| Title: | Gene-Ranking Analysis of Pathway Expression |
|---|---|
| Description: | Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing the consensus behavior of biological pathways in the form of a template, and for quantifying the extent to which individual samples deviate from the template. GRAPE templates are based only on the relative rankings of the genes within the pathway and can be used for classification of tissue types or disease subtypes. GRAPE can be used to represent gene-expression samples as vectors of pathway scores, where each pathway score indicates the departure from a given collection of reference samples. The resulting pathway- space representation can be used as the feature set for various applications, including survival analysis and drug-response prediction. Users of GRAPE should use the following citation: Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics, 18:317 (June 2017). |
| Authors: | Michael Klein <[email protected]> |
| Maintainer: | Michael Klein <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.1 |
| Built: | 2025-01-25 05:03:52 UTC |
| Source: | https://github.com/cran/GRAPE |
Calculate pathway scores of a single pathway of a set of samples relative to a reference set of samples
getPathwayScores(refmat, newmat, w = w_quad)getPathwayScores(refmat, newmat, w = w_quad)
refmat |
Pathway expression matrix of reference samples. Rows are genes, columns are samples. |
newmat |
Pathway expression matrix of new samples. Rows are genes, columns are samples. |
w |
Weight function. Default is quadratic weight function. |
Vector of pathway scores of each sample in newmat.
## Toy example: 50 reference samples set.seed(10); refmat <- matrix(rnorm(5*50),nrow=5,ncol=50); rownames(refmat) <- paste0("g",1:5) ### make g2 and g5 larger in refmat refmat[2,] <- rnorm(50,3,2); refmat[5,] <- rnorm(50,4,4) ### 15 new samples newmat <- matrix(rnorm(5*15),nrow=5,ncol=15); rownames(newmat) <- paste0("g",1:5) ### make g2 and g3 larger in newmat newmat[2,] <- rnorm(15,2,3); newmat[3,] <- rnorm(15,4,3) ps_new <- getPathwayScores(refmat,newmat) ### get pathway scores of new samples ps_ref <- getPathwayScores(refmat,refmat) ### get pathway scores of reference samples ps_both <- getPathwayScores(refmat,cbind(refmat,newmat)) ### get pathway scores of both # > ps_new # [1] 6.2720 8.5696 9.9904 6.9056 3.7824 8.9344 13.0880 10.2912 3.7824 # 0.0384 13.1136 6.8032 4.8512 12.8512 10.2912## Toy example: 50 reference samples set.seed(10); refmat <- matrix(rnorm(5*50),nrow=5,ncol=50); rownames(refmat) <- paste0("g",1:5) ### make g2 and g5 larger in refmat refmat[2,] <- rnorm(50,3,2); refmat[5,] <- rnorm(50,4,4) ### 15 new samples newmat <- matrix(rnorm(5*15),nrow=5,ncol=15); rownames(newmat) <- paste0("g",1:5) ### make g2 and g3 larger in newmat newmat[2,] <- rnorm(15,2,3); newmat[3,] <- rnorm(15,4,3) ps_new <- getPathwayScores(refmat,newmat) ### get pathway scores of new samples ps_ref <- getPathwayScores(refmat,refmat) ### get pathway scores of reference samples ps_both <- getPathwayScores(refmat,cbind(refmat,newmat)) ### get pathway scores of both # > ps_new # [1] 6.2720 8.5696 9.9904 6.9056 3.7824 8.9344 13.0880 10.2912 3.7824 # 0.0384 13.1136 6.8032 4.8512 12.8512 10.2912
Takes in matrix, where columns are samples and rows are pathway genes, outputs the binary and probability templates
makeBinaryTemplateAndProbabilityTemplate(submat)makeBinaryTemplateAndProbabilityTemplate(submat)
submat |
A matrix where columns are samples and rows are pathway genes |
List containing binary template vector and probability template vector
submat <- cbind(c(1,3,2,1.5),c(2,3,1.5,1.2),c(1.4,4.2,3.5,3.8)) rownames(submat) <- c("gene_A","gene_B","gene_C","gene_D") temp <- makeBinaryTemplateAndProbabilityTemplate(submat) bt <- temp$binary_template; pt <- temp$probability_template cbind(bt,pt)submat <- cbind(c(1,3,2,1.5),c(2,3,1.5,1.2),c(1.4,4.2,3.5,3.8)) rownames(submat) <- c("gene_A","gene_B","gene_C","gene_D") temp <- makeBinaryTemplateAndProbabilityTemplate(submat) bt <- temp$binary_template; pt <- temp$probability_template cbind(bt,pt)
Represents new samples as vectors of pathway scores relative to reference samples
makeGRAPE_psMat(refge, newge, pathway_list, w = w_quad)makeGRAPE_psMat(refge, newge, pathway_list, w = w_quad)
refge |
Gene expression matrix of reference samples. Rows are genes, columns are samples. |
newge |
Gene expression matrix of new samples. Rows are genes, columns are samples. |
pathway_list |
List of pathways. Each pathway is a character vector consisting of gene names. |
w |
Weight function. Default is quadratic weight function. |
Vector of pathway scores of each sample in newmat.
