summary.HTSCluster: Summarize results from clustering using a Poisson mixture model

Description

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

Usage

## S3 method for class 'HTSCluster':
summary(object, cluster.choice = "ICL", ...)

Arguments

object

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

...

Additional arguments

Details

Provides the following summary of results: 1) Selected number of clusters (for all cluster.choice) and BIC or ICL values for cluster.choice = "BIC" and cluster.choice = "ICL", respectively. 2) Number of observations across all clusters with a maximum conditional probability greater than 90model. 3) Number of observations per cluster with a maximum conditional probability greater than 90selected model. 4) $\ensuremath\boldsymbol{\lambda}$ values for the selected model. 5) $\ensuremath\boldsymbol{\pi}$ values for the selected model.

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")

## Summary of results:

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

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

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