| Title: | Consensus Clustering using Multiple Algorithms and Parameters |
|---|---|
| Description: | Functions for calculation of robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take. See Simpson, T. I., Armstrong, J. D. & Jarman, A. P. (2010) <doi:10.1186/1471-2105-11-590> and Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003) <doi:10.1023/a:1023949509487>. |
| Authors: | Dr. T. Ian Simpson [aut, cre, cph]
|
| Maintainer: | Dr. T. Ian Simpson <[email protected]> |
| License: | GPL (> 2) |
| Version: | 1.2 |
| Built: | 2024-08-14 14:49:43 UTC |
| Source: | https://github.com/biomedicalinformaticsgroup/clustercons |
clusterCons is a package containing functions that generate robustness measures for clusters and cluster membership based on generating consensus matrices from bootstrapped clustering experiments in which a random proportion of rows of the data set are used in each individual clustering. This allows the user to prioritise clusters and the members of clusters based on their consistency in this regime. The functions allow the user to select several algorithms to use in the re-sampling scheme and with any of the parameters that the algorithm would normally take.
| Package: | clusterCons |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2010-10-12 |
| License: | GPL |
| LazyLoad: | yes |
| Depends: | methods,cluster,lattice,RColorBrewer,grid,apcluster |
| Extends: | cluster |
| Suggests: | latticeExtra |
The user should first prepare an entirely numeric data.frame in which the conditions to be clustered are the column names and the unique ids
of the entities are the row names. Compatibility of the resulting data.fram can be checked by using the data_check function.
cluscomp - generate consensus matrices from re-sampled clustering experiments with the option of multiple algorithms and parametersclrob - calculate the robustness of the clusters from the consensus matrixmemrob - calculate the cluster membership robustness from the consensus matrix
agnes_clmem - wrapper for the agnes function of package clusterdiana_clmem - wrapper for the diana function of package clusterhclust_clmem - wrapper for the hclust function of package clusterkmeans_clmem - wrapper for the kmeans function of package clusterpam_clmem - wrapper for the pam function of package clusterapcluster_clmem - wrapper for the apclusterK function of package apcluster
auc - calculates the area under the curve for a series of clustering experiments with the same cluster numberaucs - calculates the areas under the curves of a series of clustering experiments over a range of cluster numbersdeltak - calculates the change in the area under the curve
data_check - check that the provided data.frame is formatted correctlyexpSetProcess - extracts the data set from an object of class expressionSetvalidConsMatrixObject - check the validity of a consmatrix objectvalidMergeMatrixObject - check the validity of a mergematrix objectvalidMemRobListObject - check the validity of a membership robustness list objectvalidMemRobMatrixObject - check the validity of a membership robustness matrix objectvalidAUCObject - check the validity of an "auc" class objectvalidDkObject - check the validity of an "dk" class object
aucplot - plot area under the curve (AUC) plots from consensus clustering resultsdkplot - plot change in AUC by cluster number (delta-K plot)expressionPlot - plot the original data partitioned by cluster membershipmembBoxPlot - plot a box and whisker plot of the membership robustness for each cluster
cluster
#load data data(sim_profile);
#perform consensus clustering cmr <- cluscomp(sim_profile,algo=list('agnes','pam','kmeans'),clmin=2,clmax=7,rep=10,merge=1);
#see the consensus and merge matrices summary(cmr);
#fetch the cluster robustness for agnes consensus clustering with k=3 clrob(cmr$e1_agnes_k3);
#show the membership robustness for cluster 1 memrob(cmr$e1_agnes_k3)$cluster1
#show the same, but for the merge against the k=3 agnes clustering structure #note we provide the reference matrix (which is the original cluster membership for agnes where k=3) clrob(cmr$merge_k3,cmr$e1_agnes_k3@rm); memrob(cmr$merge_k3,cmr$e1_agnes_k3@rm)$cluster1;
#calculate the AUCs acs <- aucs(cmr);
#plot the AUC curves aucplot(acs);
#calculate the delta-Ks dks <- deltak(acs);
#plot the delta-K curves dkplot(dks);
#plot the expression profiles expressionPlot(sim_profile,cmr$e1_agnes_k3);
#plot the bwplot of membership robustness for the same membBoxPlot(memrob(cmr$e1_agnes_k3));
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Monti, S., Tamayo, P., Mesirov, J. and Golub, T. Machine Learning, 52, July 2003.
