| Title: | Signature discovery from omics data |
|---|---|
| Description: | Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. |
| Authors: | Philippe Rinaudo [aut],
Etienne A. Thevenot [aut, cre]
|
| Maintainer: | Etienne A. Thevenot <[email protected]> |
| License: | CeCILL |
| Version: | 1.33.0 |
| Built: | 2024-07-24 05:17:31 UTC |
| Source: | https://github.com/bioc/biosigner |
Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics.
Philippe Rinaudo <[email protected]> and Etienne A. Thevenot <[email protected]>.
Maintainer: Etienne A. Thevenot <[email protected]>
Main function of the 'biosigner' package. For each of the available classifiers (PLS-DA, Random Forest, and SVM), the significant features are selected and the corresponding models are built.
biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'matrix' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'data.frame' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'SummarizedExperiment' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiAssayExperiment' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'ExpressionSet' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiDataSet' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] )biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = NA, fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'matrix' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'data.frame' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'SummarizedExperiment' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiAssayExperiment' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'ExpressionSet' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'MultiDataSet' biosign( x, y, methodVc = c("all", "plsda", "randomforest", "svm")[1], bootI = 50, pvalN = 0.05, permI = 1, fixRankL = FALSE, seedI = 123, plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] )
x |
Numerical data frame or matrix (observations x variables), or SummarizedExperiment (or ExpressionSet) object ; NAs are allowed for PLS-DA but for SVM, samples with NA will be removed |
y |
Two-level factor corresponding to the class labels, or a character indicating the name of the column of the pData to be used, when x is an ExpressionSet object |
methodVc |
Character vector: Either one or all of the following classifiers: Partial Least Squares Discriminant Analysis ('plsda'), or Random Forest ('randomforest'), or Support Vector Machine ('svm') |
bootI |
Integer: Number of bootstaps for resampling |
pvalN |
Numeric: To speed up the selection, only variables which significantly improve the model up to two times this threshold (to take into account potential fluctuations) are computed |
permI |
Integer: Random permutation are used to assess the significance of each new variable included into the model (forward selection) |
fixRankL |
Logical: Should the initial ranking be computed with the full model only, or as the median of the ranks from the models built on the sampled dataset? |
seedI |
integer: optional seed to obtain exactly the same signature when rerunning biosigner; default is '123'; set to NULL to prevent seed setting |
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 'biosign' containing the following slots: 1) 'methodVc' character vector: selected classifier(s) ('plsda', 'randomforest', and/or 'svm'), 2) 'accuracyMN' numeric matrix: balanced accuracies for the full models, and the models restricted to the 'S' and 'AS' signatures (predictions are obtained by using the resampling scheme selected with the 'bootI' and 'crossvalI' arguments), 3) 'tierMC' character matrix: contains the tier ('S', 'A', 'B', 'C', 'D', or 'E') of each feature for each classifier (features with tier 'S' have been found significant in all backward selections; features with tier 'A' have been found significant in all but the last selection, and so on), 4) modelLs list: selected classifier(s) trained on the subset restricted to the 'S' features, 5) signatureLs list: 'S' signatures for each classifier; and 6) 'AS' list: 'AS' signatures and corresponding trained classifiers, in addition to the dataset restricted to tiers 'S' and 'A' ('xMN') and the labels ('yFc')
Philippe Rinaudo and Etienne Thevenot (CEA)
