| Title: | Classify diseases and build associated gene networks using gene expression profiles |
|---|---|
| Description: | Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. |
| Authors: | Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. |
| Maintainer: | Sara Aibar <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.47.0 |
| Built: | 2024-10-30 07:58:08 UTC |
| Source: | https://github.com/bioc/geNetClassifier |
Comprehensive package to automatically train a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier.
| Package: | geNetClassifier |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2013-02-28 |
| License: | GPL (>=2) |
| LazyLoad: | yes |
| Depends: | R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods |
| Imports: | e1071, ipred, graphics |
| Suggests: | leukemiasEset |
| Enhances: | RColorBrewer, igraph |
Sara Aibar, Celia Fontanillo and Javier De Las Rivas
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, CSIC/USAL). Salamanca. Spain.
Maintainer: Sara Aibar <[email protected]>
Main functions included in this package:
Query stats funtions:
Plots and genes info:
Classes:
Related data sets:
Calculates the genes ranking and/or plots the posterior probability of the genes ordered by class ranking.
calculateGenesRanking(eset=NULL, sampleLabels=NULL, numGenesPlot=1000, plotTitle="Significant genes", plotLp=TRUE, lpThreshold = 0.95, numSignificantGenesType="ranked", returnRanking="full", nullHiphothesisFilter=0.95, nGenesExprDiff=1000, geneLabels=NULL, precalcGenesRanking=NULL, IQRfilterPercentage= 0, verbose=TRUE)calculateGenesRanking(eset=NULL, sampleLabels=NULL, numGenesPlot=1000, plotTitle="Significant genes", plotLp=TRUE, lpThreshold = 0.95, numSignificantGenesType="ranked", returnRanking="full", nullHiphothesisFilter=0.95, nGenesExprDiff=1000, geneLabels=NULL, precalcGenesRanking=NULL, IQRfilterPercentage= 0, verbose=TRUE)
eset |
ExpressionSet or Matrix. Gene expression of the train samples (positive & non-logaritmic normalized values). |
sampleLabels |
Character. PhenoData variable (column name) containing the train samples class labels. |
numGenesPlot |
Integer. Number of genes to plot. |
plotTitle |
Character. Plot title. |
plotLp |
Logical. If FALSE no plot is drawn. |
lpThreshold |
Numeric between 0 and 1. Required posterior probability value to consider a gene 'significant'. |
numSignificantGenesType |
Character. Type of count for number of genes over lpThreshold.
|
returnRanking |
Character. Type of ranking to return:
|
nullHiphothesisFilter |
Numeric between 0 and 1. Genes with a Null Hipothesis with a posterior probability over this threshold will be removed from the ranking. |
nGenesExprDiff |
Numeric. Number of top genes to calculate the differencial expression for. |
geneLabels |
Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots. |
IQRfilterPercentage |
Integer. InterQuartile Range (IQR) filter applied to the initial data. Not recommended for more than two classes. |
precalcGenesRanking |
Allows providing a |
verbose |
Logical. If TRUE, messages indicating the execution progress will be printed on screen. |
Significant genes: Genes with posterior probability over 'lpThreshold'.
More significant genes may mean:
Very different class
More systemic disease
Plot lines represet the posterior probability of genes, sorted by rank from left to right.
In order to find genes that diferentiate the classes from each other, the function ranks the genes bassed on their posterior probability for each class.
The posterior probability represents how well a gene differentiates samples from a class, from samples from other classes. Therefore, Genes with high posterior probability are good to differentiate a class from all the others.
This posterior probability is calculated by emfit (pkg:EBarrays), an expectation-maximization (EM) algorithm for gene expression mixture model.
GenesRanking Optional. Requested genes ranking.
Plot Optional. Plot of the posterior probability of the top genes.
plot.GenesRanking is a shortcut to plotting a previusly calculated genes ranking.
i.e. plot(genesRanking)
# Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) ## Not run: ###### # Calculate/plot the significant genes (+ info) of a dataset # without training classifier/calculating network ###### # Return only significant genes ranking (default) signGenesRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType") numGenes(signGenesRanking) # Return the full genes ranking: fullRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", returnRanking="full") numGenes(fullRanking) numSignificantGenes(fullRanking) # The significant genes can then be extracted from it: signGenesRanking2 <- getTopRanking(fullRanking, numGenesClass=numSignificantGenes(fullRanking)) numGenes(signGenesRanking2) # Changing the posterior probability required to consider genes significant: signGenesRanking90 <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", lpThreshold=0.9) numGenes(signGenesRanking90) ## End(Not run) ###### # Ploting previously calculated rankings: ###### # Load or calculate a ranking (or a classifier with geNetClassifier) data(leukemiasClassifier) # Sample trained classifier, @genesRanking # Default plot: # - equivalent to plot(leukemiasClassifier@genesRanking) # - in this case, the previously calculated 'fullRanking' # is equivalent to 'leukemiasClassifier@genesRanking' calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking) # Changing arguments: calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking, numGenesPlot=5000, plotTitle="Leukemias", lpThreshold=0.9)# Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) ## Not run: ###### # Calculate/plot the significant genes (+ info) of a dataset # without training classifier/calculating network ###### # Return only significant genes ranking (default) signGenesRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType") numGenes(signGenesRanking) # Return the full genes ranking: fullRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", returnRanking="full") numGenes(fullRanking) numSignificantGenes(fullRanking) # The significant genes can then be extracted from it: signGenesRanking2 <- getTopRanking(fullRanking, numGenesClass=numSignificantGenes(fullRanking)) numGenes(signGenesRanking2) # Changing the posterior probability required to consider genes significant: signGenesRanking90 <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", lpThreshold=0.9) numGenes(signGenesRanking90) ## End(Not run) ###### # Ploting previously calculated rankings: ###### # Load or calculate a ranking (or a classifier with geNetClassifier) data(leukemiasClassifier) # Sample trained classifier, @genesRanking # Default plot: # - equivalent to plot(leukemiasClassifier@genesRanking) # - in this case, the previously calculated 'fullRanking' # is equivalent to 'leukemiasClassifier@genesRanking' calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking) # Changing arguments: calculateGenesRanking(precalcGenesRanking=leukemiasClassifier@genesRanking, numGenesPlot=5000, plotTitle="Leukemias", lpThreshold=0.9)
Generates the probability matrix.
externalValidation.probMatrix(queryResult, realLabels, numDecimals=2)externalValidation.probMatrix(queryResult, realLabels, numDecimals=2)
queryResult |
Object returned by |
realLabels |
Factor. Actual/real class of the samples. |
numDecimals |
Integer. Number of decimals to return. |
A probability matrix contains the probabilities of assigning each assigned sample to each class. They help identifying where errors are likelly to occur even though there were not actual errors in the external/cross validation.
The probability matrix.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training: geNetClassifier
Query the classifier: queryGeNetClassifier
Query summary: querySummary
External validation stats: externalValidation.stats
########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## External Validation ########################## # Select the samples to query the classifier # - External validation: samples not used for training testSamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples]) # Obtain the probability matrix for the assigned samples: externalValidation.probMatrix(queryResult, leukemiasEset[,testSamples]$LeukemiaType)########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## External Validation ########################## # Select the samples to query the classifier # - External validation: samples not used for training testSamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples]) # Obtain the probability matrix for the assigned samples: externalValidation.probMatrix(queryResult, leukemiasEset[,testSamples]$LeukemiaType)
Taking as input the confussion matrix resulting from external validation calculates the global Accuracy, Call Rate, Sensitivity, Specificity and Matthews Correlation Coefficient.
externalValidation.stats(confussionMatrix, numDecimals = 2)externalValidation.stats(confussionMatrix, numDecimals = 2)
confussionMatrix |
Confussion matrix containing the real class as rows and the assigned class as columns. |
numDecimals |
Integer. Number of decimals to show on the statistics. |
List:
global General classifier stats.
Accuracy: Percentage of correctly assigned samples within all assigned samples.
CallRate: Percentage of samples wich were assigned to a class.
byClass Stats by class.
