| Title: | Integrative Clustering of Multiple Genomic Dataset |
|---|---|
| Description: | Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization. |
| Authors: | Prabhakar Chalise, Rama Raghavan, Brooke Fridley |
| Maintainer: | Prabhakar Chalise <[email protected]> |
| License: | GPL-2 |
| Version: | 1.2.0 |
| Built: | 2024-11-20 06:40:58 UTC |
| Source: | CRAN |
Given the true clustering assignment for the subjects, this function calculates cluster entropy index comparing with clustering assignment determined by integrative NMF algorithm. Smaller value of cluster entropy indicates better cluster predictive discrimination.
ClusterEntropy(ComputedClusters, TrueClasses)ClusterEntropy(ComputedClusters, TrueClasses)
ComputedClusters |
Clustering assignment determined by integrative NMF algorithm |
TrueClasses |
True clustering assignment of the subjects |
Cluster entropy index value
Prabhakar Chalise, Rama Raghavan, Brooke Fridley
Kim Hyunsoo and Park Haesun (2007). Sparse non-negative matrix factorization via alternating non-negativity constrained least squares for microarray data analysis. Bioinformatics, 23: 1495-1502.
prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ClusterEntropy(ComputedClusters=fit$clusters, TrueClasses=true.cluster.assignment$cluster.id)prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ClusterEntropy(ComputedClusters=fit$clusters, TrueClasses=true.cluster.assignment$cluster.id)
Given the true clustering assignment for the subjects, this function calculates cluster purity index comparing with clustering assignment determined by integrative NMF algorithm. Higher value of cluster purity indicates better cluster predictive discrimination.
ClusterPurity(ComputedClusters, TrueClasses)ClusterPurity(ComputedClusters, TrueClasses)
ComputedClusters |
Clustering assignment determined by integrative NMF algorithm |
TrueClasses |
True clustering assignment of the subjects |
Cluster purity index value
Prabhakar Chalise, Rama Raghavan, Brooke Fridley
Kim Hyunsoo and Park Haesun (2007). Sparse non-negative matrix factorization via alternating non-negativity constrained least squares for microarray data analysis. Bioinformatics, 23: 1495-1502.
prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ClusterPurity(ComputedClusters=fit$clusters, TrueClasses=true.cluster.assignment$cluster.id)prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ClusterPurity(ComputedClusters=fit$clusters, TrueClasses=true.cluster.assignment$cluster.id)
Given the integrative NMF fit object, the function creates image plot of the consensus matrix ordered according to clusters groups. Cleaner block structure indicates stronger clusters.
ConsensusMatPlot(fit, rowLab = TRUE, colLab = TRUE)ConsensusMatPlot(fit, rowLab = TRUE, colLab = TRUE)
fit |
A nmf.mnnals fit object |
rowLab |
If true row label is displayed. Default is TRUE. |
colLab |
If true column label is displayed. Default is TRUE. |
Image plot of the consensus matrix ordered according to cluster groups is returned.
Prabhakar Chalise, Rama Raghavan, Brooke Fridley
Brunnet J, Tamayo P Golub, T and Mesirov J (2004) Metagene and molecular pattern discovery using matrix factorization. PNAS, 101, 4164-4169
Monti S, Tamayo P, Mesirov J and Golup T (2003). Consesus Clustering: A resampling based method for class discovery and visualization of gene expression microarray data. Machine Learning J, 52:91-118.
prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ConsensusMatPlot(fit,rowLab=TRUE,colLab=TRUE)prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) ConsensusMatPlot(fit,rowLab=TRUE,colLab=TRUE)
Given a single or multiple types of datasets (e.g. DNA methylation, mRNA expression, protein expression, DNA copy number) measured on same set of samples and pre-defined number of clusters, the function carries out clustering of the samples together with cluster membership assignment to the samples utilizing all the data set in a single comprehensive step.
