| Title: | Order Restricted Clustering for Microarray Experiments |
|---|---|
| Description: | Provides clustering of genes with similar dose response (or time course) profiles. It implements the method described by Lin et al. (2012). |
| Authors: | Adetayo Kasim, Martin Otava, Tobias Verbeke |
| Maintainer: | Rudradev Sengupta <[email protected]> |
| License: | GPL-3 |
| Version: | 2.0.2 |
| Built: | 2024-12-04 07:09:42 UTC |
| Source: | CRAN |
Dose data; a vector of length 12 with 3 observations for each of 4 doses.
data(doseData)data(doseData)
The format is: num [1:12] 1 1 1 2 2 2 3 3 ...
data(doseData) doseDatadata(doseData) doseData
This dose-response microarray data contains 1000 genes and 4 doses (one control dose (zero dose) and three increasing dose) with 3 arrays at each dose level. Due to confidetiality, it is only part of the real data set.
data(geneData)data(geneData)
A data frame with 1000 observations on the following 12 variables.
X1Sample one with zero dose
X1.1Sample two with zero dose
X1.2Sample three with zero dose
X2Sample one with second dose
X2.1Sample two with second dose
X2.2Sample three with second dose
X3Sample one with third dose
X3.1Sample two with third dose
X3.2Sample three with third dose
X4Sample one with fourth dose
X4.1Sample two with fourth dose
X4.2Sample three with fourth dose
Testing for Trend in Dose-Response Microarray Experiments: a Comparison of Testing Procedures, Multiplicity, and Resampling-Based Inference, Lin et al. 2007, Stat. App. in Gen. & Mol. Bio., 6(1), article 26.
data(geneData) head(geneData)data(geneData) head(geneData)
The function calculates the likelihood for the increasing and decreasing trend in the dose response for all the given genes separately gene-by-gene. The trend with the higher likelihood is chosen and the isotonic regression is applied on the means.
monotoneDirection(geneData, doseData)monotoneDirection(geneData, doseData)
geneData |
gene expression matrix for all genes |
doseData |
indicates the dose levels |
A list with components
direction |
the direction with the higher likelihood of increasing (indicated by "up") or decreasing (indicated by "dn") trend. |
incData |
isotonic means with respect to dose for those genes that were classified as following the increasing trend. |
decData |
isotonic means with respect to dose for those genes that were classified as following the decreasing trend. |
obsincData |
observed gene expression matrix for those genes that were classified as following the increasing trend. |
obsdecData |
observed gene expression matrix for those genes that were classified as following the decreasing trend. |
arrayMean |
isotonic means with respect to dose for all genes. |
Adetayo Kasim, Martin Otava and Tobias Verbeke
Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R. Springer.
Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 1, 93-103.
data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) ## direction of monotone trend Direction <- dirData$direction ## Isotonic means for upward genes incData <- as.data.frame(dirData$incData) ##Isotonic means for downward genes decData <- as.data.frame(dirData$decData) ## observd data upward genes obsIncData <- as.data.frame(dirData$obsincData) ## observed data for downward genes obsDecData <- as.data.frame(dirData$obsdecData) ## isotonic means for all genes isoMeans <- as.data.frame(dirData$arrayMean)data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) ## direction of monotone trend Direction <- dirData$direction ## Isotonic means for upward genes incData <- as.data.frame(dirData$incData) ##Isotonic means for downward genes decData <- as.data.frame(dirData$decData) ## observd data upward genes obsIncData <- as.data.frame(dirData$obsincData) ## observed data for downward genes obsDecData <- as.data.frame(dirData$obsdecData) ## isotonic means for all genes isoMeans <- as.data.frame(dirData$arrayMean)
The function performs delta-clustering of a microarray data. It can be used for clustering of both the time-course or dose-response microarray data.
