Objects can be created by calls of the form
new("oplsMultiDataSet")
or by applying the opls
function to
a MultiDataSet instance
Etienne Thevenot, [email protected]
# In progress
# In progress
Score plot visualization for PCA and (O)PLS(-DA) models in either ggplot or ggplotly formats
plot_score( x, model.c = "", components.vi = c(1, 2), label.c = c("", "sample_names")[2], color.c = "", title.c = "", palette.c = "Set1", legend.c = "right", ellipse.l = TRUE, plotly.l = FALSE, info.vc = "sample_names", size.ls = list(axis_lab.i = 16, axis_text.i = 14, point.i = 3, label.i = 5, title.i = 20, legend_title.i = 15, legend_text.i = 15) ) ## S4 method for signature 'SummarizedExperiment' plot_score( x, model.c = "", components.vi = c(1, 2), label.c = c("", "sample_names")[2], color.c = "", title.c = "", palette.c = "Set1", legend.c = "right", ellipse.l = TRUE, plotly.l = FALSE, info.vc = "sample_names", size.ls = list(axis_lab.i = 16, axis_text.i = 14, point.i = 3, label.i = 5, title.i = 20, legend_title.i = 15, legend_text.i = 15) ) ## S4 method for signature 'opls' plot_score( x, model.c = "", components.vi = c(1, 2), label.c = c("", "sample_names")[2], color.c = "", title.c = "", palette.c = "Set1", legend.c = "right", ellipse.l = TRUE, plotly.l = FALSE, info.vc = "sample_names", size.ls = list(axis_lab.i = 16, axis_text.i = 14, point.i = 3, label.i = 5, title.i = 20, legend_title.i = 15, legend_text.i = 15) )
plot_score( x, model.c = "", components.vi = c(1, 2), label.c = c("", "sample_names")[2], color.c = "", title.c = "", palette.c = "Set1", legend.c = "right", ellipse.l = TRUE, plotly.l = FALSE, info.vc = "sample_names", size.ls = list(axis_lab.i = 16, axis_text.i = 14, point.i = 3, label.i = 5, title.i = 20, legend_title.i = 15, legend_text.i = 15) ) ## S4 method for signature 'SummarizedExperiment' plot_score( x, model.c = "", components.vi = c(1, 2), label.c = c("", "sample_names")[2], color.c = "", title.c = "", palette.c = "Set1", legend.c = "right", ellipse.l = TRUE, plotly.l = FALSE, info.vc = "sample_names", size.ls = list(axis_lab.i = 16, axis_text.i = 14, point.i = 3, label.i = 5, title.i = 20, legend_title.i = 15, legend_text.i = 15) ) ## S4 method for signature 'opls' plot_score( x, model.c = "", components.vi = c(1, 2), label.c = c("", "sample_names")[2], color.c = "", title.c = "", palette.c = "Set1", legend.c = "right", ellipse.l = TRUE, plotly.l = FALSE, info.vc = "sample_names", size.ls = list(axis_lab.i = 16, axis_text.i = 14, point.i = 3, label.i = 5, title.i = 20, legend_title.i = 15, legend_text.i = 15) )
x |
An S4 object of class |
model.c |
character(1): name of the model to be plotted; use 'names(ropls::getOpls(se))' to see the available models in the se object |
components.vi |
integer(2): number of the components to display as x and y axis |
label.c |
character(1): name of the colData (resp. pData) column to be used for the labels |
color.c |
character(1): name of the colData (resp. pData) column to be used for the colors |
title.c |
character(1): plot title |
palette.c |
character(1): name of the RColorBrewer palette (for qualitative factor) |
legend.c |
character(1): position of the legend (either 'bottom', 'left', 'top' or 'right' [default]) |
ellipse.l |
logical(1): should ellipses be drawn (for qualitative factor) |
