A function to visualize the clustering results obtained from a Poisson mixture model.
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x 
An object of class 
file.name 
Optional file name if plots are to be saved in a PDF file. 
graphs 
Type of graph to be included in plots. May be equal to

capushe.validation 
Optional number of clusters to use for capushe validation (should be less than the maximum number of clusters
specificed in the 
data 
(n x q) matrix of observed counts for n observations and q variables (only required for the plotting of weighted histograms) 
... 
Additional arguments (mainly useful for plotting) 
For objects of class "HTSCluster"
, the plotting function provides the possibility for the following visualizations:
1) A histogram of maximum conditional probabilities across all clusters.
2) Percluster boxplots of maximum conditional probabilities.
3) Weighted histograms of observation profiles (with weights equal to the corresponding conditional probability for each observation in each cluster), plotted independently for each variable. Fitted densities after fitting the Poisson mixture model are overlaid in red.
4) A global view of λ and π values for the selected model. When the number of conditions <= 2, bar heights represent the value of λ_k for each cluster, and bar width corresponds to the value of π_k.
For objects of class "HTSClusterWrapper"
, the plotting function provides the possibility for one or all of the following visualizations:
1) ICL plot for all fitted models.
2) BIC plot for all fitted models.
5) Capushe diagnostic plots.
Andrea Rau
Rau, A., MaugisRabusseau, C., MartinMagniette, M.L., Celeux G. (2015). Coexpression analysis of highthroughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, 31(9):14201427.
Andrea Rau, Gilles Celeux, MarieLaure MartinMagniette, and Cathy MaugisRabusseau (2011). Clustering highthroughput sequencing data with Poisson mixture models. Technical report RR7786, Inria Saclay – IledeFrance.
PoisMixClus
, PoisMixClusWrapper
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  set.seed(12345)
## Simulate data as shown in Rau et al. (2011)
## Library size setting "A", high cluster separation
## n = 2000 observations
simulate < PoisMixSim(n = 200, libsize = "A", separation = "high")
y < simulate$y
conds < simulate$conditions
## Run the PMMII model for g = 3
## "TC" library size estimate, EM algorithm
run < PoisMixClus(y, g = 3,
norm = "TC", conds = conds, init.type = "smallem")
## Visualization of results (not run):
## plot(run)

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