NormMixClus: Normal mixture model estimation and selection for a series of...

Description Usage Arguments Value Author(s) Examples

View source: R/NormMixClus-functions.R

Description

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.

Usage

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

Arguments

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.

Value

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.

Author(s)

Andrea Rau, Cathy Maugis-Rabusseau

Examples

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## 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)

andreamrau/coseq documentation built on July 25, 2021, 10:17 a.m.