BHC
Bayesian Hierarchical Clustering

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BHC: Function to perform Bayesian Hierarchical Clustering on a 2D...

BHC: Function to perform Bayesian Hierarchical Clustering on a 2D...
In BHC: Bayesian Hierarchical Clustering

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets.

Usage

1
2
bhc(data, itemLabels, nFeatureValues, timePoints, dataType,
    noise, numReps, noiseMode, robust, numThreads, randomised, m, verbose)

Arguments

data

A 2D array containing discretised data. The dimensions of data should be nDataItems * nFeatures, and the algorithm will cluster the data items.

itemLabels

A character array containing nDataItems entries, one for each data item in the analysis. The leaf nodes of the output dendrogram will be labelled with these labels.

nFeatureValues

Deprecated. This is a legacy argument, retained for backwards compatibility. Any value passed to it will have no effect.

timePoints

An array of length nFeatures, containing the time points of the measurements.

dataType

A string specifying the data type. Either ``multinomial'', ``time-course'', or ``cubicspline''.

noise

Noise term for each gene, required only if noiseMode=2. The noise term for each gene is calculated as

\frac{∑(\mathrm{residuals}^2)}{(\mathrm{number\, of\, observations\, for\, gene} - 1)(\mathrm{number\, of\, replicates})},

where (number of observations for gene) is typically (number of time points * number of replicates).

numReps

Number of replicates per observation.

noiseMode

Noise mode. If 0 then fitted noise; 2 estimated noise from replicates.

robust

0 to use single Gaussian likelihood, 1 to use mixture likelihood.

numThreads

The BHC library has been parallelised using OpenMP (currently on UN*X systems only). Specify here the number of threads to use (the default value is 1).

randomised

Set to TRUE if you wish to use the randomised algorithm.

m

If randomised is set to TRUE, then this is the dimension of the randomly chosen subset D_m in the randomised algorithm.

verbose

If set to TRUE, the algorithm will output some information to screen as it runs.

Details

Typical usage for the multinomial case: 

1
bhc(data, itemLabels).

To use the squared-exponential covariance: 

1
2
bhc(data, itemLabels, 0, timePoints, "time-course",
    noise, numReps, noiseMode),

and the cubic spline covariance: 

1
2
bhc(data, itemLabels, 0, timePoints, "cubicspline",
    noise, numReps, noiseMode).

To use the randomised algorithm, simply include the following two arguments: 

1
2
bhc(data, itemLabels, 0, timePoints, "time-course",
    noise, numReps, noiseMode, randomised=TRUE, m=10)

Value

A DENDROGRAM object (see the R stats package for details).

Author(s)

Rich Savage, Emma Cooke, Robert Darkins, and Yang Xu

References

Bayesian Hierarchical Clustering, Heller + Ghahramani, Gatsby Unit Technical Report GCNU-TR 2005-002 (2005); also see shorter version in ICML-2005; R/BHC:fast Bayesian hierarchical clustering for microarray data, Savage et al, BMC Bioinformatics 10:242 (2009); Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements, Cooke et al, currently under review

See Also

hclust

Examples

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##BUILD SAMPLE DATA AND LABELS
data         <- matrix(0,15,10)
itemLabels   <- vector("character",15)
data[1:5,]   <- 1 ; itemLabels[1:5]   <- "a"
data[6:10,]  <- 2 ; itemLabels[6:10]  <- "b"
data[11:15,] <- 3 ; itemLabels[11:15] <- "c"
timePoints   <- 1:10 # for the time-course case

##DATA DIMENSIONS
nDataItems <- nrow(data)
nFeatures  <- ncol(data)

##RUN MULTINOMIAL CLUSTERING
hc1 <- bhc(data, itemLabels, verbose=TRUE)
plot(hc1, axes=FALSE)

##RUN TIME-COURSE CLUSTERING
hc2 <- bhc(data, itemLabels, 0, timePoints, "time-course",
          numReps=1, noiseMode=0, numThreads=2, verbose=TRUE)
plot(hc2, axes=FALSE)

##OUTPUT CLUSTER LABELS TO FILE
WriteOutClusterLabels(hc1, "labels.txt", verbose=TRUE)