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] # signature selection for all 3 classifiers # a bootI = 5 number of bootstraps is used for this example # we recommend to keep the default bootI = 50 value for your analyzes diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## Application to a SummarizedExperiment diaplasma.se <- SummarizedExperiment::SummarizedExperiment(assays = list(diaplasma = t(dataMatrix)), colData = sampleMetadata, rowData = variableMetadata) # restricting to the first 100 features to speed up the example diaplasma.se <- diaplasma.se[1:100, ] diaplasma.se <- biosign(diaplasma.se, "type", bootI = 5) head(SummarizedExperiment::rowData(diaplasma.se)) # getting the biosign output diaplasma_type.biosign <- getBiosign(diaplasma.se)[["type_plsda.forest.svm"]] getAccuracyMN(diaplasma_type.biosign) ## Application to an ExpressionSet diaSet <- Biobase::ExpressionSet(assayData = t(dataMatrix), phenoData = new("AnnotatedDataFrame", data = sampleMetadata), featureData = new("AnnotatedDataFrame", data = variableMetadata), experimentData = new("MIAME", title = "diaplasma")) # restricting to the first 100 features to speed up the example diaSet <- diaSet[1:100, ] diaSign <- biosign(diaSet, "type", bootI = 5) diaSet <- getEset(diaSign) head(Biobase::fData(diaSet)) detach(diaplasma) ## Application to a MultiAssayExperiment data("NCI60", package = "ropls") nci.mae <- NCI60[["mae"]] library(MultiAssayExperiment) # Cancer types table(nci.mae$cancer) # Restricting to the 'ME' and 'LE' cancer types and to the 'agilent' and 'hgu95' datasets nci.mae <- nci.mae[, nci.mae$cancer %in% c("ME", "LE"), c("agilent", "hgu95")] # Selecting the significant features for PLS-DA, RF, and SVM classifiers nci.mae <- biosign(nci.mae, "cancer", bootI = 5) # Getting the tiers SummarizedExperiment::rowData(nci.mae[["agilent"]]) # Getting the models mae_biosign.ls <- getBiosign(nci.mae) # Name of the models stored in the (metadata of) each SummarizedExperiment object names(mae_biosign.ls[["agilent"]]) # Visualizing the individual results for (set.c in names(mae_biosign.ls)) plot(mae_biosign.ls[[set.c]][["cancer_plsda.forest.svm"]], typeC = "tier", plotSubC = set.c) ## Application to a MultiDataSet data("NCI60", package = "ropls") 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 library(Biobase) sample_names.vc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancer_type.vc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sample_names.vc[cancer_type.vc %in% c("ME", "LE")], ] # Selecting the significant features for PLS-DA, RF, and SVM classifiers nci_cancer.biosign <- biosign(nci.mds, "cancer", bootI = 5) # Getting back the updated MultiDataSet nci.mds <- getMset(nci_cancer.biosign)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] # signature selection for all 3 classifiers # a bootI = 5 number of bootstraps is used for this example # we recommend to keep the default bootI = 50 value for your analyzes diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## Application to a SummarizedExperiment diaplasma.se <- SummarizedExperiment::SummarizedExperiment(assays = list(diaplasma = t(dataMatrix)), colData = sampleMetadata, rowData = variableMetadata) # restricting to the first 100 features to speed up the example diaplasma.se <- diaplasma.se[1:100, ] diaplasma.se <- biosign(diaplasma.se, "type", bootI = 5) head(SummarizedExperiment::rowData(diaplasma.se)) # getting the biosign output diaplasma_type.biosign <- getBiosign(diaplasma.se)[["type_plsda.forest.svm"]] getAccuracyMN(diaplasma_type.biosign) ## Application to an ExpressionSet diaSet <- Biobase::ExpressionSet(assayData = t(dataMatrix), phenoData = new("AnnotatedDataFrame", data = sampleMetadata), featureData = new("AnnotatedDataFrame", data = variableMetadata), experimentData = new("MIAME", title = "diaplasma")) # restricting to the first 100 features to speed up the example diaSet <- diaSet[1:100, ] diaSign <- biosign(diaSet, "type", bootI = 5) diaSet <- getEset(diaSign) head(Biobase::fData(diaSet)) detach(diaplasma) ## Application to a MultiAssayExperiment data("NCI60", package = "ropls") nci.mae <- NCI60[["mae"]] library(MultiAssayExperiment) # Cancer types