plotly.l |
logical(1): should the ggplot be converted to an interactive plotly (default: FALSE) |
info.vc |
character(): names of the colData (resp. pData) columns to be used for the plotly info; the default 'sample_names' will return the sample names as the plotly info |
size.ls |
list: sizes for axis labels (default: 16), axis text (default: 14), points (default: 3), labels (default = 5), title (default = 20), legend title (default: 15), legend text (default: 15) |
invisible ggplot2 (or ggplotly) object
# loading the 'sacurine' dataset from the 'ropls' package data(sacurine, package = "ropls") # SummarizedExperiment sac.se <- sacurine[["se"]] ## computing the PCA sac.se <- opls(sac.se) ## score plot plot_score(sac.se, "PCA") plot_score(sac.se, "PCA", color.c = "age") plot_score(sac.se, "PCA", color.c = "gender", plotly.l = TRUE, info.vc = "all") ## PLS-DA modeling sac.se <- opls(sac.se, "gender") plot_score(sac.se, "gender_PLSDA") plot_score(sac.se, "gender_PLSDA", plotly.l = TRUE) ## OPLS-DA modeling sac.se <- opls(sac.se, "gender", predI = 1, orthoI = NA) plot_score(sac.se, "gender_OPLSDA") plot_score(sac.se, "gender_OPLSDA", plotly.l = TRUE, info.vc = "all") # empty plot (in case no model was built) rand.se <- sac.se rand.se$gender <- sample(rand.se$gender) rand.se <- opls(rand.se, "gender") plot_score(rand.se, "gender_PLSDA") # 1D plot (in case of a single predicted component) single.se <- opls(sac.se, predI = 1, fig.pdfC = "none") plot_score(single.se, "PCA", label.c = "") single.se <- opls(sac.se, "gender", predI = 1, fig.pdfC = "none") plot_score(single.se, "gender_PLSDA") # ExpressionSet sacurine.eset <- sacurine[["eset"]] ## PCA sacurine.pca <- opls(sacurine.eset) ## score plot (model.c does not need to be specified here since 'opls' objects contain only one model) plot_score(sacurine.pca) plot_score(sacurine.pca, color.c = "age")
# loading the 'sacurine' dataset from the 'ropls' package data(sacurine, package = "ropls") # SummarizedExperiment sac.se <- sacurine[["se"]] ## computing the PCA sac.se <- opls(sac.se) ## score plot plot_score(sac.se, "PCA") plot_score(sac.se, "PCA", color.c = "age") plot_score(sac.se, "PCA", color.c = "gender", plotly.l = TRUE, info.vc = "all") ## PLS-DA modeling sac.se <- opls(sac.se, "gender") plot_score(sac.se, "gender_PLSDA") plot_score(sac.se, "gender_PLSDA", plotly.l = TRUE) ## OPLS-DA modeling sac.se <- opls(sac.se, "gender", predI = 1, orthoI = NA) plot_score(sac.se, "gender_OPLSDA") plot_score(sac.se, "gender_OPLSDA", plotly.l = TRUE, info.vc = "all") # empty plot (in case no model was built) rand.se <- sac.se rand.se$gender <- sample(rand.se$gender) rand.se <- opls(rand.se, "gender") plot_score(rand.se, "gender_PLSDA") # 1D plot (in case of a single predicted component) single.se <- opls(sac.se, predI = 1, fig.pdfC = "none") plot_score(single.se, "PCA", label.c = "") single.se <- opls(sac.se, "gender", predI = 1, fig.pdfC = "none") plot_score(single.se, "gender_PLSDA") # ExpressionSet sacurine.eset <- sacurine[["eset"]] ## PCA sacurine.pca <- opls(sacurine.eset) ## score plot (model.c does not need to be specified here since 'opls' objects contain only one model) plot_score(sacurine.pca) plot_score(sacurine.pca, color.c = "age")
This function plots values based upon a model trained by opls
.