nmf.mnnals(dat = dat, k = k, maxiter = 200, st.count = 20, n.ini = 30, ini.nndsvd = TRUE, seed = TRUE,wt=if(is.list(dat)) rep(1,length(dat)) else 1)nmf.mnnals(dat = dat, k = k, maxiter = 200, st.count = 20, n.ini = 30, ini.nndsvd = TRUE, seed = TRUE,wt=if(is.list(dat)) rep(1,length(dat)) else 1)
dat |
A single data or a list of multiple data matrices measured on same set of samples. For each data matrix in the list, samples should be on rows and genomic features should be on columns. |
k |
Number of clusters |
maxiter |
Maximum number of iteration, default is 200. |
st.count |
Count for stability in connectivity matrix, default is 20. |
n.ini |
Number of initializations of the random matrices, default is 30. |
ini.nndsvd |
Initialization of the Hi matrices using non negative double singular value decomposition (NNDSVD). If true, one of the initializations of algorithm will use NNDSVD. Default is TRUE. |
seed |
Random seed for initialization of algorithm, default is TRUE |
wt |
Weight, default is 1 for each data. |
consensus |
Consensus matrix |
W |
Common basis matrix across the multiple data sets |
H |
List of data specific coefficient matrices. |
convergence |
Matrix with five columns and number of rows equal to number of iterative steps required to converge the algorithm or number of maximum iteration. The five columns represent number of iterations, count for stability in connectivity matrix, stability indicator (1/0), absolute difference in reconstruction error between ith and (i-1)th iteration and value of the objective function respectively. |
min.f.WH |
Collection of values of objective function at convergence for each initialization of the algorithm. |
clusters |
Cluster membership assignment to samples. |
Prabhakar Chalise, Rama Raghavan, Brooke Fridley
Chalise P and Fridley B (2017). Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm. PLOS ONE, 12(5), e0176278.
Chalise P, Raghavan R and Fridley B (2016). InterSIM: Simulation tool for multiple integrative 'omic datasets. Computer Methods and Programs in Biomedicine, 128:69-74.
#### Simulation of three interrelated dataset #prop <- c(0.65,0.35) #prop <- c(0.30,0.40,0.30) prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 library(InterSIM) sim.D <- InterSIM(n.sample=100, cluster.sample.prop=prop, delta.methyl=effect, delta.expr=effect, delta.protein=effect, p.DMP=0.25, p.DEG=NULL, p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat1[1:5,1:5] dat2[1:5,1:5] dat3[1:5,1:5] dat <- list(dat1,dat2,dat3) # Find optimum number of clusters for the data #opt.k <- nmf.opt.k(dat=dat, n.runs=5, n.fold=5, k.range=2:7, result=TRUE, #make.plot=TRUE, progress=TRUE) # Find clustering assignment for the samples fit <- nmf.mnnals(dat=dat, k=length(prop), maxiter=200, st.count=20, n.ini=15, ini.nndsvd=TRUE, seed=TRUE) table(fit$clusters) fit$clusters[1:10]#### Simulation of three interrelated dataset #prop <- c(0.65,0.35) #prop <- c(0.30,0.40,0.30) prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 library(InterSIM) sim.D <- InterSIM(n.sample=100, cluster.sample.prop=prop, delta.methyl=effect, delta.expr=effect, delta.protein=effect, p.DMP=0.25, p.DEG=NULL, p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat1[1:5,1:5] dat2[1:5,1:5] dat3[1:5,1:5] dat <- list(dat1,dat2,dat3) # Find optimum number of clusters for the data #opt.k <- nmf.opt.k(dat=dat, n.runs=5, n.fold=5, k.range=2:7, result=TRUE, #make.plot=TRUE, progress=TRUE) # Find clustering assignment for the samples fit <- nmf.mnnals(dat=dat, k=length(prop), maxiter=200, st.count=20, n.ini=15, ini.nndsvd=TRUE, seed=TRUE) table(fit$clusters) fit$clusters[1:10]
Given a single or multiple types of datasets (e.g. DNA methylation, mRNA expression, protein expression, DNA copy number) measured on same set of samples, the function finds optimum number of clusters for the data or dataset.
nmf.opt.k(dat = dat, n.runs = 30, n.fold = 5, k.range = 2:8, result = TRUE, make.plot = TRUE, progress = TRUE, st.count = 10, maxiter = 100, wt=if(is.list(dat)) rep(1,length(dat)) else 1)nmf.opt.k(dat = dat, n.runs = 30, n.fold = 5, k.range = 2:8, result = TRUE, make.plot = TRUE, progress = TRUE, st.count = 10, maxiter = 100, wt=if(is.list(dat)) rep(1,length(dat)) else 1)
dat |
A single data or list of multiple types of data set measured on same set of samples. For each data matrix in the list, samples should be on rows and genomic features should be on columns. |
n.runs |
Number of runs of algorithm in order to find optimum number of clusters, default is 30. |
n.fold |
Number of folds for k-fold cross-validation, default is 5. |
k.range |
Search range for optimum number of clusters, default is 2:8 |
result |
Logical, to display the result-matrix, default is TRUE. |
make.plot |
Logical, to display the plot of cluster prediction index vs search range of clusters, default is TRUE |
progress |
Logical, to display the progress (in percentage) of the algorithm, default is TRUE |
st.count |
Count for stability in connectivity matrix, default is 10. |
maxiter |
Maximum number of iteration, default is 100. |
wt |
Weight, default is 1 for each data. |
The function returns a matrix of cluster prediction index (CPI) values for each run (columns) over the search range of number of clusters (rows). The function also generates plot of CPI over the search range of number of clusters.