table(nci.mae$cancer) # Restricting to the 'ME' and 'LE' cancer types and to the 'agilent' and 'hgu95' datasets nci.mae <- nci.mae[, nci.mae$cancer %in% c("ME", "LE"), c("agilent", "hgu95")] # Selecting the significant features for PLS-DA, RF, and SVM classifiers nci.mae <- biosign(nci.mae, "cancer", bootI = 5) # Getting the tiers SummarizedExperiment::rowData(nci.mae[["agilent"]]) # Getting the models mae_biosign.ls <- getBiosign(nci.mae) # Name of the models stored in the (metadata of) each SummarizedExperiment object names(mae_biosign.ls[["agilent"]]) # Visualizing the individual results for (set.c in names(mae_biosign.ls)) plot(mae_biosign.ls[[set.c]][["cancer_plsda.forest.svm"]], typeC = "tier", plotSubC = set.c) ## Application to a MultiDataSet data("NCI60", package = "ropls") 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 library(Biobase) sample_names.vc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancer_type.vc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sample_names.vc[cancer_type.vc %in% c("ME", "LE")], ] # Selecting the significant features for PLS-DA, RF, and SVM classifiers nci_cancer.biosign <- biosign(nci.mds, "cancer", bootI = 5) # Getting back the updated MultiDataSet nci.mds <- getMset(nci_cancer.biosign)
The biosigner object class
methodVccharacter vector: selected classifier(s) ('plsda', 'randomforest', or 'svm')
accuracyMNnumeric matrix: balanced accuracies for the full models, and the models restricted to the 'S' and 'AS' signatures
tierMCcharacter matrix: contains the tier ('S', 'A', 'B', 'C', 'D', or 'E') of each feature for each classifier
yFcfactor with two levels: response factor
modelLslist: selected classifier(s) trained on the subset restricted to the 'S' features
signatureLslist: 'S' signatures for each classifier
xSubMNmatrix: dataset restricted to the 'S' tier
ASlist: 'AS' signatures and corresponding trained classifiers, in addition to the dataset restricted to tiers 'S' and 'A' ('xMN') and the labels ('yFc')
esetExpressionSet: when 'biosign' has been applied to an ExpressionSet, the instance with additional columns in fData containing the selected features is stored here
Objects can be created by calls of the form
new("biosign", ...) or by calling the biosign function
Philippe Rinaudo and Etienne Thevenot (CEA)
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) detach(diaplasma)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) detach(diaplasma)
An S4 class to store the the biosign objects generated by the application of the 'biosign' method to a MultiDataSet instance
biosignLsList: List of instances from the 'biosign' class corresponding to the models built on each ExpresssionSet
Objects can be created by calls of the form
new("biosignMultiDataSet", ...) or by applying the biosign function to
a MultiDataSet instance
# In progress# In progress
Plasma samples from 69 diabetic patients were analyzed by reversed-phase liquid chromatography coupled to high-resolution mass spectrometry (Orbitrap Exactive) in the negative ionization mode. The raw data were pre-processed with XCMS and CAMERA (5,501 features), corrected for signal drift, log10 transformed, and annotated with an in-house spectral database. The patient's age, body mass index, and diabetic type are recorded. These three clinical covariates are strongly associated, most of the type II patients being older and with a higher bmi than the type I individuals.
A list with the following elements:
dataMatrix: a 69 samples x 5,501 features matrix of numeric type corresponding to the intensity profiles (values have been log10-transformed),
sampleMetadata: a 69 x 3 data frame, with the patients' diabetic type ('type', factor), age ('age', numeric), and body mass index ('bmi', numeric),
variableMetadata: a 5,501 x 8 data frame, with the median m/z ('mzmed', numeric) and the median retention time in seconds ('rtmed', numeric) from XCMS, the 'isotopes' (character), 'adduct' (character) and 'pcgroups' (numeric) annotations from CAMERA, and the names of the m/z and RT matching compounds from an in-house database of pure spectra from commercial metabolites ('spiDb', character).
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.
'diaplasma' dataset.
