# PoisMixMean: Calculate the conditional per-cluster mean of each...

### Description

This function is used to calculate the conditional per-cluster mean expression for all observations. This value corresponds to μ = (μ_{ijlk}) = (\hat{w}_i \hat{λ}_{jk}) for the PMM-I model and μ = (μ_{ijlk}) = (\hat{w}_i s_{jl}\hat{λ}_{jk}) for the PMM-II model.

### Usage

 1 PoisMixMean(y, g, conds, s, lambda) 

### Arguments

 y (n x q) matrix of observed counts for n observations and q variables g Number of clusters conds Vector of length q defining the condition (treatment group) for each variable (column) in y s Estimate of normalized per-variable library size lambda (d x g) matrix containing the current estimate of lambda, where d is the number of conditions (treatment groups) and g is the number of clusters

### Value

A list of length g containing the (n x q) matrices of mean expression for all observations, conditioned on each of the g clusters

### Author(s)

Andrea Rau <andrea.rau@jouy.inra.fr>

### References

Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux G. (2015). Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, 31(9):1420-1427.

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.

PoisMixClus for Poisson mixture model estimation and model selection

### Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 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, norm = "TC", conds = conds) pi.est <- run$pi lambda.est <- run$lambda ## Calculate the per-cluster mean for each observation means <- PoisMixMean(y, g = 3, conds, s, lambda.est) 

Search within the HTSCluster package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.