ORCME(DRdata, lambda, phi, robust=FALSE)ORCME(DRdata, lambda, phi, robust=FALSE)
DRdata |
matrix of a microarray data with rows corresponding to genes and columns corresponding to time points or different doses |
lambda |
assumed proportion of coherence relative to the observed data, it ranges between 0 and 1. A lambda value of 1 considers the observed data as a cluster and lambda value of 0 finds every possible pattern within the data. |
phi |
minimum number of genes in a cluster |
robust |
logical variable that determines, if algorithm uses robust version based on median polish and
absolute values, instead of mean square error. Default is |
The matrix of classification into clusters: each row represents one gene and columns found clusters. The matrix consist of the Booleans values, in each row there is only one of them TRUE which means that the gene was classified into the respective cluster.
Adetayo Kasim, Martin Otava and Tobias Verbeke
Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in EarlyDrug Development Experiments Using R. Springer.
Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 1, 93-103.
monotoneDirection, plotIsomeans
data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) print(orcme <- ORCME(DRdata=incData,lambda=0.15,phi=2)) orcmeRobust <- ORCME(DRdata=incData,lambda=0.15,phi=2, robust=TRUE) # number of genes within cluster colSums(orcme) colSums(orcmeRobust)data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) print(orcme <- ORCME(DRdata=incData,lambda=0.15,phi=2)) orcmeRobust <- ORCME(DRdata=incData,lambda=0.15,phi=2, robust=TRUE) # number of genes within cluster colSums(orcme) colSums(orcmeRobust)
The function is plotting the profiles of the genes that belongs to the same cluster. It is not providing the clustering itself, just plotting the results of clustering from input. Optionally, the function can center the profiles around the gene-specific means.
plotCluster(DRdata, doseData, ORCMEoutput, clusterID, zeroMean=FALSE, xlabel, ylabel, main="")plotCluster(DRdata, doseData, ORCMEoutput, clusterID, zeroMean=FALSE, xlabel, ylabel, main="")
DRdata |
the microarray data with rows corresponding to genes and columns corresponding to time points or different doses |
doseData |
indicates the dose levels |
ORCMEoutput |
the matrix of classification into clusters: each row represents one gene and columns found clusters. The matrix consist of the Booleans values, in each row there is only one of them TRUE which means that the gene was classified into the respective gene |
clusterID |
id of the cluster to be plotted |
zeroMean |
if TRUE, it centers the gene profiles around the gene-specific means, default is FALSE |
xlabel |
a title for the x axis |
ylabel |
a title for the y axis |
main |
an overall title for the plot |
Plot of the gene specific profiles dependent one the dose level (or time point) that are classified into the given cluster.
Adetayo Kasim, Martin Otava and Tobias Verbeke
Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R. Springer.
Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 1, 93-103.
data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) ORCMEoutput <- ORCME(DRdata=incData,lambda=0.15,phi=2) plotCluster(DRdata=incData,doseData=doseData, ORCMEoutput=ORCMEoutput, clusterID=4,zeroMean=FALSE, xlabel="Dose",ylabel="Gene Expression")data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) ORCMEoutput <- ORCME(DRdata=incData,lambda=0.15,phi=2) plotCluster(DRdata=incData,doseData=doseData, ORCMEoutput=ORCMEoutput, clusterID=4,zeroMean=FALSE, xlabel="Dose",ylabel="Gene Expression")
The function is plotting the observed data points of the gene expression and isotonic means with respect to dose for one particular gene.
plotIsomeans(monoData, obsData, doseData, geneIndex)plotIsomeans(monoData, obsData, doseData, geneIndex)
monoData |
isotonic means with respect to dose for all genes |
obsData |
observed gene expression for all genes |
doseData |
indicates the dose levels |
geneIndex |
index of the gene to be plotted |
Plot of the data points and the isotonic means for each dose with the isotonic regression curve.
Adetayo Kasim, Martin Otava and Tobias Verbeke
Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R. Springer.
Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 1, 93-103.
data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) obsIncData <- as.data.frame(dirData$obsincData) ## gene-specific profile plot plotIsomeans(monoData=incData,obsData=obsIncData,doseData= doseData,geneIndex=10)data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) obsIncData <- as.data.frame(dirData$obsincData) ## gene-specific profile plot plotIsomeans(monoData=incData,obsData=obsIncData,doseData= doseData,geneIndex=10)
This function provides the plots of the dependency of the variety of properties on the proportion of heterogeneity in observed data set. It is not using the clustering as simple input, but it is also computing additional properties. The function can plot within cluster sum of squares, number of cluster, penalized within cluster sum of squares, Calsanzik and Harabasx index and Hartigan index.
plotLambda(lambdaChoiceOutput,output)plotLambda(lambdaChoiceOutput,output)
lambdaChoiceOutput |
the output of the function resampleORCME |
output |
the variable that determines which output would be plotted, the values are "wss" for the cluster sum of squares, "ncluster" for the number of cluster, "pwss" for the penalized within cluster sum of squares, "ch" for the Calsanzik and Harabasx index and "h" for the Hartigan index |
A plot of one of the properties mentioned above dependent on the proportion of heterogeneity. The confidence intervals are plotted instead of the point estimates.
Adetayo Kasim, Martin Otava and Tobias Verbeke
Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R. Springer.
Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 1, 93-103.
data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) lambdaVector <- c(0.05,0.50,0.95) lambdaChoiceOutput <- resampleORCME(clusteringData=incData, lambdaVector=lambdaVector) plotLambda(lambdaChoiceOutput,output="wss") plotLambda(lambdaChoiceOutput,output="ncluster") plotLambda(lambdaChoiceOutput,output="pwss") plotLambda(lambdaChoiceOutput,output="ch") plotLambda(lambdaChoiceOutput,output="h")data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) lambdaVector <- c(0.05,0.50,0.95) lambdaChoiceOutput <- resampleORCME(clusteringData=incData, lambdaVector=lambdaVector) plotLambda(lambdaChoiceOutput,output="wss") plotLambda(lambdaChoiceOutput,output="ncluster") plotLambda(lambdaChoiceOutput,output="pwss") plotLambda(lambdaChoiceOutput,output="ch") plotLambda(lambdaChoiceOutput,output="h")
The function is computing within cluster sum of squares for given proportion of heterogeneity. Minimal number of genes per cluster is fixed as 2. The sum of squares is computed through resampling the 100 data sets with 100 genes randomly sampled with replacement from the reduced expression data.
resampleORCME(clusteringData, lambdaVector, robust=FALSE)resampleORCME(clusteringData, lambdaVector, robust=FALSE)
clusteringData |
the microarray data with rows corresponding to genes and columns corresponding to time points or different doses |
lambdaVector |
vector of assumed proportions of of heterogeneity of the observed data, it ranges between 0 and 1. A lambda value of 1 considers the observed data as a cluster and lambda value of 0 finds every possible pattern within the data |
robust |
logical variable that determines, if algorithm uses robust version based on median polish and
absolute values, instead of mean square error. Default is |
A list of matrices that represent one of the 100 iterations. Every matrix consist of the columns
lambda |
vector of the proportions of heterogeneity given as input |
WSS |
within clusters sum of squares for given proportion of heterogeneity |
TSS |
total clusters sum of squares for given proportions of heterogeneity |
nc |
number of clusters as a function for given proportions of heterogeneity |
Adetayo Kasim, Martin Otava and Tobias Verbeke
Lin D., Shkedy Z., Yekutieli D., Amaratunga D., and Bijnens, L. (editors). (2012) Modeling Dose-response Microarray Data in Early Drug Development Experiments Using R. Springer.
Cheng, Y. and Church, G. M. (2000). Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, 1, 93-103.
data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) lambdaVector <- c(0.05,0.50,0.95) resampleORCME(clusteringData=incData, lambdaVector=lambdaVector, robust=FALSE)data(doseData) data(geneData) dirData <- monotoneDirection(geneData = geneData,doseData = doseData) incData <- as.data.frame(dirData$incData) lambdaVector <- c(0.05,0.50,0.95) resampleORCME(clusteringData=incData, lambdaVector=lambdaVector, robust=FALSE)