##FOR THE MULTINOMIAL CASE, THE DATA CAN BE DISCRETISED
newData      <- data[] + rnorm(150, 0, 0.1);
percentiles  <- FindOptimalBinning(newData, itemLabels, transposeData=TRUE, verbose=TRUE)
discreteData <- DiscretiseData(t(newData), percentiles=percentiles)
discreteData <- t(discreteData)
hc3          <- bhc(discreteData, itemLabels, verbose=TRUE)
plot(hc3, axes=FALSE)

Example output

[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]   0.7642338 -88.7830929
[1]   1.236254 -99.514068
[1]   0.4309005 -79.1391819
[1]   0.4309005 -79.1391819
[1]   0.4309005 -79.1391819
[1] Hyperparameter: 0.430900452187475
[1] Lower bound on overall LogEvidence: -7.9139e+01
[1] *******************
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: time-course"
[1]   0.0000 242.4406
[1] Hyperparameter: 0
[1] Lower bound on overall LogEvidence: 2.4244e+02
[1] *******************
[1] ---CLUSTER 1 ---
[1] c
[1] c
[1] c
[1] c
[1] c
[1] ---CLUSTER 2 ---
[1] a
[1] a
[1] a
[1] a
[1] a
[1] ---CLUSTER 3 ---
[1] b
[1] b
[1] b
[1] b
[1] b

DATA DISCRETISATION
-------------------
Percentiles: 0.1 0.8 0.1 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 281.454243850254
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -100.3550
[1] 1265.9246 -100.3267
[1] 1564.651 -100.318
[1] 1806.2702 -100.3131
[1] 1898.6027 -100.3115
[1] 1955.6674 -100.3106
[1] 1990.9353 -100.3101
[1] 2012.7321 -100.3098
[1] 2026.2032 -100.3096
[1] 2034.5289 -100.3095
[1] 2039.6744 -100.3094
[1] 2042.8545 -100.3094
[1] 2044.8199 -100.3093
[1] 2046.0346 -100.3093
[1] 2046.7853 -100.3093
[1] 2047.2493 -100.3093
[1] 2047.5826 -100.3093
[1] 2047.5826 -100.3093
[1] 2047.5826 -100.3093
[1] Hyperparameter: 2047.58264278864
[1] Lower bound on overall LogEvidence: -1.0031e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.15 0.7 0.15 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 353.192289224347
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -143.8307
[1] 1265.9246 -143.7785
[1] 1564.6508 -143.7624
[1] 1808.0122 -143.7532
[1] 1715.0564 -143.7564
[1] 1899.6794 -143.7503
[1] 1956.3328 -143.7487
[1] 1991.3466 -143.7477
[1] 2012.9862 -143.7471
[1] 2026.3603 -143.7468
[1] 2034.6259 -143.7466
[1] 2039.7344 -143.7464
[1] 2042.8916 -143.7463
[1] 2044.8428 -143.7463
[1] 2046.0488 -143.7463
[1] 2046.7941 -143.7462
[1] 2047.2547 -143.7462
[1] 2047.5881 -143.7462
[1] 2047.5881 -143.7462
[1] 2047.5881 -143.7462
[1] Hyperparameter: 2047.58805279817
[1] Lower bound on overall LogEvidence: -1.4375e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.2 0.6 0.2 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 353.192289224347
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -143.8307
[1] 1265.9246 -143.7785
[1] 1564.6508 -143.7624
[1] 1808.0122 -143.7532
[1] 1715.0564 -143.7564
[1] 1899.6794 -143.7503
[1] 1956.3328 -143.7487
[1] 1991.3466 -143.7477
[1] 2012.9862 -143.7471
[1] 2026.3603 -143.7468
[1] 2034.6259 -143.7466
[1] 2039.7344 -143.7464
[1] 2042.8916 -143.7463
[1] 2044.8428 -143.7463
[1] 2046.0488 -143.7463
[1] 2046.7941 -143.7462
[1] 2047.2547 -143.7462
[1] 2047.5881 -143.7462
[1] 2047.5881 -143.7462
[1] 2047.5881 -143.7462
[1] Hyperparameter: 2047.58805279817
[1] Lower bound on overall LogEvidence: -1.4375e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.25 0.5 0.25 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 353.192289224347
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -143.8307
[1] 1265.9246 -143.7785
[1] 1564.6508 -143.7624
[1] 1808.0122 -143.7532
[1] 1715.0564 -143.7564
[1] 1899.6794 -143.7503
[1] 1956.3328 -143.7487
[1] 1991.3466 -143.7477
[1] 2012.9862 -143.7471
[1] 2026.3603 -143.7468
[1] 2034.6259 -143.7466
[1] 2039.7344 -143.7464
[1] 2042.8916 -143.7463
[1] 2044.8428 -143.7463
[1] 2046.0488 -143.7463
[1] 2046.7941 -143.7462
[1] 2047.2547 -143.7462
[1] 2047.5881 -143.7462
[1] 2047.5881 -143.7462
[1] 2047.5881 -143.7462
[1] Hyperparameter: 2047.58805279817
[1] Lower bound on overall LogEvidence: -1.4375e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.3 0.4 0.3 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.35 0.3 0.35 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 365.826459355395
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -158.8043
[1] 1265.9246 -158.7461
[1] 1564.6508 -158.7281
[1] 1808.2878 -158.7178
[1] 1715.2268 -158.7214
[1] 1899.8497 -158.7146
[1] 1956.4381 -158.7128
[1] 1991.4116 -158.7117
[1] 2013.0265 -158.7111
[1] 2026.3852 -158.7107
[1] 2034.6413 -158.7104
[1] 2039.7439 -158.7103
[1] 2042.8974 -158.7102
[1] 2044.8464 -158.7101
[1] 2046.0510 -158.7101
[1] 2046.7954 -158.7101
[1] 2047.2555 -158.7101
[1] 2047.5889 -158.7101
[1] 2047.5889 -158.7101
[1] 2047.5889 -158.7101
[1] Hyperparameter: 2047.58890894343
[1] Lower bound on overall LogEvidence: -1.5871e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.26 0.48 0.26 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.27 0.46 0.27 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.28 0.44 0.28 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.29 0.42 0.29 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.3 0.4 0.3 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.31 0.38 0.31 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.32 0.36 0.32 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.33 0.34 0.33 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

