| Title: | Clustering Algorithm for Data Integration and Disease Subtyping |
|---|---|
| Description: | Provides a robust approach for omics data integration and disease subtyping. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et al. (2017)<DOI: 10.1101/gr.215129.116>, Nguyen et al. (2021)<DOI: 10.3389/fonc.2021.725133>). |
| Authors: | Hung Nguyen, Bang Tran, Duc Tran and Tin Nguyen |
| Maintainer: | Van-Dung Pham <[email protected]> |
| License: | LGPL |
| Version: | 2.0.7 |
| Built: | 2024-11-02 04:42:57 UTC |
| Source: | https://github.com/cran/PINSPlus |
This package implements clustering algorithms proposed by Nguyen et al. (2017, 2019). Perturbation Clustering for data INtegration and disease Subtyping (PINS) is an approach for integraton of data and classification of diseases into various subtypes. PINS+ provides algorithms supporting both single data type clustering and multi-omics data type. PINSPlus is an improved version of PINS by allowing users to customize the based clustering algorithm and perturbation methods. Furthermore, PINSPlus is fast and supports the analysis or large datasets with millions of samples and features.
PINS+ provides PerturbationClustering and SubtypingOmicsData functions for single data type clustering and multi-omics data type clustering. PINS makes use of different clustering algorithms such as kmeans and pam to perform clustering actions. The principle of PINS is to find the optimum number of clusters and location of each sample in the clusters based on perturbation methods such as noise or subsampling. PINS+ allows users to pass their own clustering algorithm and perturbation method.
H Nguyen, S Shrestha, S Draghici, & T Nguyen. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 35(16), 2843-2846, (2019).
T Nguyen, R Tagett, D Diaz, S Draghici. A novel method for data integration and disease subtyping. Genome Research, 27(12):2025-2039, 2017.
Nguyen, H., Shrestha, S., Draghici, S., & Nguyen, T. (2019). PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 35(16), 2843-2846.
PerturbationClustering, SubtypingOmicsData
Acute myelogenous leukemia dataset
A list containing properties:
| Name | Type | Description |
| Gene | data.frame | mRNA expression data |
| Group | data.frame | Data frame indicating the cluster to which each sample is allocated |
https://www.pnas.org/doi/full/10.1073/pnas.0308531101
Brunet, J. P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004). Metagenes and molecular pattern discovery using matrix factorization. Proceedings of the national academy of sciences, 101(12), 4164-4169.
The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data collection is part of a larger effort to build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).
This embed version of KIRC in PINPlus package is the reduced version of KIRC using Principle Component Analysis.
A list containing properties:
| Name | Type | Description |
| GE | data.frame | mRNA expression data |
| ME | data.frame | DNA Methylation data |
| MI | data.frame | miRNA expression data |
| survival | data.frame | Clinical survival data |
https://portal.gdc.cancer.gov/projects/TCGA-KIRC
The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
Perform subtyping using one type of high-dimensional data
PerturbationClustering( data, kMin = 2, kMax = 5, k = NULL, verbose = T, ncore = 1, clusteringMethod = "kmeans", clusteringFunction = NULL, clusteringOptions = NULL, perturbMethod = "noise", perturbFunction = NULL, perturbOptions = NULL, PCAFunction = NULL, iterMin = 20, iterMax = 200, madMin = 0.001, msdMin = 1e-06, sampledSetSize = 2000, knn.k = NULL )PerturbationClustering( data, kMin = 2, kMax = 5, k = NULL, verbose = T, ncore = 1, clusteringMethod = "kmeans", clusteringFunction = NULL, clusteringOptions = NULL, perturbMethod = "noise", perturbFunction = NULL, perturbOptions = NULL, PCAFunction = NULL, iterMin = 20, iterMax = 200, madMin = 0.001, msdMin = 1e-06, sampledSetSize = 2000, knn.k = NULL )
data |
Input matrix. The rows represent items while the columns represent features. |
kMin |
The minimum number of clusters used for automatically detecting the number of clusters. Default value is |
kMax |
The maximum number of clusters used for automatically detecting the number of clusters. Default value is |
k |
The number of clusters. If k is set then kMin and kMax will be ignored. |
verbose |
Boolean value indicating the algorithm to run with or without logging. Default value is |