Rinaudo P., Boudah S., Junot C. and Thevenot E.A. (2016). biosigner: a new method for the discovery of significant molecular signatures from omics data. Frontiers in Molecular Biosciences 3. doi:10.3389/fmolb.2016.00026
Balanced accuracies for the full models, and the models restricted to the 'S' and 'AS' signatures
getAccuracyMN(object) ## S4 method for signature 'biosign' getAccuracyMN(object)getAccuracyMN(object) ## S4 method for signature 'biosign' getAccuracyMN(object)
object |
An S4 object of class |
A numeric matrix containing the balanced accuracies for the full models, and the models restricted to the 'S' and 'AS' signatures (predictions are obtained by using the resampling scheme selected with the 'bootI' and 'crossvalI' arguments)
Philippe Rinaudo and Etienne Thevenot (CEA)
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## individual boxplot of the selected signatures getAccuracyMN(diaSign) detach(diaplasma)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## individual boxplot of the selected signatures getAccuracyMN(diaSign) detach(diaplasma)
The models are extracted as a list
getBiosign(object) ## S4 method for signature 'SummarizedExperiment' getBiosign(object) ## S4 method for signature 'MultiAssayExperiment' getBiosign(object)getBiosign(object) ## S4 method for signature 'SummarizedExperiment' getBiosign(object) ## S4 method for signature 'MultiAssayExperiment' getBiosign(object)
object |
An S4 object of class |
List of biosigner outputs contained in the SummarizedExperiment object
Etienne Thevenot, [email protected]
# Getting the diaplasma data set as a SummarizedExperiment data(diaplasma) diaplasma.se <- SummarizedExperiment::SummarizedExperiment(assays = list(diaplasma = t(diaplasma[["dataMatrix"]])), colData = diaplasma[["sampleMetadata"]], rowData = diaplasma[["variableMetadata"]]) diaplasma.se <- diaplasma.se[1:100, ] # Selecting the features diaplasma.se <- biosign(diaplasma.se, "type", bootI = 5, fig.pdfC = "none") # Getting the signatures diaplasma.biosign <- getBiosign(diaplasma.se)[["type_plsda.forest.svm"]] diaplasma.biosign# Getting the diaplasma data set as a SummarizedExperiment data(diaplasma) diaplasma.se <- SummarizedExperiment::SummarizedExperiment(assays = list(diaplasma = t(diaplasma[["dataMatrix"]])), colData = diaplasma[["sampleMetadata"]], rowData = diaplasma[["variableMetadata"]]) diaplasma.se <- diaplasma.se[1:100, ] # Selecting the features diaplasma.se <- biosign(diaplasma.se, "type", bootI = 5, fig.pdfC = "none") # Getting the signatures diaplasma.biosign <- getBiosign(diaplasma.se)[["type_plsda.forest.svm"]] diaplasma.biosign
Extracts the complemented ExpressionSet when biosign has been applied to an ExpressionSet
## S4 method for signature 'biosign' getEset(object)## S4 method for signature 'biosign' 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 computed tiers for each feature and each classifier.
Etienne Thevenot, [email protected]
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## building the ExpresssionSet instance diaSet <- Biobase::ExpressionSet(assayData = t(dataMatrix), phenoData = new("AnnotatedDataFrame", data = sampleMetadata), featureData = new("AnnotatedDataFrame", data = variableMetadata), experimentData = new("MIAME", title = "diaplasma")) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 diaSet <- diaSet[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(diaSet, "type", bootI = 5) diaSet <- biosigner::getEset(diaSign) head(Biobase::pData(diaSet)) head(Biobase::fData(diaSet)) detach(diaplasma)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## building the ExpresssionSet instance diaSet <- Biobase::ExpressionSet(assayData = t(dataMatrix), phenoData = new("AnnotatedDataFrame", data = sampleMetadata), featureData = new("AnnotatedDataFrame", data = variableMetadata), experimentData = new("MIAME", title = "diaplasma")) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 diaSet <- diaSet[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(diaSet, "type", bootI = 5) diaSet <- biosigner::getEset(diaSign) head(Biobase::pData(diaSet)) head(Biobase::fData(diaSet)) detach(diaplasma)
Extracts the complemented MultiDataSet when biosign has been applied to a MultiDataSet
## S4 method for signature 'biosignMultiDataSet' getMset(object)## S4 method for signature 'biosignMultiDataSet' getMset(object)
object |
An S4 object of class |
An S4 object of class MultiDataSet.
