probaPost: Calculate the conditional probability of belonging to each cluster in a Poisson mixture model

Description

This function computes the conditional probabilities $t_{ik}$ that an observation i arises from the $k^{\mathrm{th}}$ component for the current value of the mixture parameters.

Usage

probaPost(y, g, conds, pi, s, lambda)

Arguments

(n x q) matrix of observed counts for n observations and q variables

Number of clusters

conds

Vector of length q defining the condition (treatment group) for each variable (column) in y

Vector of length g containing the current estimate of $\hat{\ensuremath\boldsymbol{\pi}}$

Vector of length q containing the estimates for the normalized library size parameters for each of the q variables in y

lambda

(d x g) matrix containing the current estimate $\ensuremath\boldsymbol{\lambda}$, where d is the number of conditions (treatment groups)

Value

t(n x g) matrix made up of the conditional probability of each observation belonging to each of the g clusters

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 = 200 observations

simulate <- PoisMixSim(n = 200, libsize = "A", separation = "high")
y <- simulate$y
conds <- simulate$conditions
s <- colSums(y) / sum(y)     ## TC estimate of lib size

## 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) 
pi.est <- run$pi
lambda.est <- run$lambda

## Calculate the conditional probability of belonging to each cluster
proba <- probaPost(y, g = 3, conds = conds, pi = pi.est, s = s, 
	lambda = lambda.est)

## head(round(proba,2))