NormMixClusK {coseq} | R Documentation

## Normal mixture model estimation

### Description

Perform co-expression and co-abudance analysis of high-throughput sequencing data, with or without data transformation, using a Normal mixture models for single number of clusters K. The output of `NormMixClusK` is an S4 object of class `RangedSummarizedExperiment`.

### Usage

    NormMixClusK(
      y_profiles,
      K,
      alg.type = "EM",
      init.runs = 50,
      init.type = "small-em",
      GaussianModel = "Gaussian_pk_Lk_Ck",
      init.iter = 20,
      iter = 1000,
      cutoff = 0.001,
      verbose = TRUE,
      digits = 3,
      seed = NULL
    )

### Arguments

`y_profiles` | y (n x q) matrix of observed profiles for n observations and q variables
`K` | Number of clusters (a single value).
`alg.type` | Algorithm to be used for parameter estimation: “`EM`”, “`CEM`”, “`SEM`”
`init.runs` | Number of runs to be used for the Small-EM strategy, with a default value of 50
`init.type` | Type of initialization strategy to be used: “`small-em`” for the Small-EM strategy, “`random`”, “`CEM`”, or “`SEMMax`”
`GaussianModel` | One of the 28 forms of Gaussian models defined in Rmixmod, by default equal to the `"Gaussian_pk_Lk_Ck"` (i.e., a general family model with free proportions, free volume, free shape, and free orientation)
`init.iter` | Number of iterations to be used within each run for the Small-EM strategry, with a default value of 20
`iter` | Maximum number of iterations to be run for the chosen algorithm
`cutoff` | Cutoff to declare algorithm convergence
`verbose` | If `TRUE`, verbose output is created
`digits` | Integer indicating the number of decimal places to be used for the `probaPost` output
`seed` | If desired, an integer defining the seed of the random number generator. If `NULL`, a random seed is used.

### Value

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

### Author(s)

Cathy Maugis-Rabusseau, Andrea Rau

### Examples

    ## 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)

[Package coseq version 1.18.0 Index]