Sensitivity: Percentage of samples of each class which were correctly identified (Rate of true positives)
Specificity: Percentage of samples assigned to a given class that really belonged to the class (Rate of true negatives)
MCC (Matthews Correlation Coefficient): Measure wich takes into account both, true and false positives and negatives. (100%: Perfect assignments)
confMatrix Confussion matrix.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training: geNetClassifier
Querying the classifier: queryGeNetClassifier
Generating the probability matrix: externalValidation.probMatrix
########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## External Validation: ########################## # Select the samples to query the classifier # - External validation: samples not used for training testSamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples]) # Create the confusion matrix confMatrix <- table(leukemiasEset[,testSamples]$LeukemiaType,queryResult$class) # Calculate its accuracy, call rate, sensitivity and specificity: externalValidation.stats(confMatrix)########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## External Validation: ########################## # Select the samples to query the classifier # - External validation: samples not used for training testSamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples]) # Create the confusion matrix confMatrix <- table(leukemiasEset[,testSamples]$LeukemiaType,queryResult$class) # Calculate its accuracy, call rate, sensitivity and specificity: externalValidation.stats(confMatrix)
Returns the names of the classes in a GenesRanking
signature(object = "GenesRanking")Main package function and classifier training:
geNetClassifier
This method's class (GenesRanking) help page.
data(leukemiasClassifier) gClasses(leukemiasClassifier@genesRanking)data(leukemiasClassifier) gClasses(leukemiasClassifier@genesRanking)
Contains the estimation of the Generalization Error and the gene stats for geNetClassifier executed with the given data and parameters. \
Calculated by 5-fold cross-validation.
accuracy:"Matrix". Accuracy and call rate.
sensitivitySpecificity:"Matrix". Sensitivity, Specificity, Matthews Correlation Coefficient and Call Rate for each
of the classes.
confMatrix:"Matrix". Confussion matrix.
probMatrix:"Matrix". Probabilities of belonging to each class for the assigned samples. Helps identifying
where errors are likely to occur even though there were not actual errors in the cross-validation.
querySummary:"List". Stats regarding the probability and number of assigned test samples to each class.
classificationGenes.stats:"List". Some basic statistics regarding the chosen genes.
classificationGenes.num:"Matrix". Number of genes used for each of the 5 cross-validaton classifiers.
signature(object = "GeneralizationError"): Shows an overview of all the slots in the object.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training:
geNetClassifier
###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # Note: Required 'estimateGError=TRUE' # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier", # estimateGError=TRUE) data(leukemiasClassifier) # Sample trained classifier # Global view of the returned object and its structure: leukemiasClassifier names(leukemiasClassifier) ######### # Exploring the cross validation stats # Note: Required 'estimateGError=TRUE' in geNetClassifier() ######### # Generalization Error estimated by cross-validation: leukemiasClassifier@generalizationError overview(leukemiasClassifier@generalizationError) # i.e. probabilityMatrix: leukemiasClassifier@generalizationError@probMatrix # i.e. statistics of the genes chosen in any of the CV loops for for AML: leukemiasClassifier@[email protected]$AML###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # Note: Required 'estimateGError=TRUE' # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier", # estimateGError=TRUE) data(leukemiasClassifier) # Sample trained classifier # Global view of the returned object and its structure: leukemiasClassifier names(leukemiasClassifier) ######### # Exploring the cross validation stats # Note: Required 'estimateGError=TRUE' in geNetClassifier() ######### # Generalization Error estimated by cross-validation: leukemiasClassifier@generalizationError overview(leukemiasClassifier@generalizationError) # i.e. probabilityMatrix: leukemiasClassifier@generalizationError@probMatrix # i.e. statistics of the genes chosen in any of the CV loops for for AML: leukemiasClassifier@generalizationError@classificationGenes.stats$AML
Information of the genes in the ranking (table format).
object |
a GenesRanking |
nGenes |
integer. Number of genes to show per class |
numDecimals |
integer. Number of decimals to show in the numeric values |
classes |
character. Classes of the genes to show |
genes |
character. Genes to show |
A list containing a dataframe with the details of the genes of each class. For each gene, the following information is provided:
ranking |
Ranking of the gene. |
gERankMean |
Mean rank the gene obtained in the cross-validation loops. Only available if geNetClassifier() was called with option |
class |
Class the gene was chosen for (the class the gene differentiates from the other classes). |
postProb |
Posterior probability which the gene was assigned by the expectation-maximization algorithm (emfit). Tied values are ranked based on the higher absolute value of exprsMeanDiff. Values are rounded. Several genes may look tied at posterior probability '1' but may actually be i.e. 0.999998 and 0.999997. |
exprsMeanDiff |
Difference betwen the mean expression of the gene within its class and its mean expression in the other classes. |
exprsUpDw |
Gene repressed (DOWN) or over-expressed(UP) for the current class (compared to the other classes). |
discriminantPower |
Measure calculated based on the coordinates of the support vectors. Represents the weight that the classifier gives to each gene to separate the classes. |
discrPwClass |
Class for which the Discriminant Power was calculated for. |
isRedundant |
Does the gene have a high correlation or mutual information with other genes in the list? The threshold to consider a gene redundant can be set through the arguments (by default: correlationsThreshold=0.8 and interactionsThreshold=0.5). |
genesDetails(object, nGenes=NULL, numDecimals=4, classes=NULL, genes=NULL)
Main package function and classifier training:
geNetClassifier
This method's class (GenesRanking) help page.
data(leukemiasClassifier) # Sample geNetClassifier() return options(width=200) # Optional, use in case the table rows are wrapped genesDetails(leukemiasClassifier@classificationGenes)$CML genesDetails(leukemiasClassifier@genesRanking, nGenes=5, numDecimals=2, classes="AML") genesDetails(leukemiasClassifier@genesRanking, genes=c("ENSG00000096006", "ENSG00000168081","ENSG00000105699"))$CLLdata(leukemiasClassifier) # Sample geNetClassifier() return options(width=200) # Optional, use in case the table rows are wrapped genesDetails(leukemiasClassifier@classificationGenes)$CML genesDetails(leukemiasClassifier@genesRanking, nGenes=5, numDecimals=2, classes="AML") genesDetails(leukemiasClassifier@genesRanking, genes=c("ENSG00000096006", "ENSG00000168081","ENSG00000105699"))$CLL
Contains the network returned by geNetClassifier. (Slot: @genesNetwork)
signature(object = "GenesNetwork"): Returns the network nodes (genes).
signature(object = "GenesNetwork"): Returns the network edges (relationships).
signature(object = "GenesNetwork"): Returns the number of nodes (genes) in the network.
signature(object = "GenesNetwork"): Returns the number of edges (relationships) in the network,
signature(network = "GenesNetwork"): Returns a new network containing only the given genes.
signature(network = "GenesNetwork"): Exports the network as text file.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training: geNetClassifier
Plot network or export as iGraph: plotNetwork
###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ###### # Explore the returned object ###### # Global view of the object and its structure: names(leukemiasClassifier) # List of Networks by classes: leukemiasClassifier@genesNetwork # Access to the nodes or edges of each network: getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,] getNodes(leukemiasClassifier@genesNetwork$AML)[1:50] ###### # Plotting ###### # Example: Plotting the sub-network of a class classificationGenes # Get the sub-network containing only the classification genes: subNet <- getSubNetwork(leukemiasClassifier@genesNetwork, leukemiasClassifier@classificationGenes) # Get the classification genes' info/details: clGenesInfo <- genesDetails(leukemiasClassifier@classificationGenes) # Plot the network of the class "ALL" plotNetwork(subNet$ALL, genesInfo=clGenesInfo, plotOnlyConnectedNodesNetwork=FALSE)###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ###### # Explore the returned object ###### # Global view of the object and its structure: names(leukemiasClassifier) # List of Networks by classes: leukemiasClassifier@genesNetwork # Access to the nodes or edges of each network: getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,] getNodes(leukemiasClassifier@genesNetwork$AML)[1:50] ###### # Plotting ###### # Example: Plotting the sub-network of a class classificationGenes # Get the sub-network containing only the classification genes: subNet <- getSubNetwork(leukemiasClassifier@genesNetwork, leukemiasClassifier@classificationGenes) # Get the classification genes' info/details: clGenesInfo <- genesDetails(leukemiasClassifier@classificationGenes) # Plot the network of the class "ALL" plotNetwork(subNet$ALL, genesInfo=clGenesInfo, plotOnlyConnectedNodesNetwork=FALSE)
Contains a genes ranking and the genes info calculated by geNetClassifier.
(Slots @classificationGenes and @genesRanking from geNetClassifier output)
signature(object = "GenesRanking"): Returns data.frames with information about the genes.
signature(object = "GenesRanking"): Returns a matrix containing the ranked genes.
signature(object = "GenesRanking", numGenesClass): Returns a new GenesRanking object containing only the top genes of each class.
signature(object = "GenesRanking"): Returns the classes for which the genes are ranked.
signature(object = "GenesRanking"): Returns the number of available ranked genes per class.
signature(object = "GenesRanking"): Returns the number of significant genes per class (genes over the given posterior probability threshold).
signature(x = "GenesRanking", y = "missing"): Plots the genes' posterior probability. Wrapper of calculateGenesRanking.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
For more information on how the ranking is calculated and how to interpret the given information, see the package vignette.