DATA DISCRETISATION
-------------------
Percentiles: 0.34 0.32 0.34 
We have the following parameters for the data array:
nGenes:       15
nExperiments: 10
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 376.199589369386
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]  782.5754 -163.5695
[1] 1265.9246 -163.5086
[1] 1564.6508 -163.4898
[1] 1808.379 -163.479
[1] 1715.2829 -163.4827
[1] 1899.9058 -163.4756
[1] 1956.4728 -163.4737
[1] 1991.4331 -163.4726
[1] 2013.0397 -163.4719
[1] 2026.3934 -163.4715
[1] 2034.6464 -163.4713
[1] 2039.7470 -163.4711
[1] 2042.899 -163.471
[1] 2044.848 -163.471
[1] 2046.0517 -163.4709
[1] 2046.7959 -163.4709
[1] 2047.2558 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] 2047.5892 -163.4709
[1] Hyperparameter: 2047.58919090973
[1] Lower bound on overall LogEvidence: -1.6347e+02
[1] *******************

OPTIMISED DISCRETISATION
------------------------
Percentiles: 0.3 0.4 0.3
LogEvidence: 212.7287

DATA DISCRETISATION
-------------------
Percentiles: 0.3 0.4 0.3 
We have the following parameters for the data array:
nGenes:       10
nExperiments: 15
***Please check that these are the right way round! (it affects the discretisation)***

Discretisation logEvidence: 98.1799959220148
(Need to add this to the model logEvidence)
-------------------
[1] Running Bayesian Hierarchical Clustering....
[1] "DataType: multinomial"
[1] Optimising global hyperparameter...
[1]    0.8411863 -115.5340881
[1]    1.283814 -121.370976
[1]    0.5078529 -111.1444609
[1]    0.5078529 -111.1444609
[1]    0.5078529 -111.1444609
[1] Hyperparameter: 0.507852925225962
[1] Lower bound on overall LogEvidence: -1.1114e+02
[1] *******************

BHC documentation built on Nov. 8, 2020, 7:54 p.m.

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