## S4 method for signature 'oplsMultiDataSet,ANY' plot( x, y, typeVc = c("correlation", "outlier", "overview", "permutation", "predict-train", "predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")[7], parAsColFcVn = NA, parCexN = 0.8, parCompVi = c(1, 2), parEllipsesL = NA, parLabVc = NA, parPaletteVc = NA, parTitleL = TRUE, parCexMetricN = NA, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'opls,ANY' plot( x, y, typeVc = c("correlation", "outlier", "overview", "permutation", "predict-train", "predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")[7], parAsColFcVn = NA, parCexN = 0.8, parCompVi = c(1, 2), parEllipsesL = NA, parLabVc = NA, parPaletteVc = NA, parTitleL = TRUE, parCexMetricN = NA, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] )
## S4 method for signature 'oplsMultiDataSet,ANY' plot( x, y, typeVc = c("correlation", "outlier", "overview", "permutation", "predict-train", "predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")[7], parAsColFcVn = NA, parCexN = 0.8, parCompVi = c(1, 2), parEllipsesL = NA, parLabVc = NA, parPaletteVc = NA, parTitleL = TRUE, parCexMetricN = NA, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'opls,ANY' plot( x, y, typeVc = c("correlation", "outlier", "overview", "permutation", "predict-train", "predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")[7], parAsColFcVn = NA, parCexN = 0.8, parCompVi = c(1, 2), parEllipsesL = NA, parLabVc = NA, parPaletteVc = NA, parTitleL = TRUE, parCexMetricN = NA, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] )
x |
An S4 object of class |
y |
Currently not used |
typeVc |
Character vector: the following plots are available: 'correlation': Variable correlations with the components, 'outlier': Observation diagnostics (score and orthogonal distances), 'overview': Model overview showing R2Ycum and Q2cum (or 'Variance explained' for PCA), 'permutation': Scatterplot of R2Y and Q2Y actual and simulated models after random permutation of response values; 'predict-train' and 'predict-test': Predicted vs Actual Y for reference and test sets (only if Y has a single column), 'summary' [default]: 4-plot summary showing permutation, overview, outlier, and x-score together, 'x-variance': Spread of raw variables corresp. with min, median, and max variances, 'x-loading': X-loadings (the 6 of variables most contributing to loadings are colored in red to facilitate interpretation), 'x-score': X-Scores, 'xy-score': XY-Scores, 'xy-weight': XY-Weights |
parAsColFcVn |
Optional factor character or numeric vector to be converted into colors for the score plot; default is NA [ie colors will be converted from 'y' in case of (O)PLS(-DA) or will be 'black' for PCA] |
parCexN |
Numeric: amount by which plotting text should be magnified relative to the default |
parCompVi |
Integer vector of length 2: indices of the two components to be displayed on the score plot (first two components by default) |
parEllipsesL |
Should the Mahalanobis ellipses be drawn? If 'NA' [default], ellipses are drawn when either a character parAsColVcn is provided (PCA case), or when 'y' is a character factor ((O)PLS-DA cases). |
parLabVc |
Optional character vector for the labels of observations on the plot; default is NA [ie row names of 'x', if available, or indices of 'x', otherwise, will be used] |
parPaletteVc |
Optional character vector of colors to be used in the plots |
parTitleL |
Should the titles of the plots be printed on the graphics (default = TRUE); It may be convenient to set this argument to FALSE when the user wishes to add specific titles a posteriori |
parCexMetricN |
Numeric: magnification of the metrics at the bottom of score plot (default -NA- is 1 in 1x1 and 0.7 in 2x2 display) |
plotSubC |
Character: Graphic subtitle |
fig.pdfC |
Character: File name with '.pdf' extension for the figure; if 'interactive' (default), figures will be displayed interactively; if 'none', no figure will be generated |
info.txtC |
Character: File name with '.txt' extension for the printed results (call to sink()'); if 'interactive' (default), messages will be printed on the screen; if 'none', no verbose will be generated |