Main package function and classifier training:
geNetClassifier
Plot the ranking genes's posterior probability: plot.GenesRanking
###### # Calculate a genesRanking ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Calculate the genesRanking with calculateGenesRanking() ## Not run: genesRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", returnRanking="full") ## End(Not run) # geNetClassifier() also calculates a genes ranking # Sample output: data(leukemiasClassifier) genesRanking <- leukemiasClassifier@genesRanking ###### # Exploring the rankings ###### # Number of available genes in the ranking: numGenes(genesRanking) # Number of significant genes (genes with posterior probability over the threshold. # Default: lpThreshold=0.95): numSignificantGenes(genesRanking) # Top 10 genes of CML: genesDetails(genesRanking)$CML[1:10,] # To get a sub ranking with the top 10 genes: getTopRanking(genesRanking, 10) # Genes details of the top 10 genes: genesDetails(getTopRanking(genesRanking, 10)) ###### # Exploring the genes used for training the classifier ###### numGenes(leukemiasClassifier@classificationGenes) leukemiasClassifier@classificationGenes #genesDetails(leukemiasClassifier@classificationGenes) # List by classes genesDetails(leukemiasClassifier@classificationGenes)$AML # Show a class genes # If your R console wraps the table rows, try widening your display width: # options(width=200) ###### # Creating a GenesRanking object # i.e. To use geNetClassifier() with a ranking based on another algorithm ###### ### 1. Calculate gene scores # Two classes: geneScore <- matrix(sample(seq(0,1,by=0.01), size=100, replace=TRUE)) colnames(geneScore) <- "BothClasses" rownames(geneScore) <- paste("Gene", 1:100, sep="") # More than two classes: geneScore <- matrix(sample(seq(0,1,by=0.01), size=300, replace=TRUE), ncol=3) colnames(geneScore) <- paste("Class", 1:3, sep="") rownames(geneScore) <- paste("Gene", 1:100, sep="") ### 2. Create object postProb <- geneScore ord <- apply(postProb, 2, function(x) order(x, decreasing=TRUE)) numGenesClass <- apply(postProb, 2, function(x) sum(!is.na(x))) customRanking <- new("GenesRanking", postProb=postProb, ord=ord, numGenesClass=numGenesClass) # GenesRanking object ready: customRanking genesDetails(customRanking) customRanking@numGenesClass numSignificantGenes(customRanking) # geNetClassifier(..., precalcGenesRanking = customRanking)###### # Calculate a genesRanking ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Calculate the genesRanking with calculateGenesRanking() ## Not run: genesRanking <- calculateGenesRanking(leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", returnRanking="full") ## End(Not run) # geNetClassifier() also calculates a genes ranking # Sample output: data(leukemiasClassifier) genesRanking <- leukemiasClassifier@genesRanking ###### # Exploring the rankings ###### # Number of available genes in the ranking: numGenes(genesRanking) # Number of significant genes (genes with posterior probability over the threshold. # Default: lpThreshold=0.95): numSignificantGenes(genesRanking) # Top 10 genes of CML: genesDetails(genesRanking)$CML[1:10,] # To get a sub ranking with the top 10 genes: getTopRanking(genesRanking, 10) # Genes details of the top 10 genes: genesDetails(getTopRanking(genesRanking, 10)) ###### # Exploring the genes used for training the classifier ###### numGenes(leukemiasClassifier@classificationGenes) leukemiasClassifier@classificationGenes #genesDetails(leukemiasClassifier@classificationGenes) # List by classes genesDetails(leukemiasClassifier@classificationGenes)$AML # Show a class genes # If your R console wraps the table rows, try widening your display width: # options(width=200) ###### # Creating a GenesRanking object # i.e. To use geNetClassifier() with a ranking based on another algorithm ###### ### 1. Calculate gene scores # Two classes: geneScore <- matrix(sample(seq(0,1,by=0.01), size=100, replace=TRUE)) colnames(geneScore) <- "BothClasses" rownames(geneScore) <- paste("Gene", 1:100, sep="") # More than two classes: geneScore <- matrix(sample(seq(0,1,by=0.01), size=300, replace=TRUE), ncol=3) colnames(geneScore) <- paste("Class", 1:3, sep="") rownames(geneScore) <- paste("Gene", 1:100, sep="") ### 2. Create object postProb <- geneScore ord <- apply(postProb, 2, function(x) order(x, decreasing=TRUE)) numGenesClass <- apply(postProb, 2, function(x) sum(!is.na(x))) customRanking <- new("GenesRanking", postProb=postProb, ord=ord, numGenesClass=numGenesClass) # GenesRanking object ready: customRanking genesDetails(customRanking) customRanking@numGenesClass numSignificantGenes(customRanking) # geNetClassifier(..., precalcGenesRanking = customRanking)
Gene symbols to use as gene labels in the package examples.
Source: simplified version of genes.human.annotation from GATExplorer (http://bioinfow.dep.usal.es/xgate/mapping/mapping.php?content=annotationfiles).
data(geneSymbols)data(geneSymbols)
Named character vector containing the gene symbol as content, and the associated Ensemble ID as name.
data(geneSymbols) head(geneSymbols)data(geneSymbols) head(geneSymbols)
Allows to train the classifier, calculate the genes network...
geNetClassifier(eset, sampleLabels, plotsName = NULL, buildClassifier = TRUE, estimateGError = FALSE, calculateNetwork = TRUE, labelsOrder = NULL, geneLabels = NULL, numGenesNetworkPlot = 100, minGenesTrain = 1, maxGenesTrain = 100, continueZeroError = FALSE, numIters = 6, lpThreshold = 0.95, numDecimals = 3, removeCorrelations = FALSE, correlationsThreshold = 0.8, correlationMethod = "pearson", removeInteractions = FALSE, interactionsThreshold = 0.5, minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8, IQRfilterPercentage = 0, skipInteractions = TRUE, precalcGenesNetwork = NULL, precalcGenesRanking = NULL, returnAllGenesRanking = TRUE, kernel="linear", verbose=TRUE, ...)geNetClassifier(eset, sampleLabels, plotsName = NULL, buildClassifier = TRUE, estimateGError = FALSE, calculateNetwork = TRUE, labelsOrder = NULL, geneLabels = NULL, numGenesNetworkPlot = 100, minGenesTrain = 1, maxGenesTrain = 100, continueZeroError = FALSE, numIters = 6, lpThreshold = 0.95, numDecimals = 3, removeCorrelations = FALSE, correlationsThreshold = 0.8, correlationMethod = "pearson", removeInteractions = FALSE, interactionsThreshold = 0.5, minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8, IQRfilterPercentage = 0, skipInteractions = TRUE, precalcGenesNetwork = NULL, precalcGenesRanking = NULL, returnAllGenesRanking = TRUE, kernel="linear", verbose=TRUE, ...)
eset |
ExpressionSet or matrix. Gene expression of the train samples (positive & non-logaritmic normalized values). |
sampleLabels |
Character. PhenoData variable (column name) containing the train samples class labels. |
labelsOrder |
Vector or Factor. Order in which the labels should be shown in the returned results and plots. |
plotsName |
Character. File name with which the plots should be saved. If not provided, no plots will be drawn. |
buildClassifier |
Logical. If TRUE trains a classifier with the given samples. |
estimateGError |
Logical. If TRUE uses cross-validation to estimate the Generalization Error of a classiffier trained with the given samples. |
calculateNetwork |
Logical. If TRUE calculates the coexpression network between the best genes. |
geneLabels |
Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots. |
numGenesNetworkPlot |
Integer. Number of genes to show in the coexpression network for each class. |
minGenesTrain |
Integer. Maximum number of genes per class to train the classifier with. |
maxGenesTrain |
Integer. Maximum number of genes per class to train the classifier with. |
continueZeroError |
Logical. If TRUE, the program will continue testing combinations with more genes even if error 0 has been reached. |
numIters |
Integer. Number of iterations to determine the optimum number of genes (between |
lpThreshold |
Numeric between 0 and 1. Required posterior probability value to consider a gene 'significant'. |
removeCorrelations |
Logical. If TRUE, no correlated genes will be chosen to train the classifier. |
correlationsThreshold |
Numeric between 0 and 1. Minimum Pearson's correlation coefficient to consider genes correlated. |
correlationMethod |
"pearson", "kendall" or "spearman". Type of correlation to calculate between genes. |
removeInteractions |
Logical. If TRUE, genes with Mutual Information coefficient over the threshold will not be chosen to train the classifier. |
interactionsThreshold |
Numeric between 0 and 1. Minimum Mutual Information coefficient to consider two genes equivalent. |
numDecimals |
Integer. Number of decimals to show in the statistics. |
minProbAssignCoeff |
Numeric. Allows modifying the required probability to assign a sample to a class in the internal crossvalidation. For details see: |
minDiffAssignCoeff |
Numeric. Allows modifying the difference of probabilities required between the most likely class and second most likely class to assign a sample. For details see: |
IQRfilterPercentage |
Integer. InterQuartile Range (IQR) filter applied to the initial data. Not recommended for more than two classes. |
skipInteractions |
Logical. If TRUE, the interactions between genes are not calculated (they will not appear on the genes network). Saves some execution time. Only available if |
precalcGenesNetwork |
|
precalcGenesRanking |
|
returnAllGenesRanking |
Logical. If TRUE, returns the whole genes ranking. If FALSE the returned ranking contains only the significant genes (genes over lpThreshold). |
verbose |
Logical. If TRUE, messages indicating the execution progress will be shown. |
kernel |
Character. Type of SVM kernel. Default: "linear", |
... |
Other arguments to pass to the |
A GeNetClassifierReturn object containing the classifier and the genes chosen to train it (classificationGenes), Cross-Validation statistics, the whole GenesRanking and each class' GenesNetwork (if requested).
Several plots saved as 'plotsName_....pdf' in the working directory.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Packages used by this function:EBarrays: emfit (Implements EM algorithm for gene expression mixture model) and ebPatterns, for calculating the gene ranking.
Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski (2007). EBarrays: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification. R package.e1071: svm.
Evgenia Dimitriadou, Kurt Hornik, Friedrich Leisch, David Meyer and Andreas Weingessel (2011). e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. R package.
http://CRAN.R-project.org/package=e1071ipred: kfoldcv (computes feasible sample sizes for the k groups in k-fold cv) for the cross-validations.
Andrea Peters and Torsten Hothorn (2012). ipred: Improved Predictors. R package. http://CRAN.R-project.org/package=ipredminet for the Mutual Information network.
