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, ...)
## S3 method for class 'HTSClusterWrapper':
summary(object, ...)

Arguments

object

An object of class "HTSCluster" or "HTSClusterWrapper"

...

Additional arguments

Details

The summary function for an object of class "HTSCluster" provides the following summary of results: 1) Number of clusters and model selection criterion used, if applicable. 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.

The summary function for an object of class "HTSClusterWrapper" provides the number of clusters selected for the BIC, ICL, DDSE, and Djump model selection approaches.

References

Rau, A., Celeux, G., Martin-Magniette, M.-L., Maugis-Rabusseau, C. (2011). Clustering high-throughput sequencing data with Poisson mixture models. Inria Research Report 7786. Available at http://hal.inria.fr/inria-00638082.

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
## "TC" library size estimate, EM algorithm
run <- PoisMixClus(y, g = 3, lib.size = TRUE,
    lib.type = "TC", conds = conds, init.type = "small-em")

## Summary of results:
summary(run)