Prabhakar Chalise, Rama Raghavan, Brooke Fridley
Chalise P and Fridley B (2017). Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm. PLOS ONE, 12(5), e0176278.
Chalise P, Raghavan R and Fridley B (2016). InterSIM: Simulation tool for multiple integrative 'omic datasets. Computer Methods and Programs in Biomedicine, 128:69-74.
#### Simulation of three interrelated dataset #prop <- c(0.65,0.35) #prop <- c(0.30,0.40,0.30) prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 library(InterSIM) sim.D <- InterSIM(n.sample=100, cluster.sample.prop=prop, delta.methyl=effect, delta.expr=effect, delta.protein=effect, p.DMP=0.25, p.DEG=NULL, p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat1[1:5,1:5] dat2[1:5,1:5] dat3[1:5,1:5] dat <- list(dat1,dat2,dat3) # Find optimum number of clusters for the data #opt.k <- nmf.opt.k(dat=dat, n.runs=5, n.fold=5, k.range=2:7, result=TRUE, #make.plot=TRUE,progress=TRUE)#### Simulation of three interrelated dataset #prop <- c(0.65,0.35) #prop <- c(0.30,0.40,0.30) prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 library(InterSIM) sim.D <- InterSIM(n.sample=100, cluster.sample.prop=prop, delta.methyl=effect, delta.expr=effect, delta.protein=effect, p.DMP=0.25, p.DEG=NULL, p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat1[1:5,1:5] dat2[1:5,1:5] dat3[1:5,1:5] dat <- list(dat1,dat2,dat3) # Find optimum number of clusters for the data #opt.k <- nmf.opt.k(dat=dat, n.runs=5, n.fold=5, k.range=2:7, result=TRUE, #make.plot=TRUE,progress=TRUE)
Given the integrative NMF fit object, the function creates silhouette width plot with different colors for different cluster groups.
SilhouettePlot(fit, cluster.col = NULL)SilhouettePlot(fit, cluster.col = NULL)
fit |
A nmf.mnnals fit object |
cluster.col |
Colors for the cluster groups. If NULL, default colors are used. |
Silhouette width plot is returned together with mean silhouette width for each group, overall silhouette width and summary statistics.
Prabhakar Chalise, Rama Raghavan, Brooke Fridley
Rousseeue PJ (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53-65
prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) SilhouettePlot(fit,cluster.col=NULL)prop <- c(0.20,0.30,0.27,0.23) effect <- 2.5 sim.D <- InterSIM(n.sample=100,cluster.sample.prop=prop,delta.methyl=effect, delta.expr=effect,delta.protein=effect,p.DMP=0.25,p.DEG=NULL,p.DEP=NULL, do.plot=FALSE, sample.cluster=TRUE, feature.cluster=TRUE) dat1 <- sim.D$dat.methyl dat2 <- sim.D$dat.expr dat3 <- sim.D$dat.protein true.cluster.assignment <- sim.D$clustering.assignment ## Make all data positive by shifting to positive direction. ## Also rescale the datasets so that they are comparable. if (!all(dat1>=0)) dat1 <- pmax(dat1 + abs(min(dat1)), .Machine$double.eps) dat1 <- dat1/max(dat1) if (!all(dat2>=0)) dat2 <- pmax(dat2 + abs(min(dat2)), .Machine$double.eps) dat2 <- dat2/max(dat2) if (!all(dat3>=0)) dat3 <- pmax(dat3 + abs(min(dat3)), .Machine$double.eps) dat3 <- dat3/max(dat3) # The function nmf.mnnals requires the samples to be on rows and variables on columns. dat <- list(dat1,dat2,dat3) fit <- nmf.mnnals(dat=dat,k=length(prop),maxiter=200,st.count=20,n.ini=15,ini.nndsvd=TRUE, seed=TRUE) SilhouettePlot(fit,cluster.col=NULL)