plot.HTSCluster: Visualize results from clustering using a Poisson mixture model

Description

A function to visualize the clustering results obtained from a Poisson mixture model.

Usage

## S3 method for class 'HTSCluster':
plot(x, cluster.choice = "ICL", file.name = FALSE, 
	graphs = c("BIC.ICL", "map", "map.bycluster", "weighted.histograms", "lambda"), ...)

Arguments

An object of class "HTSCluster"

cluster.choice

The number of clusters desired, as selected by the "ICL" criterion (the default), the "BIC" criterion, or an arbitrary number of clusters among those estimated in x

file.name

Optional file name if plots are to be saved in a PDF file.

graphs

Type of graph to be included in plots.

...

Additional arguments (mainly useful for plotting)

Details

Provides the following visualizations: 1) A histogram of maximum conditional probabilities across all clusters. 2) Per-cluster 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 $\ensuremath\boldsymbol{\lambda}$ and $\ensuremath\boldsymbol{\pi}$ values for the selected model. When the number of conditions <= 2,="" bar="" heights="" represent="" the="" value="" of="" $\ensuremath\boldsymbol{\lambda}_k$="" for="" each="" cluster,="" and="" width="" corresponds="" to="" $\ensuremath\boldsymbol{\pi_k}$.="" when="" number="" conditions=""> 2, star plots are drawn for each cluster to illustrate the relative value of $\ensuremath\boldsymbol{\lambda}_k$ in each condition, where half-circle radiuses reflect the corresponding value of $\ensuremath\boldsymbol{\pi_k}$.

References

Andrea Rau, Gilles Celeux, Marie-Laure Martin-Magniette, and Cathy Maugis-Rabusseau (2011). Clustering high-throughput sequencing data with Poisson mixture models. Technical report RR-7786, Inria Saclay -- Ile-de-France.

Examples

Run this code

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 PMM-II model for g = {3, 4, 5}
## "TC" library size estimate, EM algorithm
run <- PoisMixClus(y, gmin = 3, gmax = 5, lib.size = TRUE,
    lib.type = "TC", conds = conds, init.type = "small-em")

## Visualization of results (not run):

## ICL results:
## plot(run, cluster.choice = "ICL")

## BIC results:
## plot(run, cluster.choice = "BIC")

## Results for cluster size = 5:
## plot(run, cluster.choice = 5)