These functions calculate the area under the curve (AUC) for cumulative density functions of a consensus matrix. The function auc operates on an indvidual consensus matrix whereas aucs operates on an entire cluscomp analysis result as described below.
auc(x) aucs(x)auc(x) aucs(x)
x |
For The functions will not allow any missing values (NAs). |
auc(x) returns an individual AUC value.
aucs(x) returns a data.frame with the following variables.
k |
cluster number as a factor |
a |
algorithm identifier as a factor |
aucs |
the AUC value |
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
#load up a test cluscomp result data('testcmr'); #look at the result structure summary(testcmr); #calculate an individual AUC value for a consensus matrix ac <- auc(testcmr$e1_kmeans_k2@cm); #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr);#load up a test cluscomp result data('testcmr'); #look at the result structure summary(testcmr); #calculate an individual AUC value for a consensus matrix ac <- auc(testcmr$e1_kmeans_k2@cm); #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr);
Objects of class 'auc' contain a data.frame which have three variables k, a and auc
as described in the aucs function description. This class simply holds the result from a call to aucs.
Objects can be created by calls of the form new("auc", ...), although they are normally generated internally by the aucs function.
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see the aucs function.
showClass("auc")showClass("auc")
This function uses the lattice function xyplot to generate an AUC plot from a valid "auc" class object (see auc-class).
aucplot(x)aucplot(x)
x |
a valid "auc" class object (see |
No return value, called for side effects
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
#load up a test cluscomp result data('testcmr'); #look at the result structure summary(testcmr); #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr); #plot the AUC curve aucplot(acs);#load up a test cluscomp result data('testcmr'); #look at the result structure summary(testcmr); #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr); #plot the AUC curve aucplot(acs);
These methods are mainly internal although the user may like to check their original data using
data_check before they perform consensus clustering experiments.
data_check(x) validConsMatrixObject(object) validMemRobListObject(object) validMemRobMatrixObject(object) validMergeMatrixObject(object) validAUCObject(object) validDkObject(object)data_check(x) validConsMatrixObject(object) validMemRobListObject(object) validMemRobMatrixObject(object) validMergeMatrixObject(object) validAUCObject(object) validDkObject(object)
x |
The data.frame object to be checked prior to using with the |
object |
The object to be checked with the suitable function by type. These are used internally by several of the functions in the package. |
returns TRUE if check is passed or an error message if it is not
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
#load data data(sim_profile); #check if this can be used by cluscomp data_check(sim_profile); #perform a clusomp run cmr <- cluscomp(sim_profile,clmin=2,clmax=2,rep=10); #check one of the consensus matrices validConsMatrixObject(cmr$e1_kmeans_k2)#load data data(sim_profile); #check if this can be used by cluscomp data_check(sim_profile); #perform a clusomp run cmr <- cluscomp(sim_profile,clmin=2,clmax=2,rep=10); #check one of the consensus matrices validConsMatrixObject(cmr$e1_kmeans_k2)
This function calculates the cluster robustness from a consmatrix or mergematrix
class object.
clrob(x,rm)clrob(x,rm)
x |
either a |
rm |
(optional) if a |
Returns a data.frame of the cluster robustness values indexed by cluster number.
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see cluscomp, consmatrix and mergematrix.