data(sacurine) attach(sacurine) for(typeC in c("correlation", "outlier", "overview", "permutation", "predict-train","predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")) { print(typeC) if(grepl("predict", typeC)) subset <- "odd" else subset <- NULL plsModel <- opls(dataMatrix, sampleMetadata[, "gender"], predI = ifelse(typeC != "xy-weight", 1, 2), orthoI = ifelse(typeC != "xy-weight", 1, 0), permI = ifelse(typeC == "permutation", 10, 0), subset = subset, info.txtC = "none", fig.pdfC = "none") plot(plsModel, typeVc = typeC) } sacPlsda <- opls(dataMatrix, sampleMetadata[, "gender"]) plot(sacPlsda, parPaletteVc = c("green4", "magenta")) detach(sacurine) #### Application to an opls object generated by an ExpressionSet sacSet <- sacurine[["eset"]] sacPlsda <- opls(sacSet, "gender") plot(sacPlsda, typeVc = "x-score") #### Application to a opls object generated by an MultiDataSet data(NCI60) nciMset <- NCI60[["mds"]] # Restricting to the 'agilent' and 'hgu95' datasets nciMset <- nciMset[, c("agilent", "hgu95")] # Restricting to the 'ME' and 'LE' cancer types sampleNamesVc <- Biobase::sampleNames(nciMset[["agilent"]]) cancerTypeVc <- Biobase::pData(nciMset[["agilent"]])[, "cancer"] nciMset <- nciMset[sampleNamesVc[cancerTypeVc %in% c("ME", "LE")], ] # Building PLS-DA models for the cancer type nciPlsda <- opls(nciMset, "cancer", predI = 2) plot(nciPlsda, typeVc = "x-score")
data(sacurine) attach(sacurine) for(typeC in c("correlation", "outlier", "overview", "permutation", "predict-train","predict-test", "summary", "x-loading", "x-score", "x-variance", "xy-score", "xy-weight")) { print(typeC) if(grepl("predict", typeC)) subset <- "odd" else subset <- NULL plsModel <- opls(dataMatrix, sampleMetadata[, "gender"], predI = ifelse(typeC != "xy-weight", 1, 2), orthoI = ifelse(typeC != "xy-weight", 1, 0), permI = ifelse(typeC == "permutation", 10, 0), subset = subset, info.txtC = "none", fig.pdfC = "none") plot(plsModel, typeVc = typeC) } sacPlsda <- opls(dataMatrix, sampleMetadata[, "gender"]) plot(sacPlsda, parPaletteVc = c("green4", "magenta")) detach(sacurine) #### Application to an opls object generated by an ExpressionSet sacSet <- sacurine[["eset"]] sacPlsda <- opls(sacSet, "gender") plot(sacPlsda, typeVc = "x-score") #### Application to a opls object generated by an MultiDataSet data(NCI60) nciMset <- NCI60[["mds"]] # Restricting to the 'agilent' and 'hgu95' datasets nciMset <- nciMset[, c("agilent", "hgu95")] # Restricting to the 'ME' and 'LE' cancer types sampleNamesVc <- Biobase::sampleNames(nciMset[["agilent"]]) cancerTypeVc <- Biobase::pData(nciMset[["agilent"]])[, "cancer"] nciMset <- nciMset[sampleNamesVc[cancerTypeVc %in% c("ME", "LE")], ] # Building PLS-DA models for the cancer type nciPlsda <- opls(nciMset, "cancer", predI = 2) plot(nciPlsda, typeVc = "x-score")
Returns predictions of the (O)PLS(-DA) model on a new dataset
## S4 method for signature 'opls' predict(object, newdata)
## S4 method for signature 'opls' predict(object, newdata)
object |
An S4 object of class |
newdata |
Either a data frame or a matrix, containing numeric columns only, with the same number of columns (variables) as the 'x' used for model training with 'opls'. |
Predictions (either a vector, factor, or matrix depending on the y response used for training the model)
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) predictorMN <- dataMatrix responseFc <- sampleMetadata[, "gender"] sacurine.plsda <- opls(predictorMN, responseFc, subset = "odd") trainVi <- getSubsetVi(sacurine.plsda) table(responseFc[trainVi], fitted(sacurine.plsda)) table(responseFc[-trainVi], predict(sacurine.plsda, predictorMN[-trainVi, ])) detach(sacurine)
data(sacurine) attach(sacurine) predictorMN <- dataMatrix responseFc <- sampleMetadata[, "gender"] sacurine.plsda <- opls(predictorMN, responseFc, subset = "odd") trainVi <- getSubsetVi(sacurine.plsda) table(responseFc[trainVi], fitted(sacurine.plsda)) table(responseFc[-trainVi], predict(sacurine.plsda, predictorMN[-trainVi, ])) detach(sacurine)
Displays information about the dataset and the model.
## S4 method for signature 'opls' print(x)
## S4 method for signature 'opls' print(x)
x |
An S4 object of class |
Invisible.
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) print(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) print(sacurine.plsda) detach(sacurine)
Returns the residuals from the (O)PLS(-DA) regression models
residuals(object, ...)
residuals(object, ...)
object |
An S4 object of class |
... |
Currently not used. |
Numeric matrix or vector (same dimensions as the modeled y response); if y is a character vector or a factor (in case of classification), the residuals equal 0 (predicted class identical to the true class) or 1 (prediction error)
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) sacurine.pls <- opls(dataMatrix, sampleMetadata[, "age"]) head(residuals(sacurine.pls)) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.pls <- opls(dataMatrix, sampleMetadata[, "age"]) head(residuals(sacurine.pls)) detach(sacurine)
Urine samples from 183 human adults were analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LTQ Orbitrap) in the negative ionization mode. A total of 109 metabolites were identified or annotated at the MSI level 1 or 2. After retention time alignment with XCMS, peaks were integrated with Quan Browser. After signal drift and batch effect correction of intensities, each urine profile was normalized to the osmolality of the sample. Finally, the data were log10 transformed.