# Loading the 'NCI60_4arrays' from the 'omicade4' package data("NCI60_4arrays", package = "omicade4") # Selecting two of the four datasets setNamesVc <- c("agilent", "hgu95") # Creating the MultiDataSet instance nciMset <- MultiDataSet::createMultiDataSet() # Adding the two datasets as ExpressionSet instances for (setC in setNamesVc) { # Getting the data exprMN <- as.matrix(NCI60_4arrays[[setC]]) pdataDF <- data.frame(row.names = colnames(exprMN), cancer = substr(colnames(exprMN), 1, 2), stringsAsFactors = FALSE) fdataDF <- data.frame(row.names = rownames(exprMN), name = rownames(exprMN), stringsAsFactors = FALSE) # Building the ExpressionSet eset <- Biobase::ExpressionSet(assayData = exprMN, phenoData = new("AnnotatedDataFrame", data = pdataDF), featureData = new("AnnotatedDataFrame", data = fdataDF), experimentData = new("MIAME", title = setC)) # Adding to the MultiDataSet nciMset <- MultiDataSet::add_eset(nciMset, eset, dataset.type = setC, GRanges = NA, warnings = FALSE) } # 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")], ] # Summary of the MultiDataSet nciMset # Selecting the significant features for PLS-DA, RF, and SVM classifiers, and getting back the updated MultiDataSet nciBiosign <- biosign(nciMset, "cancer") nciMset <- getMset(nciBiosign) # In the updated MultiDataSet, the updated featureData now contains the cancer_biosign_'classifier' columns # indicating the selected features lapply(Biobase::fData(nciMset), head)# Loading the 'NCI60_4arrays' from the 'omicade4' package data("NCI60_4arrays", package = "omicade4") # Selecting two of the four datasets setNamesVc <- c("agilent", "hgu95") # Creating the MultiDataSet instance nciMset <- MultiDataSet::createMultiDataSet() # Adding the two datasets as ExpressionSet instances for (setC in setNamesVc) { # Getting the data exprMN <- as.matrix(NCI60_4arrays[[setC]]) pdataDF <- data.frame(row.names = colnames(exprMN), cancer = substr(colnames(exprMN), 1, 2), stringsAsFactors = FALSE) fdataDF <- data.frame(row.names = rownames(exprMN), name = rownames(exprMN), stringsAsFactors = FALSE) # Building the ExpressionSet eset <- Biobase::ExpressionSet(assayData = exprMN, phenoData = new("AnnotatedDataFrame", data = pdataDF), featureData = new("AnnotatedDataFrame", data = fdataDF), experimentData = new("MIAME", title = setC)) # Adding to the MultiDataSet nciMset <- MultiDataSet::add_eset(nciMset, eset, dataset.type = setC, GRanges = NA, warnings = FALSE) } # 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")], ] # Summary of the MultiDataSet nciMset # Selecting the significant features for PLS-DA, RF, and SVM classifiers, and getting back the updated MultiDataSet nciBiosign <- biosign(nciMset, "cancer") nciMset <- getMset(nciBiosign) # In the updated MultiDataSet, the updated featureData now contains the cancer_biosign_'classifier' columns # indicating the selected features lapply(Biobase::fData(nciMset), head)
List of 'S' (or 'S' and 'A') signatures for each classifier
getSignatureLs(object, tierC = c("S", "AS")[1]) ## S4 method for signature 'biosign' getSignatureLs(object, tierC = c("S", "AS")[1])getSignatureLs(object, tierC = c("S", "AS")[1]) ## S4 method for signature 'biosign' getSignatureLs(object, tierC = c("S", "AS")[1])
object |
An S4 object of class |
tierC |
Character: defines whether signatures from the 'S' tier only (default) or the ('S' and 'A') tiers should be returned |
List of 'S' (or 'S' and 'A') signatures for each classifier
Philippe Rinaudo and Etienne Thevenot (CEA)
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## individual boxplot of the selected signatures getSignatureLs(diaSign) detach(diaplasma)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## individual boxplot of the selected signatures getSignatureLs(diaSign) detach(diaplasma)
Displays classifier tiers or individual boxplots from selected features
This function plots signatures obtained by biosign.
## S4 method for signature 'biosign,ANY' plot( x, y, tierMaxC = "S", typeC = c("tier", "boxplot")[1], plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'biosignMultiDataSet,ANY' plot( x, y, tierMaxC = "S", typeC = c("tier", "boxplot")[1], plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] )## S4 method for signature 'biosign,ANY' plot( x, y, tierMaxC = "S", typeC = c("tier", "boxplot")[1], plotSubC = "", fig.pdfC = c("none", "interactive", "myfile.pdf")[2], info.txtC = c("none", "interactive", "myfile.txt")[2] ) ## S4 method for signature 'biosignMultiDataSet,ANY' plot( x, y, tierMaxC = "S", typeC = c("tier", "boxplot")[1], 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. |
tierMaxC |
Character: Maximum level of tiers to display: Either 'S' and 'A', (for boxplot), or also 'B', 'C', 'D', and 'E' (for tiers) by decreasing number of selections |
typeC |
Character: Plot type; either 'tier' [default] displaying the comparison of signatures up to the selected 'tierMaxC' or 'boxplot' showing the individual boxplots of the features selected by all the classifiers |
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 |
A plot is created on the current graphics device.
