NormMixClus: Normal mixture model estimation and selection for a series of...
In andreamrau/coseq: Co-Expression Analysis of Sequencing Data

Description Usage Arguments Value Author(s) Examples

Perform co-expression and co-abudance analysis of high-throughput sequencing data, with or without data transformation, using a Normal mixture models. The output of NormMixClus is an S4 object of class RangedSummarizedExperiment.

NormMixClus(
  y_profiles,
  K,
  subset = NULL,
  parallel = TRUE,
  BPPARAM = bpparam(),
  seed = NULL,
  ...
)

`y_profiles`	(n x q) matrix of observed profiles for n observations and q variables
`K`	Number of clusters (a single value or a sequence of values).
`subset`	Optional vector providing the indices of a subset of genes that should be used for the co-expression analysis (i.e., row indices of the data matrix `y`.
`parallel`	If `FALSE`, no parallelization. If `TRUE`, parallel execution using BiocParallel (see next argument `BPPARAM`). A note on running in parallel using BiocParallel: it may be advantageous to remove large, unneeded objects from the current R environment before calling the function, as it is possible that R's internal garbage collection will copy these files while running on worker nodes.
`BPPARAM`	Optional parameter object passed internally to `bplapply` when `parallel=TRUE`. If not specified, the parameters last registered with `register` will be used.
`seed`	If desired, an integer defining the seed of the random number generator. If `NULL`, a random seed is used.
`...`	Additional optional parameters to be passed to `NormMixClusK`.

An S4 object of class coseqResults, with conditional probabilities of cluster membership for each gene in each model stored as a list of assay data, and corresponding log likelihood, ICL value, number of clusters, and form of Gaussian model for each model stored as metadata.

Andrea Rau, Cathy Maugis-Rabusseau

## Simulate toy data, n = 300 observations
set.seed(12345)
countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4)
countmat <- countmat[which(rowSums(countmat) > 0),]
profiles <- transformRNAseq(countmat, norm="none",
                            transformation="arcsin")$tcounts

conds <- rep(c("A","B","C","D"), each=2)

## Run the Normal mixture model for K = 2,3
## Object of class coseqResults
run <- NormMixClus(y=profiles, K=2:3, iter=5)
run

## Run the Normal mixture model for K=2
## Object of class SummarizedExperiment0
run2 <- NormMixClusK(y=profiles, K=2, iter=5)

## Summary of results
summary(run)

## Re-estimate mixture parameters for the model with K=2 clusters
param <- NormMixParam(run, y_profiles=profiles)