#' ### Make pathway scores mat set.seed(10) ### 50 reference samples refge <- matrix(rnorm(10*50),nrow=10,ncol=50); rownames(refge) <- paste0("g",1:10) refge[c(2,5,8),] <- matrix(rnorm(3*50,mean=2,sd=2)) refge[c(3,4,7),] <- matrix(rnorm(3*50,mean=4,sd=4)) ### 6 new samples newge <- matrix(rnorm(10*6),nrow=10,ncol=6); rownames(newge) <- paste0("g",1:10) newge[c(2:7),] <- matrix(rnorm(6*6,mean=3,sd=1)) newge[c(1,9),] <- matrix(rnorm(2*6,mean=5,sd=3)) pathway_list <- list(set1=paste0("g",1:4),set2=paste0("g",5:10),set3=paste0("g",c(1,4,8:10))) psmat <- makeGRAPE_psMat(refge,newge,pathway_list) # > psmat # [,1] [,2] [,3] [,4] [,5] [,6] # set1 2.397426 1.406275 2.516492 2.358809 2.555109 2.358809 # set2 0.670354 3.245575 3.962389 2.670354 1.741150 1.579646 # set3 1.536017 2.167373 2.167373 2.167373 2.148305 1.809322#' ### Make pathway scores mat set.seed(10) ### 50 reference samples refge <- matrix(rnorm(10*50),nrow=10,ncol=50); rownames(refge) <- paste0("g",1:10) refge[c(2,5,8),] <- matrix(rnorm(3*50,mean=2,sd=2)) refge[c(3,4,7),] <- matrix(rnorm(3*50,mean=4,sd=4)) ### 6 new samples newge <- matrix(rnorm(10*6),nrow=10,ncol=6); rownames(newge) <- paste0("g",1:10) newge[c(2:7),] <- matrix(rnorm(6*6,mean=3,sd=1)) newge[c(1,9),] <- matrix(rnorm(2*6,mean=5,sd=3)) pathway_list <- list(set1=paste0("g",1:4),set2=paste0("g",5:10),set3=paste0("g",c(1,4,8:10))) psmat <- makeGRAPE_psMat(refge,newge,pathway_list) # > psmat # [,1] [,2] [,3] [,4] [,5] [,6] # set1 2.397426 1.406275 2.516492 2.358809 2.555109 2.358809 # set2 0.670354 3.245575 3.962389 2.670354 1.741150 1.579646 # set3 1.536017 2.167373 2.167373 2.167373 2.148305 1.809322
Takes in a vector of gene expression values and returns a binary vector consisting of the pairwise rankings for the sample
makePairwiseOrder(samp)makePairwiseOrder(samp)
samp |
A vector of gene expression values |
Binary vector of the pairwise ranking representation of the samples
samp <- c(1,3,2,1.5) makePairwiseOrder(samp)samp <- c(1,3,2,1.5) makePairwiseOrder(samp)
Takes in vector of pathway gene names, returns names corresponding to the pairwise binary representation
makePairwiseOrderNames(path_genes)makePairwiseOrderNames(path_genes)
path_genes |
A vector of pathway gene names |
Names for the pairwise representation, of the form "gA<gB"
path_genes <- c("gene_A","gene_B","gene_C","gene_D") makePairwiseOrderNames(path_genes)path_genes <- c("gene_A","gene_B","gene_C","gene_D") makePairwiseOrderNames(path_genes)
Classification of a samples according to dirac distances from templates. Usually applied to the gene expression values for a single pathway.