ncore |
Number of cores that the algorithm should use. Default value is |
clusteringMethod |
The name of built-in clustering algorithm that PerturbationClustering will use. Currently supported algorithm are |
clusteringFunction |
The clustering algorithm function that will be used instead of built-in algorithms. |
clusteringOptions |
A list of parameter will be passed to the clustering algorithm in |
perturbMethod |
The name of built-in perturbation method that PerturbationClustering will use, currently supported methods are |
perturbFunction |
The perturbation method function that will be used instead of built-in ones. |
perturbOptions |
A list of parameter will be passed to the perturbation method in |
PCAFunction |
The customized PCA function that user can manually define. |
iterMin |
The minimum number of iterations. Default value is |
iterMax |
The maximum number of iterations. Default value is |
madMin |
The minimum of Mean Absolute Deviation of |
msdMin |
The minimum of Mean Square Deviation of |
sampledSetSize |
The number of sample size used for the sampling process when dataset is big. Default value is |
knn.k |
The value of k of the k-nearest neighbors algorithm. If knn.k is not set then it will be used the elbow method to calculate k. |
PerturbationClustering implements the Perturbation Clustering algorithm of Nguyen et al. (2017), Nguyen et al. (2019), and Nguyen et al. (2021). It aims to determine the optimum cluster number and location of each sample in the clusters in an unsupervised analysis.
PerturbationClustering takes input as a numerical matrix or data frame of items as rows and features as columns.
It uses a clustering algorithm as the based algorithm.
Current built-in algorithms that users can use directly are kmeans, pam and hclust.
The default parameters for built-in kmeans are nstart = 20 and iter.max = 1000.
Users can change the parameters of built-in clustering algorithm by passing the value into clusteringOptions.
PerturbationClustering also allows users to pass their own clustering algorithm instead of using built-in ones by using clusteringFunction parameter.
Once clust?eringFunction is specified, clusteringMethod will be skipped.
The value of clusteringFunction must be a function that takes two arguments: data and k,
where data is a numeric matrix or data frame containing data that need to be clustered, and k is the number of clusters.
clusteringFunction must return a vector of labels indicating the cluster to which each sample is allocated.
PerturbationClustering uses a perturbation method to perturb clustering input data.
There are two built-in methods are noise and subsampling that users can use directly by passing to perturbMethod parameter.
Users can change the default value of built-in perturbation methods by passing new value into perturbOptions:
1. noise perturbation method takes two arguments: noise and noisePercent. The default values are noise = NULL and noisePercent = "median".
If noise is specified. noisePercent will be skipped.
2. subsampling perturbation method takes one argument percent which has default value of 80
Users can also use their own perturbation methods by passing them into perturbFunction.
Once perturbFunction is specified, perturbMethod will be skipped.
The value of perturbFunction must be a function that takes one argument data
- a numeric matrix or data frame containing data that need to be perturbed.
perturbFunction must return an object list which is as follows:
1. data: the perturbed data
2. ConnectivityMatrixHandler: a function that takes three arguments:
connectivityMatrix - the connectivity matrix generated after clustering returned data,
iter - the current iteration and k - the number of cluster.
This function must return a compatible connectivity matrix with the original connectivity matrix.
This function aims to correct the connectivityMatrix if needed and returns the corrected version of it.
3. MergeConnectivityMatrices: a function that takes four arguments: oldMatrix, newMatrix, k and iter.
The oldMatrix and newMatrix are two connectivity matrices that need to be merged,
k is the cluster number and iter is the current number of iteration.
This function must returns a connectivity matrix that is merged from oldMatrix and newMatrix
The parameters sampledSetSize and knn.k are used for subsampling procedure when clustering big data. Please consult Nguyen et al. (2021) for details.