Patrick E. Meyer, Frederic Lafitte and Gianluca Bontempi (2008). MINET: An open source R/Bioconductor Package for Mutual Information based Network Inference. BMC Bioinformatics.
http://www.biomedcentral.com/1471-2105/9/461RColorBrewer (brewer.pal) for palettes in some of the plots.
Erich Neuwirth (2011). RColorBrewer: ColorBrewer palettes. R package.
http://CRAN.R-project.org/package=RColorBrewerigraph for the graphical representation of the networks.
Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.sf.net
To query the classifier: queryGeNetClassifier
All functions in the package: geNetClassifier-package
######## # Load libraries and training data ######## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) ######## # Training ######## # NOTE: Training the classifier takes a while... # Choose ONE of the followings, or modify to suit your needs: ## Not run: # "Basic" execution: All default parameters leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") # All default parameters also estimatings the classiffier's Generalization Error: # ( by default: buildClassifier=TRUE, calculateNetwork=TRUE) # Takes longer time than the basic execution leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", plotsName="leukemiasClassifier", estimateGError=TRUE) # Faster execution (few minutes - depending on the computer): # By skipping the calculation of the interactions (MI) betwen the genes, # and reducing the number of genes to explore when training the classifier # (100 by default), the execution time can be sightly reduced leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", plotsName="leukemiasClassifier", skipInteractions= TRUE, maxGenesTrain=20) # To any of these examples, you can add/remove the argument geneLabels, # in order to show/remove the gene name in the rankings and plots: # The argument labelsOrder allows showing the classes in a specific order # i.e.: labelsOrder=c("ALL","CLL","AML",CML","NoL") save(leukemiasClassifier, file="leukemiasClassifier.RData") # Save execution result # For loading the saved object in the future... # (If it doesn't find it, use getwd() to make sure you are in the right directory) #load("leukemiasClassifier.RData") # To avoid having to train a classifier to continue learning to use the package, # you can load the package's pre-executed example: data(leukemiasClassifier) #This example classifier was trained with the following code: #leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # "LeukemiaType", plotsName="leukemiasClassifier", buildClassifier=TRUE, # estimateGError=TRUE, calculateNetwork=TRUE, geneLabels=geneSymbols) ######## # Explore the returned object: ######## names(leukemiasClassifier) # More details on the class' help file: ?GeNetClassifierReturn # Further options: # The trained classifier can be used to find the class of new samples: ?queryGeNetClassifier # The default plots can be modified and presonalized to fit the user needs: ?calculateGenesRanking ?plotNetwork ?plotDiscriminantPower ?plotExpressionProfiles ## End(Not run)######## # Load libraries and training data ######## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) ######## # Training ######## # NOTE: Training the classifier takes a while... # Choose ONE of the followings, or modify to suit your needs: ## Not run: # "Basic" execution: All default parameters leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") # All default parameters also estimatings the classiffier's Generalization Error: # ( by default: buildClassifier=TRUE, calculateNetwork=TRUE) # Takes longer time than the basic execution leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", plotsName="leukemiasClassifier", estimateGError=TRUE) # Faster execution (few minutes - depending on the computer): # By skipping the calculation of the interactions (MI) betwen the genes, # and reducing the number of genes to explore when training the classifier # (100 by default), the execution time can be sightly reduced leukemiasClassifier <- geNetClassifier(eset=leukemiasEset[,trainSamples], sampleLabels="LeukemiaType", plotsName="leukemiasClassifier", skipInteractions= TRUE, maxGenesTrain=20) # To any of these examples, you can add/remove the argument geneLabels, # in order to show/remove the gene name in the rankings and plots: # The argument labelsOrder allows showing the classes in a specific order # i.e.: labelsOrder=c("ALL","CLL","AML",CML","NoL") save(leukemiasClassifier, file="leukemiasClassifier.RData") # Save execution result # For loading the saved object in the future... # (If it doesn't find it, use getwd() to make sure you are in the right directory) #load("leukemiasClassifier.RData") # To avoid having to train a classifier to continue learning to use the package, # you can load the package's pre-executed example: data(leukemiasClassifier) #This example classifier was trained with the following code: #leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # "LeukemiaType", plotsName="leukemiasClassifier", buildClassifier=TRUE, # estimateGError=TRUE, calculateNetwork=TRUE, geneLabels=geneSymbols) ######## # Explore the returned object: ######## names(leukemiasClassifier) # More details on the class' help file: ?GeNetClassifierReturn # Further options: # The trained classifier can be used to find the class of new samples: ?queryGeNetClassifier # The default plots can be modified and presonalized to fit the user needs: ?calculateGenesRanking ?plotNetwork ?plotDiscriminantPower ?plotExpressionProfiles ## End(Not run)
Object wich wraps all the items returned by geNetClassifier. It usually contains the classifier, the genes ranking and information, the network and any other requested statistics.
signature(x = "GeNetClassifierReturn"): Shows the available slots in the object.
signature(object = "GeNetClassifierReturn"): Shows an overview of all the slots in the object.
Available slots deppends on the arguments used to call geNetClassifier():
call:call. Always available.
classifier:list. SVM classifier. Only available if geNetClassifier() was called with option buildClassifier=TRUE (default settings).
classificationGenes:GenesRanking. Genes used to train the classifier. Only available if geNetClassifier() was called with option buildClassifier=TRUE (default settings).
generalizationError:GeneralizationError. Statistics calculated for the current training set and options.
Only available if geNetClassifier() was called with option estimateGError=TRUE (False by default).
genesRanking:GenesRanking. Whole genes ranking (if returnAllGenesRanking=TRUE) or significant genes ranking (if returnAllGenesRanking=FALSE, includes only the genes with posterior probability over lpThreshold)
genesRankingType:character. "all", "significant" or "significantNonRedundant"
genesNetwork:List of GenesNetwork. Only available if geNetClassifier() was called with option calculateNetwork=TRUE (default settings).
genesNetworkType:character. At the moment, only "topGenes" available.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training: geNetClassifierplot.GeNetClassifierReturn
###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ###### # Explore the returned object ###### # Global view of the object and its structure: leukemiasClassifier names(leukemiasClassifier) ### Depending on the available slots: # Call and acess to the classifier: leukemiasClassifier@call leukemiasClassifier@classifier # Genes used for training the classifier: numGenes(leukemiasClassifier@classificationGenes) leukemiasClassifier@classificationGenes # Show de tetails of the genes of a class genesDetails(leukemiasClassifier@classificationGenes)$AML # If your R console wraps the table rows, try widening your display width: # options(width=200) # Generalization Error estimated by cross-validation: leukemiasClassifier@generalizationError overview(leukemiasClassifier@generalizationError) # i.e. probabilityMatrix: leukemiasClassifier@generalizationError@probMatrix # i.e. statistics of the genes chosen in any of the CV loops for for AML: leukemiasClassifier@[email protected]$AML # List of Networks by classes: leukemiasClassifier@genesNetwork # Access to the nodes or edges of each network: getEdges(leukemiasClassifier@genesNetwork$AML) getNodes(leukemiasClassifier@genesNetwork$AML) # Genes ranking: leukemiasClassifier@genesRanking # Number of available genes in the ranking: numGenes(leukemiasClassifier@genesRanking) # Number of significant genes # (genes with posterior probability over lpThreshold, default=0.95) numSignificantGenes(leukemiasClassifier@genesRanking) # Top 10 genes of CML: genesDetails(leukemiasClassifier@genesRanking)$CML[1:10,] # To get a sub ranking with the top 10 genes: getTopRanking(leukemiasClassifier@genesRanking, 10) # Genes details of the top 10 genes: genesDetails(getTopRanking(leukemiasClassifier@genesRanking, 10))###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ###### # Explore the returned object ###### # Global view of the object and its structure: leukemiasClassifier names(leukemiasClassifier) ### Depending on the available slots: # Call and acess to the classifier: leukemiasClassifier@call leukemiasClassifier@classifier # Genes used for training the classifier: numGenes(leukemiasClassifier@classificationGenes) leukemiasClassifier@classificationGenes # Show de tetails of the genes of a class genesDetails(leukemiasClassifier@classificationGenes)$AML # If your R console wraps the table rows, try widening your display width: # options(width=200) # Generalization Error estimated by cross-validation: leukemiasClassifier@generalizationError overview(leukemiasClassifier@generalizationError) # i.e. probabilityMatrix: leukemiasClassifier@generalizationError@probMatrix # i.e. statistics of the genes chosen in any of the CV loops for for AML: leukemiasClassifier@generalizationError@classificationGenes.stats$AML # List of Networks by classes: leukemiasClassifier@genesNetwork # Access to the nodes or edges of each network: getEdges(leukemiasClassifier@genesNetwork$AML) getNodes(leukemiasClassifier@genesNetwork$AML) # Genes ranking: leukemiasClassifier@genesRanking # Number of available genes in the ranking: numGenes(leukemiasClassifier@genesRanking) # Number of significant genes # (genes with posterior probability over lpThreshold, default=0.95) numSignificantGenes(leukemiasClassifier@genesRanking) # Top 10 genes of CML: genesDetails(leukemiasClassifier@genesRanking)$CML[1:10,] # To get a sub ranking with the top 10 genes: getTopRanking(leukemiasClassifier@genesRanking, 10) # Genes details of the top 10 genes: genesDetails(getTopRanking(leukemiasClassifier@genesRanking, 10))
Returns the network's edges (relations between genes).
signature(object = "GenesNetwork")Main package function and classifier training:
geNetClassifier
This method's class (GenesNetwork) help page.