predictClassDIRAC(trainmat, testmat, train_labels)predictClassDIRAC(trainmat, testmat, train_labels)
trainmat |
Matrix of gene expression for set of genes accross training set samples. Each column is a sample. |
testmat |
Matrix of gene expression for set of genes accross test set samples. Each column is a sample. |
train_labels |
Vector of class labels for each sample in the training set. |
Predicted class labels for test set
# Toy example of two classes set.seed(10); path_genes <- c("gA","gB","gC","gD"); nsamps <- 50 # Four genes, 50 samples per class class_one_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 1 rownames(class_one_samps) <- path_genes class_one_samps[1,] <- rnorm(ncol(class_one_samps),4,2) class_one_samps[2,] <- rnorm(ncol(class_one_samps),5,4) class_one_samps[3,] <- rnorm(ncol(class_one_samps),1,1) class_one_samps[4,] <- rnorm(ncol(class_one_samps),2,1) class_two_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 2 rownames(class_two_samps) <- path_genes class_two_samps[1,] <- rnorm(ncol(class_two_samps),2,3) class_two_samps[2,] <- rnorm(ncol(class_two_samps),5,2) class_two_samps[3,] <- rnorm(ncol(class_two_samps),1,1) class_two_samps[4,] <- rnorm(ncol(class_two_samps),0,1) all_samps <- cbind(class_one_samps,class_two_samps) labels <- c(rep(1,nsamps),rep(2,nsamps)) testid <- sample.int(100,20) trainmat <- all_samps[,-testid] train_labels <- labels[-testid] testmat <- all_samps[,testid] test_labels <- labels[testid] yhat <- predictClassDIRAC(trainmat,testmat,train_labels) sum(diag(table(test_labels,yhat)))/length(test_labels) # accuracy # [1] 0.7# Toy example of two classes set.seed(10); path_genes <- c("gA","gB","gC","gD"); nsamps <- 50 # Four genes, 50 samples per class class_one_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 1 rownames(class_one_samps) <- path_genes class_one_samps[1,] <- rnorm(ncol(class_one_samps),4,2) class_one_samps[2,] <- rnorm(ncol(class_one_samps),5,4) class_one_samps[3,] <- rnorm(ncol(class_one_samps),1,1) class_one_samps[4,] <- rnorm(ncol(class_one_samps),2,1) class_two_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 2 rownames(class_two_samps) <- path_genes class_two_samps[1,] <- rnorm(ncol(class_two_samps),2,3) class_two_samps[2,] <- rnorm(ncol(class_two_samps),5,2) class_two_samps[3,] <- rnorm(ncol(class_two_samps),1,1) class_two_samps[4,] <- rnorm(ncol(class_two_samps),0,1) all_samps <- cbind(class_one_samps,class_two_samps) labels <- c(rep(1,nsamps),rep(2,nsamps)) testid <- sample.int(100,20) trainmat <- all_samps[,-testid] train_labels <- labels[-testid] testmat <- all_samps[,testid] test_labels <- labels[testid] yhat <- predictClassDIRAC(trainmat,testmat,train_labels) sum(diag(table(test_labels,yhat)))/length(test_labels) # accuracy # [1] 0.7
Classification of a samples according to grape distances from templates. Usually applied to the gene expression values for a single pathway.
predictClassGRAPE(trainmat, testmat, train_labels, w = w_quad)predictClassGRAPE(trainmat, testmat, train_labels, w = w_quad)
trainmat |
Matrix of gene expression for set of genes accross training set samples. Each column is a sample. |
testmat |
Matrix of gene expression for set of genes accross test set samples. Each column is a sample. |
train_labels |
Vector of class labels for each sample in the training set. |
w |
Weight function. Default is quadratic weight function. |
Predicted class labels for test set
# Toy example of two classes set.seed(10); path_genes <- c("gA","gB","gC","gD"); nsamps <- 50 # Four genes, 50 samples per class class_one_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 1 rownames(class_one_samps) <- path_genes class_one_samps[1,] <- rnorm(ncol(class_one_samps),4,2) class_one_samps[2,] <- rnorm(ncol(class_one_samps),5,4) class_one_samps[3,] <- rnorm(ncol(class_one_samps),1,1) class_one_samps[4,] <- rnorm(ncol(class_one_samps),2,1) class_two_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 2 rownames(class_two_samps) <- path_genes class_two_samps[1,] <- rnorm(ncol(class_two_samps),2,3) class_two_samps[2,] <- rnorm(ncol(class_two_samps),5,2) class_two_samps[3,] <- rnorm(ncol(class_two_samps),1,1) class_two_samps[4,] <- rnorm(ncol(class_two_samps),0,1) all_samps <- cbind(class_one_samps,class_two_samps) labels <- c(rep(1,nsamps),rep(2,nsamps)) testid <- sample.int(100,20) trainmat <- all_samps[,-testid] train_labels <- labels[-testid] testmat <- all_samps[,testid] test_labels <- labels[testid] yhat <- predictClassGRAPE(trainmat,testmat,train_labels,w_quad) sum(diag(table(test_labels,yhat)))/length(test_labels) # accuracy # [1] 0.8# Toy example of two classes set.seed(10); path_genes <- c("gA","gB","gC","gD"); nsamps <- 50 # Four genes, 50 samples per class class_one_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 1 rownames(class_one_samps) <- path_genes class_one_samps[1,] <- rnorm(ncol(class_one_samps),4,2) class_one_samps[2,] <- rnorm(ncol(class_one_samps),5,4) class_one_samps[3,] <- rnorm(ncol(class_one_samps),1,1) class_one_samps[4,] <- rnorm(ncol(class_one_samps),2,1) class_two_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 2 rownames(class_two_samps) <- path_genes class_two_samps[1,] <- rnorm(ncol(class_two_samps),2,3) class_two_samps[2,] <- rnorm(ncol(class_two_samps),5,2) class_two_samps[3,] <- rnorm(ncol(class_two_samps),1,1) class_two_samps[4,] <- rnorm(ncol(class_two_samps),0,1) all_samps <- cbind(class_one_samps,class_two_samps) labels <- c(rep(1,nsamps),rep(2,nsamps)) testid <- sample.int(100,20) trainmat <- all_samps[,-testid] train_labels <- labels[-testid] testmat <- all_samps[,testid] test_labels <- labels[testid] yhat <- predictClassGRAPE(trainmat,testmat,train_labels,w_quad) sum(diag(table(test_labels,yhat)))/length(test_labels) # accuracy # [1] 0.8
Classification of a samples according to euclidean distances from PC templates. Usually applied to the gene expression values for a single pathway.