#load cmr (consensus clustering result produced by cluscomp) data(testcmr); #calculate the cluster robustness of the consensus matrix for pam where k=4 clrob(testcmr$e1_kmeans_k4); #calculate the cluster robustness of the merge matrix in reference #to the clustering structrure of pam where k=4 clrob(testcmr$merge_k4,testcmr$e1_kmeans_k4@rm);#load cmr (consensus clustering result produced by cluscomp) data(testcmr); #calculate the cluster robustness of the consensus matrix for pam where k=4 clrob(testcmr$e1_kmeans_k4); #calculate the cluster robustness of the merge matrix in reference #to the clustering structrure of pam where k=4 clrob(testcmr$merge_k4,testcmr$e1_kmeans_k4@rm);
Calculates an NxN consensus matrix for each clustering experiment performed where each entry has a value between 0 (never observed) and 1 (always observed)
When running with more than one algorithm or with the same algorithm and multiple conditions a consensus matrix will be generated for each.
These can optionally be merged into a mergematrix by cluster number by setting merge=1.
cluscomp( x, diss=FALSE, algorithms = list("kmeans"), alparams = list(), alweights = list(), clmin = 2, clmax = 10, prop = 0.8, reps = 50, merge = 0 )cluscomp( x, diss=FALSE, algorithms = list("kmeans"), alparams = list(), alweights = list(), clmin = 2, clmax = 10, prop = 0.8, reps = 50, merge = 0 )
x |
data.frame of numerical data with conditions as the column names and unique ids as the row names. All variables must be numeric. Missing values(NAs) are not allowed. Optionally you can pass a distance matrix directly, in which case you must ensure that the distance matrix is a data.frame and that the row and column names match each other (as the distance matrix is a pair-wise distance calculation). |
diss |
set to TRUE if you are providing a distance matrix, default is FALSE |
algorithms |
list of algorithm names which can be drawn from 'agnes','diana','pam','kmeans' or 'hclust'. The user can also write a simple wrapper for any other clustering method (see details) |
alparams |
list of algorithm paramter lists using the same specification as for the individual algorithm called (see details) |
alweights |
list of integer weights for each algorithm (only used when merging consensus results between algorithms) |
clmin |
integer for the smallest cluster number to consider |
clmax |
integer for the largest cluster number to consider |
prop |
numeric for the proportion of rows to sample during the process. Must be between 0 and 1 |
reps |
integer for the number of iterations to perform per clustering |
merge |
an integer indicating whether you also want the merged matrices (1) or just the consensus ones (0), accepts only 1 or 0. |
cluscomp is an implementation of a consensus clustering methodology first proposed by Monti et al. (2003) in which the connectivity between any two members of a data matrix is tested by resampling statistics. The principle is that by only sampling a random proportion of rows in the data matrix and performing many clustering experiments we can capture information about the robustness of the clusters identified by the full unsampled clustering result.
For each re-sampling experiment run a zero square matrix is created with identical rows and columns matching the unique ids of the rows of the data matrix, this matrix is called the connectivity matrix. A second identically sized matrix is created to count the number of times that any pair of row ids are called in any one re-sampled clustering. This matrix is called the identity matrix. For each iteration within the experiment the rows sampled are recorded in the identity matrix and then the co-occurrence of all pairs are recorded in the connectivity matrix. These values are incremented for each iteration until finally a conensensus matrix is generated by dividing the connectivity matrix by the identity matrix.
The consensus matrix is the raw output from cluscomp implemented as a class
consmatrix. If the user has specified to return a merged matrix in addition to the consensus
matrices then for each clustering with the same k (cluster number value) an object of class mergematrix is also
returned in the list which is identical to a consmatrix with the exception that the
'cm' slot is occupied by the merged matrix (a weighted average of all the consensus matrices for
the cluster number matched consensus matrices) and there is no reference matrix slot (as there is no
reference clustering for the merge). The user should instead call the memrob
function using the merge matrix and providing a reference matrix from one of the cluster number
matched consmatrix objects from which the merge was generated. This provides a way
to quantify the difference between single and multi-algorithm resampling schemes.
a list of objects of class consmatrix and (if merge specified) mergematrix. See consmatrix and mergematrix for details.
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data. Monti, S., Tamayo, P., Mesirov, J. and Golub, T. Machine Learning, 52, July 2003.