data(leukemiasClassifier) getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,]data(leukemiasClassifier) getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,]
Returns the network's nodes (genes).
signature(object = "GenesNetwork")Main package function and classifier training:
geNetClassifier
This method's class (GenesNetwork) help page.
data(leukemiasClassifier) getNodes(leukemiasClassifier@genesNetwork$AML)[1:5]data(leukemiasClassifier) getNodes(leukemiasClassifier@genesNetwork$AML)[1:5]
Returns the number of edges (relationships) in the network.
signature(object = "GenesNetwork")Main package function and classifier training:
geNetClassifier
This method's class (GenesNetwork) help page.
data(leukemiasClassifier) getNumEdges(leukemiasClassifier@genesNetwork$AML)data(leukemiasClassifier) getNumEdges(leukemiasClassifier@genesNetwork$AML)
Returns the number of nodes (genes) in the network.
signature(object = "GenesNetwork")Main package function and classifier training:
geNetClassifier
This method's class (GenesNetwork) help page.
data(leukemiasClassifier) getNumNodes(leukemiasClassifier@genesNetwork$AML)data(leukemiasClassifier) getNumNodes(leukemiasClassifier@genesNetwork$AML)
Shows the ranking as matrix: Ranked genes by classes.
object |
a GenesRanking |
showGeneID |
boolean. If TRUE, the genes will be shown with the gene IDs used in the expressionSet. This matrix will be |
showGeneLabels |
boolean. If TRUE, and if the ranking contains gene labels, the ranking matrix will use them. This matrix will be |
The method returns a list with one or two matrices: ...$geneLabels and ...$geneID.
Main package function and classifier training:
geNetClassifier
This method's class (GenesRanking) help page.
data(leukemiasClassifier) getRanking(leukemiasClassifier@classificationGenes) # Top 7 genes (two ways): getRanking(leukemiasClassifier@genesRanking)$geneLabels[1:7,] getRanking(getTopRanking(leukemiasClassifier@genesRanking, 7)) # Show gene ID and select a class: getRanking(leukemiasClassifier@classificationGenes, showGeneID=TRUE )$geneID[,"CML", drop=FALSE]data(leukemiasClassifier) getRanking(leukemiasClassifier@classificationGenes) # Top 7 genes (two ways): getRanking(leukemiasClassifier@genesRanking)$geneLabels[1:7,] getRanking(getTopRanking(leukemiasClassifier@genesRanking, 7)) # Show gene ID and select a class: getRanking(leukemiasClassifier@classificationGenes, showGeneID=TRUE )$geneID[,"CML", drop=FALSE]
Returns the sub-network formed by the given genes.
getSubNetwork(network, genes, showWarnings=TRUE)getSubNetwork(network, genes, showWarnings=TRUE)
network |
GenesNetwork or GenesNetwork list containing the whole network. |
genes |
GenesRanking or character vector. Genes in the new network. |
showWarnings |
Logical. If true, shows warnings if the given genes are not in the network. |
A GenesNetwork or list of networks between the given genes.
Main package function and classifier training:
geNetClassifier
This method's class (GenesNetwork) help page.
data(leukemiasClassifier) clGenesSubNet <- getSubNetwork(leukemiasClassifier@genesNetwork, leukemiasClassifier@classificationGenes) getSubNetwork(leukemiasClassifier@genesNetwork, getTopRanking(leukemiasClassifier@genesRanking, numGenesClass=30))data(leukemiasClassifier) clGenesSubNet <- getSubNetwork(leukemiasClassifier@genesNetwork, leukemiasClassifier@classificationGenes) getSubNetwork(leukemiasClassifier@genesNetwork, getTopRanking(leukemiasClassifier@genesRanking, numGenesClass=30))
Returns a new ranking containing only the top genes of each class.
object |
a GenesRanking |
numGenesClass |
integer. Number of genes per class. |
getTopRanking(object, numGenesClass)
Main package function and classifier training:
geNetClassifier
This method's class (GenesRanking) help page.
data(leukemiasClassifier) # Sample classifier # Sub-ranking with the top 10 genes: getTopRanking(leukemiasClassifier@genesRanking, 10)data(leukemiasClassifier) # Sample classifier # Sub-ranking with the top 10 genes: getTopRanking(leukemiasClassifier@genesRanking, 10)
A sample of the object returned by geNetClassifier. Containins the classifier, the network, and the gene statistics.
data(leukemiasClassifier)data(leukemiasClassifier)
GeNetClassifierReturn object
data(leukemiasClassifier) # Global view of the object and its structure: leukemiasClassifier names(leukemiasClassifier) # Call and acess to the classifier: leukemiasClassifier@call leukemiasClassifier@classifier # Genes used for training the classifier: numGenes(leukemiasClassifier@classificationGenes) leukemiasClassifier@classificationGenes genesDetails(leukemiasClassifier@classificationGenes) # Generalization Error estimated by cross-validation: # leukemiasClassifier@generalizationError # overview(leukemiasClassifier@generalizationError) # List of Networks by classes: leukemiasClassifier@genesNetwork # Access to the nodes or edges of each network: getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,] getNodes(leukemiasClassifier@genesNetwork$AML)[1:50] # Global genes ranking: leukemiasClassifier@genesRanking numGenes(leukemiasClassifier@genesRanking) numSignificantGenes(leukemiasClassifier@genesRanking) # getTopRanking(leukemiasClassifier@genesRanking, 10)data(leukemiasClassifier) # Global view of the object and its structure: leukemiasClassifier names(leukemiasClassifier) # Call and acess to the classifier: leukemiasClassifier@call leukemiasClassifier@classifier # Genes used for training the classifier: numGenes(leukemiasClassifier@classificationGenes) leukemiasClassifier@classificationGenes genesDetails(leukemiasClassifier@classificationGenes) # Generalization Error estimated by cross-validation: # leukemiasClassifier@generalizationError # overview(leukemiasClassifier@generalizationError) # List of Networks by classes: leukemiasClassifier@genesNetwork # Access to the nodes or edges of each network: getEdges(leukemiasClassifier@genesNetwork$AML)[1:5,] getNodes(leukemiasClassifier@genesNetwork$AML)[1:50] # Global genes ranking: leukemiasClassifier@genesRanking numGenes(leukemiasClassifier@genesRanking) numSignificantGenes(leukemiasClassifier@genesRanking) # getTopRanking(leukemiasClassifier@genesRanking, 10)
Saves the GenesNetwork as text file.
network2txt(network, filePrefix = NULL, nwClass = NULL)network2txt(network, filePrefix = NULL, nwClass = NULL)
network |
GenesNetwork or list of GenesNetworks. |
filePrefix |
Character. File name prefix. |
nwClass |
Character. Network class. |
Saves the networks as text (.txt) files.
The files will be saved in the current working directory as filePrefix_className.txt.
Main package function and classifier training:
geNetClassifier
This method's class (GenesNetwork) help page.
## Load or calculate a network: data(leukemiasClassifier) ## Export as text: network2txt(leukemiasClassifier@genesNetwork, filePrefix="leukemiasNetwork")## Load or calculate a network: data(leukemiasClassifier) ## Export as text: network2txt(leukemiasClassifier@genesNetwork, filePrefix="leukemiasNetwork")
Provides the number of genes in the genesRanking.
signature(object = "GenesRanking")Main package function and classifier training:
geNetClassifier
This method's class (GenesRanking) help page.
data(leukemiasClassifier) numGenes(leukemiasClassifier@genesRanking)data(leukemiasClassifier) numGenes(leukemiasClassifier@genesRanking)
Provides the number of ranked genes over the posterior probability threshold
object |
a GenesRanking |
lpThreshold |
Posterior probability threshold |
numSignificantGenesType |
"ranked" or "global". |
numSignificantGenes(object, lpThreshold=0.95, numSignificantGenesType="ranked")
Main package function and classifier training:
geNetClassifier
This method's class (GenesRanking) help page.
data(leukemiasClassifier) # Total number of genes in the ranking: numGenes(leukemiasClassifier@genesRanking) # Number of genes ovher the posterior probability threshold # Default: lpThreshold=0.95, numSignificantGenesType="ranked" numSignificantGenes(leukemiasClassifier@genesRanking) numSignificantGenes(leukemiasClassifier@genesRanking, numSignificantGenesType="global") numSignificantGenes(leukemiasClassifier@genesRanking, lpThreshold=0.90)data(leukemiasClassifier) # Total number of genes in the ranking: numGenes(leukemiasClassifier@genesRanking) # Number of genes ovher the posterior probability threshold # Default: lpThreshold=0.95, numSignificantGenesType="ranked" numSignificantGenes(leukemiasClassifier@genesRanking) numSignificantGenes(leukemiasClassifier@genesRanking, numSignificantGenesType="global") numSignificantGenes(leukemiasClassifier@genesRanking, lpThreshold=0.90)
Provides an overview of all the slots in the object.