Philippe Rinaudo and Etienne Thevenot (CEA)
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## individual boxplot of the selected signatures plot(diaSign, typeC = "boxplot") detach(diaplasma) data("NCI60", package = "ropls") 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 library(Biobase) sample_names.vc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancer_type.vc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sample_names.vc[cancer_type.vc %in% c("ME", "LE")], ] # Selecting the significant features for PLS-DA, RF, and SVM classifiers nci_cancer.biosign <- biosign(nci.mds, "cancer", bootI = 5) # Plotting the selected signatures plot(nci_cancer.biosign)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## individual boxplot of the selected signatures plot(diaSign, typeC = "boxplot") detach(diaplasma) data("NCI60", package = "ropls") 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 library(Biobase) sample_names.vc <- Biobase::sampleNames(nci.mds[["agilent"]]) cancer_type.vc <- Biobase::pData(nci.mds[["agilent"]])[, "cancer"] nci.mds <- nci.mds[sample_names.vc[cancer_type.vc %in% c("ME", "LE")], ] # Selecting the significant features for PLS-DA, RF, and SVM classifiers nci_cancer.biosign <- biosign(nci.mds, "cancer", bootI = 5) # Plotting the selected signatures plot(nci_cancer.biosign)
This function predicts values based upon biosign classifiers trained
on the 'S' signature
## S4 method for signature 'biosign' predict(object, newdata, tierMaxC = "S")## S4 method for signature 'biosign' predict(object, newdata, tierMaxC = "S")
object |
An S4 object of class |
newdata |
Either a data frame or a matrix, containing numeric columns only, with column names identical to the 'x' used for model training with 'biosign'. |
tierMaxC |
Character: Maximum level of tiers to display: Either 'S'or 'A'. |
Data frame with the predictions for each classifier as factor columns.
Philippe Rinaudo and Etienne Thevenot (CEA)
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## training the classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## fitted values (for the subsets restricted to the 'S' signatures) sFitDF <- predict(diaSign) ## confusion tables print(lapply(sFitDF, function(predFc) table(actual = sampleMetadata[, "type"], predicted = predFc))) ## balanced accuracies sapply(sFitDF, function(predFc) { conf <- table(sampleMetadata[, "type"], predFc) conf <- sweep(conf, 1, rowSums(conf), "/") mean(diag(conf)) }) ## note that these values are slightly different from the accuracies ## returned by biosign because the latter are computed by using the ## resampling scheme selected by the bootI or crossvalI arguments getAccuracyMN(diaSign)["S", ] detach(diaplasma)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## training the classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) ## fitted values (for the subsets restricted to the 'S' signatures) sFitDF <- predict(diaSign) ## confusion tables print(lapply(sFitDF, function(predFc) table(actual = sampleMetadata[, "type"], predicted = predFc))) ## balanced accuracies sapply(sFitDF, function(predFc) { conf <- table(sampleMetadata[, "type"], predFc) conf <- sweep(conf, 1, rowSums(conf), "/") mean(diag(conf)) }) ## note that these values are slightly different from the accuracies ## returned by biosign because the latter are computed by using the ## resampling scheme selected by the bootI or crossvalI arguments getAccuracyMN(diaSign)["S", ] detach(diaplasma)
Prints the selected features and the accuracies of the classifiers.
## S4 method for signature 'biosign' show(object)## S4 method for signature 'biosign' show(object)
object |
An S4 object of class |
Invisible.
Philippe Rinaudo and Etienne Thevenot (CEA)
## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) diaSign detach(diaplasma)## loading the diaplasma dataset data(diaplasma) attach(diaplasma) ## restricting to a smaller dataset for this example featureSelVl <- variableMetadata[, "mzmed"] >= 490 & variableMetadata[, "mzmed"] < 500 dataMatrix <- dataMatrix[, featureSelVl] variableMetadata <- variableMetadata[featureSelVl, ] ## signature selection for all 3 classifiers ## a bootI = 5 number of bootstraps is used for this example ## we recommend to keep the default bootI = 50 value for your analyzes set.seed(123) diaSign <- biosign(dataMatrix, sampleMetadata[, "type"], bootI = 5) diaSign detach(diaplasma)