#load test data data(sim_profile); #perform a group of re-sampling clustering experiments accepting default parameters #for the clustering algorithms cmr <- cluscomp( sim_profile, algorithms=list('kmeans','pam'), merge=1, clmin=2, clmax=5, reps=5 ) #display resulting matrices contained in the consensus result list summary(cmr); #display the cluster robusteness for the kmeans k=4 consensus matrix clrob(cmr$e2_pam_k4); #plot a heatmap of the consensus matrix, note you access the cluster matrix object #through the cm slot #heatmap(cmr$e2_pam_k4@cm); #display the membership robustness for kmeans k=4 cluster 1 memrob(cmr$e2_pam_k4)$cluster1; #merged consensus example #data(testcmr); #calculate the membership robustness for the merge matrix when cluster number k=4, #in reference to the pam scaffold. (see memrob for more details). #mr <- memrob(testcmr$merge_k4,testcmr$e1_kmeans_k4@rm); #show the membership robustness for cluster 1 #mr$cluster1;#load test data data(sim_profile); #perform a group of re-sampling clustering experiments accepting default parameters #for the clustering algorithms cmr <- cluscomp( sim_profile, algorithms=list('kmeans','pam'), merge=1, clmin=2, clmax=5, reps=5 ) #display resulting matrices contained in the consensus result list summary(cmr); #display the cluster robusteness for the kmeans k=4 consensus matrix clrob(cmr$e2_pam_k4); #plot a heatmap of the consensus matrix, note you access the cluster matrix object #through the cm slot #heatmap(cmr$e2_pam_k4@cm); #display the membership robustness for kmeans k=4 cluster 1 memrob(cmr$e2_pam_k4)$cluster1; #merged consensus example #data(testcmr); #calculate the membership robustness for the merge matrix when cluster number k=4, #in reference to the pam scaffold. (see memrob for more details). #mr <- memrob(testcmr$merge_k4,testcmr$e1_kmeans_k4@rm); #show the membership robustness for cluster 1 #mr$cluster1;
Objects of class 'consmatrix' are created to hold the results of a consensus clustering experiment along with the necessary ancillary
data to allow the subsequent downstream calculations such as cluster and membership robustness. In addition the object holds the original call
made when running cluscomp.
Objects can be created by calls of the form new("consmatrix", ...), but are normally created internally by the cluscomp function to store
consensus matrices and their associated meta-data.
cm:Object of class "matrix" - the consensus matrix itself
rm:Object of class "data.frame" - the cluster membership of the full (i.e. not consensus) clustering result when
the current algorith is called with the same algorithm parameters as the consensus clustering run. This is needed to be able to work with
merge matrices that need a clustering structure on which to operate to produce cluster and membership robustness values.
a:Object of class "character" - the clustering algorithm name
k:Object of class "numeric" - the cluster number (k) used
call:Object of class "call" - the original parameters passed to cluscomp for provenance and reproducibility
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
See Also cluscomp
showClass("consmatrix"); #you can access the slots in useful ways #load a cmr data(testcmr); #get a consensus clustering matrix via the 'cm' slot cm <- testcmr$e1_kmeans_k4@cm; #this can be used as a distance matrix, e.g. for a heatmap heatmap(cm); #or as a new distance matrix dm <- data.frame(cm) #first convert to a data.frame #make sure names are the same for rows and columns names(dm) <- row.names(dm); #you need to explicitly tell cluscomp that you are passing a distance matrix cmr2 <- cluscomp(dm,diss=TRUE,clmin=2,clmax=4,rep=2); #for merge consensus clustering you take advantage of the reference matrix (rm) slot #cluster robustness for agnes with cluster number (k) = 3 clrob(testcmr$merge_k3,testcmr$e1_kmeans_k3@rm); #membership robustness for cluster 1 memrob(testcmr$merge_k3,testcmr$e1_kmeans_k3@rm)$cluster1;showClass("consmatrix"); #you can access the slots in useful ways #load a cmr data(testcmr); #get a consensus clustering matrix via the 'cm' slot cm <- testcmr$e1_kmeans_k4@cm; #this can be used as a distance matrix, e.g. for a heatmap heatmap(cm); #or as a new distance matrix dm <- data.frame(cm) #first convert to a data.frame #make sure names are the same for rows and columns names(dm) <- row.names(dm); #you need to explicitly tell cluscomp that you are passing a distance matrix cmr2 <- cluscomp(dm,diss=TRUE,clmin=2,clmax=4,rep=2); #for merge consensus clustering you take advantage of the reference matrix (rm) slot #cluster robustness for agnes with cluster number (k) = 3 clrob(testcmr$merge_k3,testcmr$e1_kmeans_k3@rm); #membership robustness for cluster 1 memrob(testcmr$merge_k3,testcmr$e1_kmeans_k3@rm)$cluster1;
These data sets are used by the examples in the package function descriptions and allow the user to explore the functionality of the package
data(golub); data(sim_class); data(sim_profile); data(testcmr);data(golub); data(sim_class); data(sim_profile); data(testcmr);
golub : data.frame of gene expression values for 999 genes for 38 leukemia patients (1-27) ALL and (28-38) AML.