It can be applied to the following classes:
signature(object = "GenesNetwork")signature(object = "GenesRanking")signature(object = "GeNetClassifierReturn")signature(object = "GeneralizationError")Main package function and classifier training:
geNetClassifier
This method's classes help pages:GenesRanking GenesNetwork GeNetClassifierReturn GeneralizationError
data(leukemiasClassifier) # geNetClassifier return: overview(leukemiasClassifier) # Generalization Error and stats estimated by cross-validation: overview(leukemiasClassifier@generalizationError) # A GenesNetwork: # (a class has to be selected, otherwise it is a list) overview(leukemiasClassifier@genesNetwork$ALL) # For a GenesRanking, we recommend to use genesDetails() instead: genesDetails(leukemiasClassifier@classificationGenes)$AMLdata(leukemiasClassifier) # geNetClassifier return: overview(leukemiasClassifier) # Generalization Error and stats estimated by cross-validation: overview(leukemiasClassifier@generalizationError) # A GenesNetwork: # (a class has to be selected, otherwise it is a list) overview(leukemiasClassifier@genesNetwork$ALL) # For a GenesRanking, we recommend to use genesDetails() instead: genesDetails(leukemiasClassifier@classificationGenes)$AML
Plots the posterior probability of the genes ordered by class ranking.
## S3 method for class 'GenesRanking' plot(x, y="missing", numGenesPlot=1000, plotTitle="Significant genes", lpThreshold = 0.95, ...)## S3 method for class 'GenesRanking' plot(x, y="missing", numGenesPlot=1000, plotTitle="Significant genes", lpThreshold = 0.95, ...)
x |
GenesRanking. |
numGenesPlot |
Numeric. Number of genes to plot. |
plotTitle |
Character. Plot main title. |
lpThreshold |
Numeric between 0 and 1. Required posterior probability value to consider a gene 'significant'. |
y |
Not required. |
... |
Not required |
Significant genes: Genes with posterior probability over 'lpThreshold'.
More significant genes may mean:
Very different class
More systemic disease
Plot lines represet the posterior probability of genes, sorted by rank from left to right.
In order to find genes that diferentiate the classes from each other, the function ranks the genes bassed on their posterior probability for each class.
The posterior probability represents how well a gene differentiates samples from a class, from samples from other classes. Therefore, Genes with high posterior probability are good to differentiate a class from all the others.
This posterior probability is calculated by emfit (pkg:EBarrays), an expectation-maximization (EM) algorithm for gene expression mixture model.
Posterior probability plot of the top genes.
# Load or calculate a ranking (or a classifier with geNetClassifier) data(leukemiasClassifier) # Sample trained classifier, @genesRanking # Default plot: plot(leukemiasClassifier@genesRanking) # Changing options: plot(leukemiasClassifier@genesRanking, numGenesPlot=5000, plotTitle="Leukemias", lpThreshold=0.9)# Load or calculate a ranking (or a classifier with geNetClassifier) data(leukemiasClassifier) # Sample trained classifier, @genesRanking # Default plot: plot(leukemiasClassifier@genesRanking) # Changing options: plot(leukemiasClassifier@genesRanking, numGenesPlot=5000, plotTitle="Leukemias", lpThreshold=0.9)
Allows generating the plots from the objet created by geNetClassifier.
## S3 method for class 'GeNetClassifierReturn' plot(x, y="missing", fileName = NULL, lpThreshold = 0.95, numGenesLpPlot = 1000, numGenesNetworkPlot = 100, geneLabels = NULL, verbose = TRUE, ...)## S3 method for class 'GeNetClassifierReturn' plot(x, y="missing", fileName = NULL, lpThreshold = 0.95, numGenesLpPlot = 1000, numGenesNetworkPlot = 100, geneLabels = NULL, verbose = TRUE, ...)
x |
GeNetClassifierReturn. Object returned by the main function " |
fileName |
Character. File name to save the plots. |
lpThreshold |
Numeric between 0 and 1. Required posterior probability value to consider a gene 'significant'. |
numGenesLpPlot |
Integer. Number of genes to show in the significant genes plot. |
numGenesNetworkPlot |
Integer. Number of genes (nodes) to plot in the network. |
geneLabels |
Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots. |
verbose |
Logical. If TRUE, messages indicating the execution progress will be printed on screen. |
y |
Not required. |
... |
Not required |
The plots are generated by default by geNetClassifier. This function allows re-plotting them with different parameters.
Plots (depending on the available info):
- Significant genes
- Classification genes' Discriminant Power
- Top ranked genes network (for each class)
Main package function and classifier training: geNetClassifier
Class GeNetClassifierReturn
Other plotting functions:
- plotDiscriminantPower
- plot.GenesRanking
- plotNetwork
# Train or load an already trained classifier data(leukemiasClassifier) # Plot default plots on-screen plot(leukemiasClassifier) # Save plots on file # (includes Discriminant Power of all genes, but the networks will not be interactive) plot(leukemiasClassifier, fileName="newPlots")# Train or load an already trained classifier data(leukemiasClassifier) # Plot default plots on-screen plot(leukemiasClassifier) # Save plots on file # (includes Discriminant Power of all genes, but the networks will not be interactive) plot(leukemiasClassifier, fileName="newPlots")
Plots the assignment probabilities of a previous query.
plotAssignments(queryResult, realLabels, minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8, totalNumberOfClasses = NULL, pointSize=0.8, identify = FALSE)plotAssignments(queryResult, realLabels, minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8, totalNumberOfClasses = NULL, pointSize=0.8, identify = FALSE)
queryResult |
Object returned by |
realLabels |
Factor. Actual/real class of the samples. |
minProbAssignCoeff |
Numeric. See |
minDiffAssignCoeff |
Numeric. See |
totalNumberOfClasses |
Numeric. Total number of classes the classifier was trained with. The assignment probability is determined bassed on it. It is not needed if there are samples of all the training classes. |
pointSize |
Numeric. Point size modifier. |
identify |
Logical. If TRUE and supported (X11 or quartz devices), the plot will be interactive and clicking on a point will identify the sample the point represents. Press ESC or right-click on the plot screen to exit. |
Plot.
Main package function and classifier training: geNetClassifier
Querying the classifier: queryGeNetClassifier
########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## External Validation: ########################## # Select the samples to query the classifier # - External validation: samples not used for training testSamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples]) ########################## ## Plot: ########################## plotAssignments(queryResult, realLabels=leukemiasEset[,testSamples]$LeukemiaType)########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## External Validation: ########################## # Select the samples to query the classifier # - External validation: samples not used for training testSamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples]) ########################## ## Plot: ########################## plotAssignments(queryResult, realLabels=leukemiasEset[,testSamples]$LeukemiaType)
Calculates and plots the Discriminant Power of the genes in the given classifier.
plotDiscriminantPower(classifier, classificationGenes = NULL, geneLabels = NULL, classNames = NULL, plotDP = TRUE, fileName = NULL, returnTable = FALSE, verbose = TRUE)plotDiscriminantPower(classifier, classificationGenes = NULL, geneLabels = NULL, classNames = NULL, plotDP = TRUE, fileName = NULL, returnTable = FALSE, verbose = TRUE)
classifier |
Classifier returned by |
classificationGenes |
Vector or Matrix. IDs of the genes to plot. |
geneLabels |
Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots. |
classNames |
Named vector. Short version of the class names if different from the ones used to train the classifier. |
plotDP |
Logical. If TRUE, plots the discriminant power of the given genes. |
fileName |
Character. File name to save the plot with. If not provided, the plots will be shown through the standard output device. |
returnTable |
Logical. If TRUE, returns a table with the genes discriminant power. |
verbose |
Logical. If TRUE, messages indicating the execution progress will be printed on screen. |
The Discriminant Power represents the weight the (SVM) classifier gives each gene to separate the classes. It is calculated based on the coordinates of the support vectors. Genes with a high Discriminant Power are better for identifying samples from the class.
Data frame Optional. Data.frame containing the genes and their Discriminant Power.
Discriminant Power plot Optional. Shown throught the standard output devide or saved in the working directory as 'fileName.pdf' if fileName was provided.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training:
geNetClassifier
###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ###### # Discriminant Power ###### # Default (plots up to 20 genes) plotDiscriminantPower(leukemiasClassifier) # Plot a specific gene: plotDiscriminantPower(leukemiasClassifier, classificationGenes="ENSG00000169575") # Plot top5 genes of a class, and return their discriminant power: # Note: The discriminant Power can only be calculated for 'classificationGenes' # (genes chosen for training the classifier) genes <- getRanking(leukemiasClassifier@classificationGenes, showGeneID=TRUE)$geneID[1:5,"AML",drop=FALSE] # Top 5 genes of AML discPowerTable2 <- plotDiscriminantPower(leukemiasClassifier, classificationGenes=genes, returnTable=TRUE) # For plotting more than 20 genes or saving the plots as .pdf, provide a fileName plotDiscriminantPower(leukemiasClassifier, fileName="leukemiasClassifier_DiscriminantPower.pdf")###### # Load data and train a classifier ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ###### # Discriminant Power ###### # Default (plots up to 20 genes) plotDiscriminantPower(leukemiasClassifier) # Plot a specific gene: plotDiscriminantPower(leukemiasClassifier, classificationGenes="ENSG00000169575") # Plot top5 genes of a class, and return their discriminant power: # Note: The discriminant Power can only be calculated for 'classificationGenes' # (genes chosen for training the classifier) genes <- getRanking(leukemiasClassifier@classificationGenes, showGeneID=TRUE)$geneID[1:5,"AML",drop=FALSE] # Top 5 genes of AML discPowerTable2 <- plotDiscriminantPower(leukemiasClassifier, classificationGenes=genes, returnTable=TRUE) # For plotting more than 20 genes or saving the plots as .pdf, provide a fileName plotDiscriminantPower(leukemiasClassifier, fileName="leukemiasClassifier_DiscriminantPower.pdf")
Plots the expression profiles of the given genes.