A list with the following elements:
dataMatrix a 183 samples x 109 variables matrix of numeric type corresponding to the intensity profiles (values have been log10-transformed)
sampleMetadata a 183 x 3 data frame, with the volunteers' age ('age', numeric), body mass index ('bmi', numeric), and gender ('gender', factor)
variableMetadata a 109 x 3 data frame, with the metabolites' MSI identification level ('msiLevel': either 1 or 2), HMDB ID when available ('hmdb', character), chemical class according to the 'super class' taxonomy of HMDB ('chemicalClass', character)
se dataset in the SummarizedExperiment format
eset dataset in the ExpressionSet format
List containing the 'dataMatrix' matrix (numeric) of data (samples as rows, variables as columns), the 'sampleMetadata' data frame of sample metadata, and the variableMetadata data frame of variable metadata. Row names of 'dataMatrix' and 'sampleMetadata' are identical. Column names of 'dataMatrix' are identical to row names of 'variableMetadata'. For details see the 'Format' section above.
Thevenot E.A., Roux A., Xu Y., Ezan E. and Junot C. (2015). Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research, DOI: 10.1021/acs.jproteome.5b00354
Displays information about the dataset and the model.
## S4 method for signature 'opls' show(object)
## S4 method for signature 'opls' show(object)
object |
An S4 object of class |
Invisible.
Philippe Rinaudo and Etienne Thevenot (CEA)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) show(sacurine.plsda) detach(sacurine)
data(sacurine) attach(sacurine) sacurine.plsda <- opls(dataMatrix, sampleMetadata[, "gender"]) show(sacurine.plsda) detach(sacurine)
Returns predictions of the (O)PLS(-DA) model on the out of the box samples (when a 'subset' of samples has been selected when training the model)
tested(object) ## S4 method for signature 'opls' tested(object)
tested(object) ## S4 method for signature 'opls' tested(object)
object |
An S4 object of class |
Predictions (either a vector, factor, or matrix depending on the y response used for training the model)
Etienne Thevenot, [email protected]
data(sacurine) attach(sacurine) testedorMN <- dataMatrix responseFc <- sampleMetadata[, "gender"] sacurine.plsda <- opls(testedorMN, responseFc, subset = "odd") trainVi <- getSubsetVi(sacurine.plsda) table(responseFc[trainVi], fitted(sacurine.plsda)) detach(sacurine)
data(sacurine) attach(sacurine) testedorMN <- dataMatrix responseFc <- sampleMetadata[, "gender"] sacurine.plsda <- opls(testedorMN, responseFc, subset = "odd") trainVi <- getSubsetVi(sacurine.plsda) table(responseFc[trainVi], fitted(sacurine.plsda)) detach(sacurine)
The 3 .tsv files are written with the indicated file
prefix, and
'_dataMatrix.tsv', '_sampleMetadata.tsv', and '_variableMetadata.tsv'
suffices, respectively. Note that the dataMatrix
is transposed before
export (e.g., the samples are written column wise in the 'dataMatrix.tsv'
exported file).
toW4M(x, filePrefixC = paste0(getwd(), "/out_"), verboseL = TRUE) ## S4 method for signature 'ExpressionSet' toW4M(x, filePrefixC = paste0(getwd(), "/out_"), verboseL = TRUE)
toW4M(x, filePrefixC = paste0(getwd(), "/out_"), verboseL = TRUE) ## S4 method for signature 'ExpressionSet' toW4M(x, filePrefixC = paste0(getwd(), "/out_"), verboseL = TRUE)
x |
An S4 object of class |
filePrefixC |
Character: common prefix (including repository full path) of the three file names: for example, the 'c:/mydata/setname' value will result in writting the 'c:/mydata/setname_dataMatrix.tsv', 'c:/mydata/setname_sampleMetadata.tsv', and 'c:/mydata/setname_variableMetadata.tsv' files. |
verboseL |
Logical: should comments be printed? |
No object returned.