PerturbationClustering returns a list with at least the following components:
k |
The optimal number of clusters |
cluster |
A vector of labels indicating the cluster to which each sample is allocated |
origS |
A list of original connectivity matrices |
pertS |
A list of perturbed connectivity matrices |
1. H Nguyen, S Shrestha, S Draghici, & T Nguyen. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 35(16), 2843-2846, (2019).
2. T Nguyen, R Tagett, D Diaz, S Draghici. A novel method for data integration and disease subtyping. Genome Research, 27(12):2025-2039, 2017.
3. T. Nguyen, "Horizontal and vertical integration of bio-molecular data", PhD thesis, Wayne State University, 2017.
4. H Nguyen, D Tran, B Tran, M Roy, A Cassell, S Dascalu, S Draghici & T Nguyen. SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis. Frontiers in oncology. 2021.
# Load the dataset AML2004 data(AML2004) data <- as.matrix(AML2004$Gene) # Perform the clustering result <- PerturbationClustering(data = data) # Plot the result condition = seq(unique(AML2004$Group[, 2])) names(condition) <- unique(AML2004$Group[, 2]) plot( prcomp(data)$x, col = result$cluster, pch = condition[AML2004$Group[, 2]], main = "AML2004" ) legend( "bottomright", legend = paste("Cluster ", sort(unique(result$cluster)), sep = ""), fill = sort(unique(result$cluster)) ) legend("bottomleft", legend = names(condition), pch = condition) # Change kmeans parameters result <- PerturbationClustering( data = data, clusteringMethod = "kmeans", clusteringOptions = list( iter.max = 500, nstart = 50 ) ) # Change to use pam result <- PerturbationClustering(data = data, clusteringMethod = "pam") # Change to use hclust result <- PerturbationClustering(data = data, clusteringMethod = "hclust") # Pass a user-defined clustering algorithm result <- PerturbationClustering(data = data, clusteringFunction = function(data, k){ # this function must return a vector of cluster kmeans(x = data, centers = k, nstart = k*10, iter.max = 2000)$cluster }) # Use noise as the perturb method result <- PerturbationClustering(data = data, perturbMethod = "noise", perturbOptions = list(noise = 0.3)) # or result <- PerturbationClustering(data = data, perturbMethod = "noise", perturbOptions = list(noisePercent = 10)) # Change to use subsampling result <- PerturbationClustering(data = data, perturbMethod = "subsampling", perturbOptions = list(percent = 90)) # Users can pass their own perturb method result <- PerturbationClustering(data = data, perturbFunction = function(data){ rowNum <- nrow(data) colNum <- ncol(data) epsilon <- matrix( data = rnorm(rowNum * colNum, mean = 0, sd = 1.234), nrow = rowNum, ncol = colNum ) list( data = data + epsilon, ConnectivityMatrixHandler = function(connectivityMatrix, ...) { connectivityMatrix }, MergeConnectivityMatrices = function(oldMatrix, newMatrix, iter, ...){ return((oldMatrix*(iter-1) + newMatrix)/iter) } ) }) # Clustering on simulation data # Load necessary library if (!require("mclust")) install.packages("mclust") library(mclust) library(irlba) #Generate a simulated data matrix with the size of 50,000 x 5,000 sampleNum <- 50000 # Number of samples geneNum <- 5000 # Number of genes subtypeNum <- 3 # Number of subtypes # Generate expression matrix exprs <- matrix(rnorm(sampleNum*geneNum, 0, 1), nrow = sampleNum, ncol = geneNum) rownames(exprs) <- paste0("S", 1:sampleNum) # Assign unique names for samples # Generate subtypes group <- sort(rep(1:subtypeNum, sampleNum/subtypeNum + 1)[1:sampleNum]) names(group) <- rownames(exprs) # Make subtypes separate for (i in 1:subtypeNum) { exprs[group == i, 1:100 + 100*(i-1)] <- exprs[group == i, 1:100 + 100*(i-1)] + 2 } # Plot the data library(irlba) exprs.pca <- irlba::prcomp_irlba(exprs, n = 2)$x plot(exprs.pca, main = "PCA") #Run PINSPlus clustering: set.seed(1) t1 <- Sys.time() result <- PerturbationClustering(data = exprs.pca, ncore = 1) t2 <- Sys.time() #Print out the running time: time<- t2-t1 #Print out the number of clusters: result$k #Get cluster assignment subtype <- result$cluster # Here we assess the clustering accurracy using Adjusted Rand Index (ARI). #ARI takes values from -1 to 1 where 0 stands for a random clustering and 1 #stands for a perfect partition result. if (!require("mclust")) install.packages("mclust") library(mclust) ari <- mclust::adjustedRandIndex(subtype, group) #Plot the cluster assginments colors <- as.numeric(as.character(factor(subtype))) plot(exprs.pca, col = colors, main = "Cluster assigments for simulation data") legend("topright", legend = paste("ARI:", ari)) legend("bottomright", fill = unique(colors), legend = paste("Group ", levels(factor(subtype)), ": ", table(subtype)[levels(factor(subtype))], sep = "" ) )# Load the dataset AML2004 data(AML2004) data <- as.matrix(AML2004$Gene) # Perform the clustering result <- PerturbationClustering(data = data) # Plot the result condition = seq(unique(AML2004$Group[, 2])) names(condition) <- unique(AML2004$Group[, 2]) plot( prcomp(data)$x, col = result$cluster, pch = condition[AML2004$Group[, 2]], main = "AML2004" ) legend( "bottomright", legend = paste("Cluster ", sort(unique(result$cluster)), sep = ""), fill = sort(unique(result$cluster)) ) legend("bottomleft", legend = names(condition), pch = condition) # Change kmeans parameters result <- PerturbationClustering( data = data, clusteringMethod = "kmeans", clusteringOptions = list( iter.max = 500, nstart = 50 ) ) # Change to use pam result <- PerturbationClustering(data = data, clusteringMethod = "pam") # Change to use hclust result <- PerturbationClustering(data = data, clusteringMethod = "hclust") # Pass a user-defined clustering algorithm result <- PerturbationClustering(data = data, clusteringFunction = function(data, k){ # this function must return a vector of cluster kmeans(x = data, centers = k, nstart = k*10, iter.max = 2000)$cluster }) # Use noise as the perturb method result <- PerturbationClustering(data = data, perturbMethod = "noise", perturbOptions = list(noise = 0.3)) # or result <- PerturbationClustering(data = data, perturbMethod = "noise", perturbOptions = list(noisePercent = 10)) # Change to use subsampling result <- PerturbationClustering(data = data, perturbMethod = "subsampling", perturbOptions = list(percent = 90)) # Users can pass their own perturb method result <- PerturbationClustering(data = data, perturbFunction = function(data){ rowNum <- nrow(data) colNum <- ncol(data) epsilon <- matrix( data = rnorm(rowNum * colNum, mean = 0, sd = 1.234), nrow = rowNum, ncol = colNum ) list( data = data + epsilon, ConnectivityMatrixHandler = function(connectivityMatrix, ...) { connectivityMatrix }, MergeConnectivityMatrices = function(oldMatrix, newMatrix, iter, ...){ return((oldMatrix*(iter-1) + newMatrix)/iter) } ) }) # Clustering on simulation data # Load necessary library if (!require("mclust")) install.packages("mclust") library(mclust) library(irlba) #Generate a simulated data matrix with the size of 50,000 x 5,000 sampleNum <- 50000 # Number of samples geneNum <- 5000 # Number of genes subtypeNum <- 3 # Number of subtypes # Generate expression matrix exprs <- matrix(rnorm(sampleNum*geneNum, 0, 1), nrow = sampleNum, ncol = geneNum) rownames(exprs) <- paste0("S", 1:sampleNum) # Assign unique names for samples # Generate subtypes group <- sort(rep(1:subtypeNum, sampleNum/subtypeNum + 1)[1:sampleNum]) names(group) <- rownames(exprs) # Make subtypes separate for (i in 1:subtypeNum) { exprs[group == i, 1:100 + 100*(i-1)] <- exprs[group == i, 1:100 + 100*(i-1)] + 2 } # Plot the data library(irlba) exprs.pca <- irlba::prcomp_irlba(exprs, n = 2)$x plot(exprs.pca, main = "PCA") #Run PINSPlus clustering: set.seed(1) t1 <- Sys.time() result <- PerturbationClustering(data = exprs.pca, ncore = 1) t2 <- Sys.time() #Print out the running time: time<- t2-t1 #Print out the number of clusters: result$k #Get cluster assignment subtype <- result$cluster # Here we assess the clustering accurracy using Adjusted Rand Index (ARI). #ARI takes values from -1 to 1 where 0 stands for a random clustering and 1 #stands for a perfect partition result. if (!require("mclust")) install.packages("mclust") library(mclust) ari <- mclust::adjustedRandIndex(subtype, group) #Plot the cluster assginments colors <- as.numeric(as.character(factor(subtype))) plot(exprs.pca, col = colors, main = "Cluster assigments for simulation data") legend("topright", legend = paste("ARI:", ari)) legend("bottomright", fill = unique(colors), legend = paste("Group ", levels(factor(subtype)), ": ", table(subtype)[levels(factor(subtype))], sep = "" ) )
Perform subtyping using multiple types of data
SubtypingOmicsData( dataList, kMin = 2, kMax = 5, k = NULL, agreementCutoff = 0.5, ncore = 1, verbose = T, sampledSetSize = 2000, knn.k = NULL, ... )SubtypingOmicsData( dataList, kMin = 2, kMax = 5, k = NULL, agreementCutoff = 0.5, ncore = 1, verbose = T, sampledSetSize = 2000, knn.k = NULL, ... )
dataList |
a list of data matrices. Each matrix represents a data type where the rows are items and the columns are features. The matrices must have the same set of items. |
kMin |
The minimum number of clusters used for automatically detecting the number of clusters in |
kMax |
The maximum number of clusters used for automatically detecting the number of clusters in |
k |
The number of clusters. If k is set then kMin and kMax will be ignored. |
agreementCutoff |
agreement threshold to be considered consistent. Default value is |
ncore |
Number of cores that the algorithm should use. Default value is |
verbose |
set it to |
sampledSetSize |
The number of sample size used for the sampling process when dataset is big. Default value is |
knn.k |
The value of k of the k-nearest neighbors algorithm. If knn.k is not set then it will be used elbow method to calculate the k. |
... |
these arguments will be passed to |
SubtypingOmicsData implements the Subtyping multi-omic data that are based on Perturbaion clustering algorithm of Nguyen et al (2017), Nguyen et al (2019) and Nguyen, et al. (2021).
The input is a list of data matrices where each matrix represents the molecular measurements of a data type. The input matrices must have the same number of rows.
SubtypingOmicsData aims to find the optimum number of subtypes and location of each sample in the clusters from integrated input data dataList through two processing stages:
1. Stage I: The algorithm first partitions each data type using the function PerturbationClustering.
It then merges the connectivities across data types into similarity matrices.
Both kmeans and similarity-based clustering algorithms - partitioning around medoids pam are used to partition the built similarity.
The algorithm returns the partitioning that agrees the most with individual data types.
2. Stage II: The algorithm attempts to split each discovered group if there is a strong agreement between data types,
or if the subtyping in Stage I is very unbalanced.
When clustering a large number of samples, this function uses a subsampling technique to reduce the computational complexity with the two parameters sampledSetSize and knn.k. Please consult Nguyen et al. (2021) for details.
SubtypingOmicsData returns a list with at least the following components:
cluster1 |
A vector of labels indicating the cluster to which each sample is allocated in Stage I |
cluster2 |
A vector of labels indicating the cluster to which each sample is allocated in Stage II |
dataTypeResult |
A list of results for individual data type. Each element of the list is the result of the |
1. H Nguyen, S Shrestha, S Draghici, & T Nguyen. PINSPlus: a tool for tumor subtype discovery in integrated genomic data. Bioinformatics, 35(16), 2843-2846, (2019).