sim_class : data.frame of 200 simulated gene expression values for 30 conditions where there are 4 discrete classes of expression profile, for testing clustering with the transposed data (clustering by column).
sim_profile : data.frame of 120 simulated gene expression values for 4 conditions where there are 4 discrete classes of expression profile, for testing general clustering (clustering by row).
testcmr : list of consensus and merge matrix results from a cluscomp run (see consmatrix-class and mergematrix-class).
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Golub, TR and Slonim, DK and Tamayo, P and Huard, C and Gaasenbeek, M and Mesirov, JP and Coller, H and Loh, ML and Downing, JR and Caligiuri, MA and Bloomfield, CD and Lander, ES. Science 1999, 286:531-537
#cluster by class data(sim_class); cutree(agnes(t(sim_class)),4); #cluster by profile data(sim_profile); cutree(agnes(sim_profile),4);#cluster by class data(sim_class); cutree(agnes(t(sim_class)),4); #cluster by profile data(sim_profile); cutree(agnes(sim_profile),4);
This function takes an "auc" class object and calculates the difference in AUC value by cluster number (called delta-K). Peaks in delta-K
coincide with the cluster numbers that are most robust and provide estimates for the optimal cluster number.
deltak(x)deltak(x)
x |
a valid |
deltak(x) returns a data.frame with the following variables.
k |
cluster number as a factor |
a |
algorithm identifier as a factor |
dk |
the delta-K value |
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see the aucs function.
#load a test cluscomp result set data(testcmr) #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr); #calculate the delta-K values dks <- deltak(acs);#load a test cluscomp result set data(testcmr) #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr); #calculate the delta-K values dks <- deltak(acs);
Objects of class 'dk' contain a data.frame which have three variables k, a and deltak
as described in the deltak function description. This class simply holds the result from a call to deltak.
Objects can be created by calls of the form new("dk", ...), although they are normally generated internally by the deltak function.
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see the aucs function.
showClass("dk")showClass("dk")
This function uses the lattice function xyplot to generate an delta-K plot from a valid "dk" class object (see dk-class).
dkplot(x)dkplot(x)
x |
a valid "dk" class object (see |
No return value, called for side effects
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
#load up a test cluscomp result data('testcmr'); #look at the result structure summary(testcmr); #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr); #calculate all of the delta-K values dks <- deltak(acs); #plot the delta-K curve dkplot(dks);#load up a test cluscomp result data('testcmr'); #look at the result structure summary(testcmr); #calculate all of the AUC values from the \code{cluscomp} result for algorithm 'kmeans' kmeanscmr <- testcmr[grep('kmeans',names(testcmr))]; acs <- aucs(kmeanscmr); #calculate all of the delta-K values dks <- deltak(acs); #plot the delta-K curve dkplot(dks);
This function uses the lattice function xyplot to generate a profile plot of the data values grouped by cluster in a multi-panel plot. The function
takes as input the original data.frame() and a valid "consmatrix" class object (see consmatrix-class) by which to segregate the data.