Etienne Thevenot, [email protected]
sacSet <- fromW4M(file.path(path.package("ropls"), "extdata")) toW4M(sacSet)
sacSet <- fromW4M(file.path(path.package("ropls"), "extdata")) toW4M(sacSet)
Numeric and graphical display of a matrix, a dataframe, an ExpressionSet or a SummarizedExperiment
Display of the class, mode, size and first...last values from the object; used inside the 'view' wrapper method
Wrapper of the stats::image function used inside the 'view' method
view( x, printL = TRUE, plotL = TRUE, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'SummarizedExperiment' view( x, printL = TRUE, plotL = TRUE, mainC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'ExpressionSet' view( x, printL = TRUE, plotL = TRUE, mainC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'data.frame' view( x, printL = TRUE, plotL = TRUE, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'matrix' view( x, printL = TRUE, plotL = TRUE, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) strF(tableMF, borderI = 2, bigMarkC = ",") imageF( x, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 1.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" )
view( x, printL = TRUE, plotL = TRUE, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'SummarizedExperiment' view( x, printL = TRUE, plotL = TRUE, mainC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'ExpressionSet' view( x, printL = TRUE, plotL = TRUE, mainC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'data.frame' view( x, printL = TRUE, plotL = TRUE, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) ## S4 method for signature 'matrix' view( x, printL = TRUE, plotL = TRUE, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 3.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" ) strF(tableMF, borderI = 2, bigMarkC = ",") imageF( x, mainC = "", subC = "", paletteC = c("heat", "revHeat", "grey", "revGrey", "palette", "ramp")[1], rowAllL = FALSE, rowCexN = 1, rowMarN = 5.1, rowLabC = "", rowTruncI = 0, colAllL = FALSE, colCexN = 1, colMarN = 1.1, colLabC = "", colTruncI = 0, drawScaleL = TRUE, delimitReplicatesL = FALSE, standardizeL = FALSE, fig.pdfC = "interactive" )
x |
object to be viewed |
printL |
should the numerical summary be printed? |
plotL |
should the graphical image be displayed? |
mainC |
character: plot main title |
subC |
character(1): plot subtitle |
paletteC |
character: color palette; either 'heat' [default], 'revHeat', 'grey', 'revGrey', 'palette', 'ramp' |
rowAllL |
logical: should all rownames be displayed or only the first and last ones? |
rowCexN |
numeric: size of row labels [default: 1] |
rowMarN |
numeric: row margin [default: 5.1] |
rowLabC |
character: label for the y (row) axis |
rowTruncI |
integer: number of character for truncation of rownames (default, 0, means no truncation) |
colAllL |
logical: should all column names be displayed or only the first and last ones? |
colCexN |
numeric: size of column labels [default: 1] |
colMarN |
numeric: column margin [default: 3.1] |
colLabC |
character: label for the x (column) axis |
colTruncI |
integer: number of character for truncation of colnames (default, 0, means no truncation) |
drawScaleL |
logical: should the color scale be drawn? [default: TRUE] |
delimitReplicatesL |
logical: should lines be added to the image to delimit replicates in row or column names? |
standardizeL |
Logical: should columns be standardized for display? (i.e. subtracting the mean and dividing by the standard deviation) [default: FALSE] |
fig.pdfC |
character: either 'interactive' [default] or the name of the pdf file to save the figure |
tableMF |
Input matrix, dataframe or vector |
borderI |
Number of border (first and last) rows and columns to display |
bigMarkC |
Big mark separator for summary results |
this method has no output
This function has no output.
library(ropls) # Get the sacurine dataset data(sacurine) # Display the data matrix view(sacurine[["dataMatrix"]]) view(sacurine[["dataMatrix"]][, 1:40], mainC = "'Sacurine' dataset", rowAllL = TRUE, colAllL = TRUE, colTruncI = 13, colMarN = 7) view(sacurine[["dataMatrix"]][, 1:40], mainC = "'Sacurine' dataset", paletteC = "ramp") # Display the sample metadata (dataframe) view(sacurine[["sampleMetadata"]]) # Display the SummarizedExperiment view(sacurine[["se"]]) # Display the ExpressionSet view(sacurine[["eset"]]) data(sacurine) strF(sacurine[['dataMatrix']]) strF(sacurine[['sampleMetadata']]) data(sacurine) imageF(sacurine[['dataMatrix']])
library(ropls) # Get the sacurine dataset data(sacurine) # Display the data matrix view(sacurine[["dataMatrix"]]) view(sacurine[["dataMatrix"]][, 1:40], mainC = "'Sacurine' dataset", rowAllL = TRUE, colAllL = TRUE, colTruncI = 13, colMarN = 7) view(sacurine[["dataMatrix"]][, 1:40], mainC = "'Sacurine' dataset", paletteC = "ramp") # Display the sample metadata (dataframe) view(sacurine[["sampleMetadata"]]) # Display the SummarizedExperiment view(sacurine[["se"]]) # Display the ExpressionSet view(sacurine[["eset"]]) data(sacurine) strF(sacurine[['dataMatrix']]) strF(sacurine[['sampleMetadata']]) data(sacurine) imageF(sacurine[['dataMatrix']])