2. T Nguyen, R Tagett, D Diaz, S Draghici. A novel method for data integration and disease subtyping. Genome Research, 27(12):2025-2039, 2017.
3. T. Nguyen, "Horizontal and vertical integration of bio-molecular data", PhD thesis, Wayne State University, 2017.
4. H Nguyen, D Tran, B Tran, M Roy, A Cassell, S Dascalu, S Draghici & T Nguyen. SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis. Frontiers in oncology. 2021.
# Load the kidney cancer carcinoma data data(KIRC) # Perform subtyping on the multi-omics data dataList <- list (as.matrix(KIRC$GE), as.matrix(KIRC$ME), as.matrix(KIRC$MI)) names(dataList) <- c("GE", "ME", "MI") result <- SubtypingOmicsData(dataList = dataList) # Change Pertubation clustering algorithm's arguments result <- SubtypingOmicsData( dataList = dataList, clusteringMethod = "kmeans", clusteringOptions = list(nstart = 50) ) # Plot the Kaplan-Meier curves and calculate Cox p-value library(survival) cluster1=result$cluster1;cluster2=result$cluster2 a <- intersect(unique(cluster2), unique(cluster1)) names(a) <- intersect(unique(cluster2), unique(cluster1)) a[setdiff(unique(cluster2), unique(cluster1))] <- seq(setdiff(unique(cluster2), unique(cluster1))) + max(cluster1) colors <- a[levels(factor(cluster2))] coxFit <- coxph( Surv(time = Survival, event = Death) ~ as.factor(cluster2), data = KIRC$survival, ties = "exact" ) mfit <- survfit(Surv(Survival, Death == 1) ~ as.factor(cluster2), data = KIRC$survival) plot( mfit, col = colors, main = "Survival curves for KIRC, level 2", xlab = "Days", ylab = "Survival",lwd = 2 ) legend("bottomright", legend = paste( "Cox p-value:", round(summary(coxFit)$sctest[3], digits = 5), sep = "" ) ) legend( "bottomleft", fill = colors, legend = paste( "Group ", levels(factor(cluster2)),": ", table(cluster2)[levels(factor(cluster2))], sep ="" ) )# Load the kidney cancer carcinoma data data(KIRC) # Perform subtyping on the multi-omics data dataList <- list (as.matrix(KIRC$GE), as.matrix(KIRC$ME), as.matrix(KIRC$MI)) names(dataList) <- c("GE", "ME", "MI") result <- SubtypingOmicsData(dataList = dataList) # Change Pertubation clustering algorithm's arguments result <- SubtypingOmicsData( dataList = dataList, clusteringMethod = "kmeans", clusteringOptions = list(nstart = 50) ) # Plot the Kaplan-Meier curves and calculate Cox p-value library(survival) cluster1=result$cluster1;cluster2=result$cluster2 a <- intersect(unique(cluster2), unique(cluster1)) names(a) <- intersect(unique(cluster2), unique(cluster1)) a[setdiff(unique(cluster2), unique(cluster1))] <- seq(setdiff(unique(cluster2), unique(cluster1))) + max(cluster1) colors <- a[levels(factor(cluster2))] coxFit <- coxph( Surv(time = Survival, event = Death) ~ as.factor(cluster2), data = KIRC$survival, ties = "exact" ) mfit <- survfit(Surv(Survival, Death == 1) ~ as.factor(cluster2), data = KIRC$survival) plot( mfit, col = colors, main = "Survival curves for KIRC, level 2", xlab = "Days", ylab = "Survival",lwd = 2 ) legend("bottomright", legend = paste( "Cox p-value:", round(summary(coxFit)$sctest[3], digits = 5), sep = "" ) ) legend( "bottomleft", fill = colors, legend = paste( "Group ", levels(factor(cluster2)),": ", table(cluster2)[levels(factor(cluster2))], sep ="" ) )