expressionPlot(x,cm);expressionPlot(x,cm);
x |
the original data.frame() object used in the clustering. |
cm |
a valid |
No return value, called for side effects
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
#load up the data set data(sim_profile); #load up an example cluscomp result with this data data('testcmr'); #plot the expression profiles expressionPlot(sim_profile,testcmr$e1_kmeans_k4);#load up the data set data(sim_profile); #load up an example cluscomp result with this data data('testcmr'); #plot the expression profiles expressionPlot(sim_profile,testcmr$e1_kmeans_k4);
This is a convenience function that is used internally to allow the user to pass an expressionSet object from the microarray processing package 'affy'
directly to the cluscomp function.
expSetProcess(x)expSetProcess(x)
x |
An object of class expressionSet from the Bioconductor package 'affy'. |
when called directly, returns a suitably labeled data.frame() object of the expressionSet expression values.
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
This function uses the lattice function bwplot to generate a box and whisker plot of membership robustness from the result of a call to the memrob function.
membBoxPlot(x)membBoxPlot(x)
x |
the result of a call to the |
No return value, called for side effects
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
#load up a test cluscomp result data('testcmr'); #calculate the membershpi robustness for one of the clustering results mr <- memrob(testcmr$e1_kmeans_k5); #plot the bwplot membBoxPlot(mr);#load up a test cluscomp result data('testcmr'); #calculate the membershpi robustness for one of the clustering results mr <- memrob(testcmr$e1_kmeans_k5); #plot the bwplot membBoxPlot(mr);
This function calculates the membership robustness from a consmatrix or mergematrix
class object.
memrob(x,rm)memrob(x,rm)
x |
either a |
rm |
(optional) if a |
Returns a list of memroblist class objects, one for each cluster, and the full membership robustness matrix as a memrobmatrix class object.
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see cluscomp, consmatrix and mergematrix.
#load cmr (consensus clustering result produced by cluscomp) data(testcmr); #calculate the cluster robustness of the consensus matrix for pam where k=4 mr1 <- memrob(testcmr$e1_kmeans_k4); #show the membership robustness of cluster 1 mr1$cluster1; #calculate the cluster robustness of the merge matrix in reference #to the clustering structure of pam where k=4 mr2 <- memrob(testcmr$merge_k4,testcmr$e1_kmeans_k4@rm); #plot a heatmap of the full membership robustness matrix heatmap(mr2$resultmatrix@mrm)#load cmr (consensus clustering result produced by cluscomp) data(testcmr); #calculate the cluster robustness of the consensus matrix for pam where k=4 mr1 <- memrob(testcmr$e1_kmeans_k4); #show the membership robustness of cluster 1 mr1$cluster1; #calculate the cluster robustness of the merge matrix in reference #to the clustering structure of pam where k=4 mr2 <- memrob(testcmr$merge_k4,testcmr$e1_kmeans_k4@rm); #plot a heatmap of the full membership robustness matrix heatmap(mr2$resultmatrix@mrm)
Objects of class 'memroblist' are created to hold the membership robustness scores for the features (e.g. genes) of a cluster.
Objects can be created by calls of the form new("memroblist", ...), although these objects are normally created internally by the memrob function.
mrl:Object of class "data.frame" - the membership robustness list itself
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see the memrob function/
showClass("memroblist") #load a cmr data(testcmr); #calculate the membership robustness for agnes, k=4 mr <- memrob(testcmr$e2_agnes_k4); #get a membership robustness list mrl <- mr$cluster1;showClass("memroblist") #load a cmr data(testcmr); #calculate the membership robustness for agnes, k=4 mr <- memrob(testcmr$e2_agnes_k4); #get a membership robustness list mrl <- mr$cluster1;
Objects of class 'memrobmatrix' hold the full membership robustness matrix generated from analysis of a consensus matrix. This
includes the calculations of membership robustness for all features (e.g. genes) for each cluster. This can be useful as it allows you to
see what conritbution a particular feature (e.g. gene) is making to other clusters. This could resonably be thought of as a measure similar
to 'fuzziness' i.e. partial cluster membership. If the value of the membership robustness for a feature is similar in many clusters then that
is additional evidence that the feature is not easily placed in any cluster.