plotExpressionProfiles(eset, genes=NULL, fileName=NULL, geneLabels=NULL, type="lines", sampleLabels=NULL, sampleColors=NULL, labelsOrder=NULL, classColors=NULL, sameScale=TRUE, showSampleNames=FALSE, showMean= FALSE, identify=TRUE, verbose=TRUE)plotExpressionProfiles(eset, genes=NULL, fileName=NULL, geneLabels=NULL, type="lines", sampleLabels=NULL, sampleColors=NULL, labelsOrder=NULL, classColors=NULL, sameScale=TRUE, showSampleNames=FALSE, showMean= FALSE, identify=TRUE, verbose=TRUE)
eset |
ExpressionSet or Matrix. Gene expression of the samples. |
genes |
Vector or Matrix. IDs of the genes to plot. |
fileName |
Character. File name to save the plots. If not provided, up to 20 genes will be shown on screen. |
geneLabels |
Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots. |
type |
Character. Plot type: "lines" or "boxplot". |
sampleLabels |
Character. PhenoData variable (column name) containing the train samples class labels. |
sampleColors |
Character. Colors for the lines of the samples. |
labelsOrder |
Vector or Factor. Order in which the labels should be shown in the returned results and plots. |
classColors |
Character. Colors for each of the classes or samples of the class. Provide either sampleColors or classColors, not both. |
sameScale |
Logical. If TRUE, plots all the genes in the same expression scale. |
showSampleNames |
Logical. If TRUE, the sample names are shown in the plot. Not recommended for big datasets. |
showMean |
Logical. If TRUE, plots the class expression mean. |
identify |
Logical. If TRUE and supported (X11 or quartz devices), the plot will be interactive and clicking on a point will identify the sample the point represents. Press ESC or right-click on the plot screen to exit. |
verbose |
Logical. If TRUE, a message indicating where the pdf is saved will be printed on screen. |
The expression profiles plot, saved in the working directory as 'fileName.pdf'.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
###### # Load libraries and expression data ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) ###### # Generic expression profile plot ###### # Plot expression of specific genes: selectedGenes <- c("ENSG00000169575","ENSG00000078399","ENSG00000005381","ENSG00000154511") plotExpressionProfiles(leukemiasEset, genes=selectedGenes, sampleLabels="LeukemiaType", type="boxplot") # Color samples: plotExpressionProfiles(leukemiasEset, genes="ENSG00000078399", sampleLabels="LeukemiaType", showMean=TRUE, identify=FALSE, sampleColors=c("grey","red") [(sampleNames(leukemiasEset) %in% c("GSM331386.CEL","GSM331392.CEL"))+1]) # Color classes: plotExpressionProfiles(leukemiasEset, genes="ENSG00000078399", sampleLabels="LeukemiaType", showMean=TRUE, identify=TRUE, classColors=c("red", "blue", "red", "red","red")) ###### # Expression profiles related to a classifier ###### # Train a classifier or load a trained one: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier # Plot expression of the selected genes in the train samples: plotExpressionProfiles(leukemiasEset[,trainSamples], leukemiasClassifier, sampleLabels="LeukemiaType", fileName="leukExprs.pdf") # Plot expression of all the genes of specific classes: classGenes <- getRanking(leukemiasClassifier@classificationGenes, showGeneID=TRUE)$geneID[,c("CLL"), drop=FALSE] # Feel free to modify plotExpressionProfiles(leukemiasEset, genes=classGenes, sampleLabels="LeukemiaType", type="boxplot") # Plot (on screen) the expression of the top ranked genes of each class plotExpressionProfiles(leukemiasEset, leukemiasClassifier, sampleLabels="LeukemiaType")###### # Load libraries and expression data ###### # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) ###### # Generic expression profile plot ###### # Plot expression of specific genes: selectedGenes <- c("ENSG00000169575","ENSG00000078399","ENSG00000005381","ENSG00000154511") plotExpressionProfiles(leukemiasEset, genes=selectedGenes, sampleLabels="LeukemiaType", type="boxplot") # Color samples: plotExpressionProfiles(leukemiasEset, genes="ENSG00000078399", sampleLabels="LeukemiaType", showMean=TRUE, identify=FALSE, sampleColors=c("grey","red") [(sampleNames(leukemiasEset) %in% c("GSM331386.CEL","GSM331392.CEL"))+1]) # Color classes: plotExpressionProfiles(leukemiasEset, genes="ENSG00000078399", sampleLabels="LeukemiaType", showMean=TRUE, identify=TRUE, classColors=c("red", "blue", "red", "red","red")) ###### # Expression profiles related to a classifier ###### # Train a classifier or load a trained one: trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier # Plot expression of the selected genes in the train samples: plotExpressionProfiles(leukemiasEset[,trainSamples], leukemiasClassifier, sampleLabels="LeukemiaType", fileName="leukExprs.pdf") # Plot expression of all the genes of specific classes: classGenes <- getRanking(leukemiasClassifier@classificationGenes, showGeneID=TRUE)$geneID[,c("CLL"), drop=FALSE] # Feel free to modify plotExpressionProfiles(leukemiasEset, genes=classGenes, sampleLabels="LeukemiaType", type="boxplot") # Plot (on screen) the expression of the top ranked genes of each class plotExpressionProfiles(leukemiasEset, leukemiasClassifier, sampleLabels="LeukemiaType")
Plots the coexpression and/or mutual information network for the given genes.
plotNetwork(genesNetwork, classificationGenes=NULL, genesRanking=NULL, genesInfo=NULL, geneLabels=NULL, returniGraphs=FALSE, plotType="dynamic", fileName=NULL, plotAllNodesNetwork=TRUE, plotOnlyConnectedNodesNetwork=FALSE, plotClassifcationGenesNetwork=FALSE, labelSize=0.5, vertexSize=NULL, width=NULL, height=NULL, verbose=TRUE)plotNetwork(genesNetwork, classificationGenes=NULL, genesRanking=NULL, genesInfo=NULL, geneLabels=NULL, returniGraphs=FALSE, plotType="dynamic", fileName=NULL, plotAllNodesNetwork=TRUE, plotOnlyConnectedNodesNetwork=FALSE, plotClassifcationGenesNetwork=FALSE, labelSize=0.5, vertexSize=NULL, width=NULL, height=NULL, verbose=TRUE)
genesNetwork |
List of GenesNetwork returned by |
classificationGenes |
Matrix or classificationGenes returned by |
genesRanking |
Matrix or genesRanking returned by |
genesInfo |
List or data.frame with the properties of the genes to plot: |
geneLabels |
Vector or Matrix. Gene name, ID or label which should be shown in the returned results and plots. |
returniGraphs |
deprecated. A list with the plotted networks as igraph objects is always returned (see |
plotType |
Character. "dynamic": Interactive plot. "static": One canvas split for the different networks. "pdf": All the networks are saved into a pdf file. |
fileName |
Character. File name to save the plot with. If not provided, the plots will be shown through the standard output device. |
plotAllNodesNetwork |
Logical. If TRUE, plots a network only with all the available genes |
plotOnlyConnectedNodesNetwork |
Logical. If TRUE, plots a network only with the connected nodes/genes |
plotClassifcationGenesNetwork |
Logical. If TRUE, plots a network only with the classification genes |
labelSize |
Integer. Gene/node label size for static and pdf plots. |
vertexSize |
Integer. Vertex minimum size. |
width |
Numeric. Dinamic or pdf plot width. |
height |
Numeric. Dinamic or pdf plot height. |
verbose |
Logical. If TRUE, messages indicating the execution progress will be shown. |
Graph list |
List with the plotted |
Network plots |
Shown throught the standard output devide or saved in the working directory as 'fileName.pdf' if |
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training:
geNetClassifier
Package igraph
plot.GenesNetwork() is an alias to this function. It can allso be called as i.e. plotNetwork(clGenesSubNet$ALL)
Note: The slot @genesNetwork returned by geNetClassifier is a List of GenesNetworks!