predictClassPC(trainmat, testmat, train_labels)predictClassPC(trainmat, testmat, train_labels)
trainmat |
Matrix of gene expression for set of genes accross training set samples. Each column is a sample. |
testmat |
Matrix of gene expression for set of genes accross test set samples. Each column is a sample. |
train_labels |
Vector of class labels for each sample in the training set. |
Predicted class labels for test set
# Toy example of two classes set.seed(10); path_genes <- c("gA","gB","gC","gD"); nsamps <- 50 # Four genes, 50 samples per class class_one_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 1 rownames(class_one_samps) <- path_genes class_one_samps[1,] <- rnorm(ncol(class_one_samps),4,2) class_one_samps[2,] <- rnorm(ncol(class_one_samps),5,4) class_one_samps[3,] <- rnorm(ncol(class_one_samps),1,1) class_one_samps[4,] <- rnorm(ncol(class_one_samps),2,1) class_two_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 2 rownames(class_two_samps) <- path_genes class_two_samps[1,] <- rnorm(ncol(class_two_samps),2,3) class_two_samps[2,] <- rnorm(ncol(class_two_samps),5,2) class_two_samps[3,] <- rnorm(ncol(class_two_samps),1,1) class_two_samps[4,] <- rnorm(ncol(class_two_samps),0,1) all_samps <- cbind(class_one_samps,class_two_samps) labels <- c(rep(1,nsamps),rep(2,nsamps)) testid <- sample.int(100,20) trainmat <- all_samps[,-testid] train_labels <- labels[-testid] testmat <- all_samps[,testid] test_labels <- labels[testid] yhat <- predictClassPC(trainmat,testmat,train_labels) sum(diag(table(test_labels,yhat)))/length(test_labels) # accuracy # [1] 0.55# Toy example of two classes set.seed(10); path_genes <- c("gA","gB","gC","gD"); nsamps <- 50 # Four genes, 50 samples per class class_one_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 1 rownames(class_one_samps) <- path_genes class_one_samps[1,] <- rnorm(ncol(class_one_samps),4,2) class_one_samps[2,] <- rnorm(ncol(class_one_samps),5,4) class_one_samps[3,] <- rnorm(ncol(class_one_samps),1,1) class_one_samps[4,] <- rnorm(ncol(class_one_samps),2,1) class_two_samps <- matrix(NA,nrow=length(path_genes),ncol=nsamps) # Class 2 rownames(class_two_samps) <- path_genes class_two_samps[1,] <- rnorm(ncol(class_two_samps),2,3) class_two_samps[2,] <- rnorm(ncol(class_two_samps),5,2) class_two_samps[3,] <- rnorm(ncol(class_two_samps),1,1) class_two_samps[4,] <- rnorm(ncol(class_two_samps),0,1) all_samps <- cbind(class_one_samps,class_two_samps) labels <- c(rep(1,nsamps),rep(2,nsamps)) testid <- sample.int(100,20) trainmat <- all_samps[,-testid] train_labels <- labels[-testid] testmat <- all_samps[,testid] test_labels <- labels[testid] yhat <- predictClassPC(trainmat,testmat,train_labels) sum(diag(table(test_labels,yhat)))/length(test_labels) # accuracy # [1] 0.55
Calculates the weights of all input entries. All entries should take values in [0,1].
w_quad(x)w_quad(x)
x |
Any number, vector of matrix. |
Weight of each element
w_quad(0.95) w_quad(cbind(c(.7,.8),c(.9,.1)))w_quad(0.95) w_quad(cbind(c(.7,.8),c(.9,.1)))