Objects can be created by calls of the form new("memrobmatrix", ...), although they are usually generated internally by the memrob function.
mrm:Object of class "matrix" - this is the full membership robustness matrix itself and therefore has the same dimensions as the
original data object used in the clustering
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see the memrob function.
showClass("memrobmatrix") #load cmr data(testcmr); #calculate membership robustness mr <- memrob(testcmr$e1_kmeans_k3) #get the full membership robustness matrix (matrix itself held in slot 'mrm') mrm <- mr$resultmatrix@mrm;showClass("memrobmatrix") #load cmr data(testcmr); #calculate membership robustness mr <- memrob(testcmr$e1_kmeans_k3) #get the full membership robustness matrix (matrix itself held in slot 'mrm') mrm <- mr$resultmatrix@mrm;
Objects of class 'mergematrix' hold the merge matrix in the same way that a consmatrix object holds a consensus matrix. As merge matrices only
make sense in the context of the consensus clustering results that were used to generate them we do not store the meta-data for any one consensus clustering parameter set
as we do for a 'consmatrix' object. All we need to identify the 'mergematrix' is the cluster number.
Objects can be created by calls of the form new("mergematrix", ...), although they are normally generated by the cluscomp function when merge is specfied.
cm:Object of class "matrix" - the merge matrix itself
k:Object of class "numeric" - the cluster number (k) value for which the merge was calculated
a:Object of class "character" - always takes the value of 'merge' to identify it as a merge matrix
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
Also see the cluscomp function.
showClass("mergematrix") #load the cmr data(testcmr); #get a merge matrix object mm <- testcmr$merge_k4; #plot a heatmap of the merge matrix heatmap(mm@cm);showClass("mergematrix") #load the cmr data(testcmr); #get a merge matrix object mm <- testcmr$merge_k4; #plot a heatmap of the merge matrix heatmap(mm@cm);
These are primarily internal functions called by cluscomp to execute
clustering runs and are unlikely to be used directly. The wrappers are detailed in
the algorithm.R file of the clusterCons package and the user can add
their own wrappers to this to extend the number of algorithms supported. These five
wrappers allow the user to specify the conditions under which the corresponding
clustering algorithms are run and follow exactly the same specifications as the
corresponding cluster functions (see agnes, pam,
hclust, diana and kmeans).
agnes_clmem(x, clnum, params = list()) pam_clmem(x, clnum, params = list()) hclust_clmem(x, clnum, params = list()) diana_clmem(x, clnum, params = list()) kmeans_clmem(x, clnum, params = list()) apcluster_clmem(x,clnum,params = list())agnes_clmem(x, clnum, params = list()) pam_clmem(x, clnum, params = list()) hclust_clmem(x, clnum, params = list()) diana_clmem(x, clnum, params = list()) kmeans_clmem(x, clnum, params = list()) apcluster_clmem(x,clnum,params = list())
x |
A data.frame of numerical values to be clustered which must pass the |
clnum |
The number of specified clusters. When using the |
params |
A list of key, value pairs specifying the parameters to pass to the clustering algorithm. These
follow the exact specification of the original functions in the |
Returns a data.frame with row.names matching that of the data.
cm |
cluster membership identifier specifying the cluster into which the row has been classified |
Dr. T. Ian Simpson [email protected]
Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.
cluster, agnes, pam, hclust, diana, kmeans and apclusterK
#load some data data(sim_profile); #run a basic agnes clustering with 3 clusters cm <- agnes_clmem(sim_profile,3); #pass some more complex parameters agnes_params = list(metric='manhattan',method='single'); cm <- agnes_clmem(sim_profile, 3,params=agnes_params);#load some data data(sim_profile); #run a basic agnes clustering with 3 clusters cm <- agnes_clmem(sim_profile,3); #pass some more complex parameters agnes_params = list(metric='manhattan',method='single'); cm <- agnes_clmem(sim_profile, 3,params=agnes_params);