data(leukemiasClassifier) # Step 1: Select a network or sub network # Sub-network containing only the classification genes: clGenesSubNet <- getSubNetwork(leukemiasClassifier@genesNetwork, leukemiasClassifier@classificationGenes) # Step 2: Select the details/info about the genes to plot # Classification genes' info: clGenesInfo <- genesDetails(leukemiasClassifier@classificationGenes) # Step 3: Plot the network # Network plots can be interactive or plotted as PDF file. # - - Use plotType="pdf" to save the network as a static pdf file. # This option is recommended for getting an overview of several networks. # - - To get an interactive network, just skip this argument. # Plot ALL network: plotNetwork(clGenesSubNet$ALL, genesInfo=clGenesInfo) # Plot AML network containing only the conected nodes: plotNetwork(clGenesSubNet$ALL, genesInfo=clGenesInfo, plotAllNodesNetwork=FALSE, plotOnlyConnectedNodesNetwork=TRUE) # The equivalent code to the plot geNetClassifier creates by default is: topRanking <- getTopRanking(leukemiasClassifier@genesRanking, numGenesClass=100) netTopGenes <- getSubNetwork(leukemiasClassifier@genesNetwork, getRanking(topRanking, showGeneID=TRUE)$geneID) plotNetwork(netTopGenes, classificationGenes=leukemiasClassifier@classificationGenes, genesRanking=topRanking, plotAllNodesNetwork=TRUE, plotOnlyConnectedNodesNetwork=TRUE, plotType="pdf", labelSize=0.3, fileName="leukemiasClassifier") # In order to save the network as text file, you can use: network2txt(leukemiasClassifier@genesNetwork, filePrefix="leukemiasNetwork")data(leukemiasClassifier) # Step 1: Select a network or sub network # Sub-network containing only the classification genes: clGenesSubNet <- getSubNetwork(leukemiasClassifier@genesNetwork, leukemiasClassifier@classificationGenes) # Step 2: Select the details/info about the genes to plot # Classification genes' info: clGenesInfo <- genesDetails(leukemiasClassifier@classificationGenes) # Step 3: Plot the network # Network plots can be interactive or plotted as PDF file. # - - Use plotType="pdf" to save the network as a static pdf file. # This option is recommended for getting an overview of several networks. # - - To get an interactive network, just skip this argument. # Plot ALL network: plotNetwork(clGenesSubNet$ALL, genesInfo=clGenesInfo) # Plot AML network containing only the conected nodes: plotNetwork(clGenesSubNet$ALL, genesInfo=clGenesInfo, plotAllNodesNetwork=FALSE, plotOnlyConnectedNodesNetwork=TRUE) # The equivalent code to the plot geNetClassifier creates by default is: topRanking <- getTopRanking(leukemiasClassifier@genesRanking, numGenesClass=100) netTopGenes <- getSubNetwork(leukemiasClassifier@genesNetwork, getRanking(topRanking, showGeneID=TRUE)$geneID) plotNetwork(netTopGenes, classificationGenes=leukemiasClassifier@classificationGenes, genesRanking=topRanking, plotAllNodesNetwork=TRUE, plotOnlyConnectedNodesNetwork=TRUE, plotType="pdf", labelSize=0.3, fileName="leukemiasClassifier") # In order to save the network as text file, you can use: network2txt(leukemiasClassifier@genesNetwork, filePrefix="leukemiasNetwork")
Queries the classifier trained by geNetClassifier in order to find out the class of new samples.
queryGeNetClassifier(classifier, eset, minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8, verbose = TRUE)queryGeNetClassifier(classifier, eset, minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8, verbose = TRUE)
classifier |
Classifier returned by geNetClassifier. (@classifier) |
eset |
ExpressionSet or Matrix. Gene expression matrix of the new samples. |
minProbAssignCoeff |
Numeric. Coefficient to modify the minimum probability requird to assign a sample to a class. Reduce to improve call rate. Increase to reduce error.
0: Removes this restriction. The sample will always be assigned to the class with the highest probability.
between 0 and 1: Reduces the required probability to assign a sample to a class.
>1: Increases the required probability. Warning: if |
minDiffAssignCoeff |
Numeric. Coefficient to modify the required difference between the two most likelly classes. Reduce to improve call rate. Increase to reduce error.
0: Removes this restriction. The probability of the second most-likely class will not be taken into account.
between 1 and 1: Reduces the required difference to assign the sample.
>1: Increases the required difference. Warning: if |
verbose |
Logical. If TRUE, messages indicating the execution progress will be printed on screen. |
By default, in order to assign a sample two contitions must be met:
if minProbAssignCoeff = 1The probability of belonging to the class should be at least double of the random probability.
if minDiffAssignCoeff = 0.8The difference of probabilities between the most likely class and the second most likely class should be more than 80
This means, that in a 4-class classifier, in order to assing a sample, the highest probabiity should be at least 0.5 (2x0.25), and the next most-likely-class should have a probability at least 0.2 (80
If these conditions are not met, the sample will be left as notAssigned.
Modify the arguments values in order to modify these assignment conditions. Setting minProbAssignCoeff = 0 and minDiffAssignCoeff = 0 all samples will be assigned to the most likely class without any further restrictions.
List:
call Command used to execute the function.
classes Classes to wich each of the samples were asigned to.
probabilities Probabilities to the 2 classes each sample is most likelly to belong to.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training: geNetClassifier
Query summary: querySummary
External validation stats: externalValidation.stats and externalValidation.probMatrix
########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: # There should be the same number of samples from each class. trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## Classifier Query ########################## # Select the samples to query the classifier # - Real use: samples whose class we want to known querySamples <- "GSM330154.CEL" # - External validation: samples not used for training querySamples <- c(1:60)[-trainSamples] #### Make a query to the classifier ("ask" about what class the new samples are): queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,querySamples]) # See the class it assigned to each sample: queryResult$class[1:5] # Or the samples which it wasn't sure about: t(queryResult$probabilities[,queryResult$class=="NotAssigned"]) # Obtain an overview of the results querySummary(queryResult) #### Optional: Modify assignment conditions # (minDiffCoef=0, minProbCoef=0: All samples will be assigned to the most likely class) queryResult_AssignAll <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,querySamples], minDiffAssignCoeff=0, minProbAssignCoeff=0) # No samples are left as "NotAssigned": queryResult$probabilities[,queryResult_AssignAll$class=="NotAssigned"] #### External validation: # Confusion matrix: confMatrix <- table(leukemiasEset[,querySamples]$LeukemiaType, queryResult_AssignAll$class) # New accuracy, call rate, sensitivity and specificity: externalValidation.stats(confMatrix) # Probability matrix for the assigned samples externalValidation.probMatrix(queryResult, leukemiasEset[,querySamples]$LeukemiaType)########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: # There should be the same number of samples from each class. trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) # summary(leukemiasEset$LeukemiaType[trainSamples]) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## Classifier Query ########################## # Select the samples to query the classifier # - Real use: samples whose class we want to known querySamples <- "GSM330154.CEL" # - External validation: samples not used for training querySamples <- c(1:60)[-trainSamples] #### Make a query to the classifier ("ask" about what class the new samples are): queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,querySamples]) # See the class it assigned to each sample: queryResult$class[1:5] # Or the samples which it wasn't sure about: t(queryResult$probabilities[,queryResult$class=="NotAssigned"]) # Obtain an overview of the results querySummary(queryResult) #### Optional: Modify assignment conditions # (minDiffCoef=0, minProbCoef=0: All samples will be assigned to the most likely class) queryResult_AssignAll <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,querySamples], minDiffAssignCoeff=0, minProbAssignCoeff=0) # No samples are left as "NotAssigned": queryResult$probabilities[,queryResult_AssignAll$class=="NotAssigned"] #### External validation: # Confusion matrix: confMatrix <- table(leukemiasEset[,querySamples]$LeukemiaType, queryResult_AssignAll$class) # New accuracy, call rate, sensitivity and specificity: externalValidation.stats(confMatrix) # Probability matrix for the assigned samples externalValidation.probMatrix(queryResult, leukemiasEset[,querySamples]$LeukemiaType)
Counts the number of samples assigned to each class and calculates basic statistics regarding the assignment probabilities.
querySummary(queryResult, showNotAssignedSamples = TRUE, numDecimals = 2, verbose = TRUE)querySummary(queryResult, showNotAssignedSamples = TRUE, numDecimals = 2, verbose = TRUE)
queryResult |
Object returned by |
showNotAssignedSamples |
Logical. Shows the two most likely classes for the NotAssigned samples and the probabilities of belonging to each of them. |
numDecimals |
Integer. Number of decimals to show on the statistics. |
verbose |
Logical. If TRUE, messages indicating the execution progress will be printed on screen. |
Returns a list with the following fields:
callRate Count and percentage of assigned samples.
assigned Number of samples assigned to each class and mean and SD of the assignment probabilities.
notAssignedSamples Optional. Most likely classes for the Not Assigned samples.
Bioinformatics and Functional Genomics Group. Centro de Investigacion del Cancer (CIC-IBMCC, USAL-CSIC). Salamanca. Spain
Main package function and classifier training: geNetClassifier
Query the classifier: queryGeNetClassifier
########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples <- c(1:10, 13:22, 25:34, 37:46, 49:58) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## Classifier query ########################## # Select the samples to query the classifier # - Real use: samples whose class we want to known querySamples <- "GSM330154.CEL" # - External validation: samples not used for training querySamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,querySamples]) ########################## ## Query Summary ########################## # Obtain an overview of the results querySummary(queryResult)########################## ## Classifier training ########################## # Load an expressionSet: library(leukemiasEset) data(leukemiasEset) # Select the train samples: trainSamples <- c(1:10, 13:22, 25:34, 37:46, 49:58) # Train a classifier or load a trained one: # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], # sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") data(leukemiasClassifier) # Sample trained classifier ########################## ## Classifier query ########################## # Select the samples to query the classifier # - Real use: samples whose class we want to known querySamples <- "GSM330154.CEL" # - External validation: samples not used for training querySamples <- c(1:60)[-trainSamples] # Make a query to the classifier: queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,querySamples]) ########################## ## Query Summary ########################## # Obtain an overview of the results querySummary(queryResult)
Allows setting or modifiying the GenesRanking properties.
setProperties(object, geneLabels=NULL, discriminantPower=NULL, meanDif=NULL, isRedundant=NULL, gERankMean=NULL)
Main package function and classifier training:
geNetClassifier
This method's class (GenesRanking) help page.