clusterTimeProfiles {pwOmics} | R Documentation

## Clustering of time profiles.

### Description

Soft clustering of time series data with Mfuzz R package [1]. Filtering of genes with low expression changes possible via min.std parameter. Expression values are standardized and undergo fuzzy c-means clustering based on minimization of weighted square error function (see [2]). Fuzzifier parameter m is estimated via mestimate function of [1] based on a relation proposed by Schwaemmle and Jansen [3]. The optimal number of clusters is determined via the minimum distance between cluster centroid using Dmin function of [3]. Be aware that the cluster number determination might be difficult especially for short time series measurements.

### Usage

    clusterTimeProfiles(dynConsensusNet, min.std = 0, ncenters = 12)

### Arguments

`dynConsensusNet` | result of dynamic analysis: inferred net generated by consDynamicNet function.
`min.std` | threshold parameter to exclude genes with a low standard deviation. All genes with an expression smaller than min.std will be excluded from clustering. Default value is 0.
`ncenters` | integer specifying the maximum number of centers which should be tested in minimum distance between cluster centroid test; this number is used as initial number to determine the data-specific maximal cluster number based on number of distinct data points.

### Value

output dataframe of mfuzz function.

### References

1. L. Kumar and M. Futschik, Mfuzz: a software package for soft clustering of microarray data, Bioinformation, 2(1) 5-7, 2007.

2. Bezdak JC, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.

3. Schwaemmle and Jensen, Bioinformatics, Vol. 26 (22), 2841-2848, 2010.

### Examples

    ## Not run:
    data(OmicsExampleData)
    data_omics = readOmics(tp_prots = c(0.25, 1, 4, 8, 13, 18, 24),
    tp_genes = c(1, 4, 8, 13, 18, 24), OmicsExampleData,
    PWdatabase = c("biocarta", "kegg", "nci", "reactome"),
    TFtargetdatabase = c("userspec"))
    data_omics = readPhosphodata(data_omics,
    phosphoreg = system.file("extdata", "phospho_reg_table.txt",
    package = "pwOmics.newupdown"))
    data_omics = readTFdata(data_omics,
    TF_target_path = system.file("extdata", "TF_targets.txt",
    package = "pwOmics.newupdown"))
    data_omics_plus = readPWdata(data_omics,
    loadgenelists = system.file("extdata/Genelists", package = "pwOmics.newupdown"))

    ## End(Not run)
    ## Not run:
    data_omics_plus = identifyPR(data_omics_plus)
    setwd(system.file("extdata/Genelists", package = "pwOmics.newupdown"))
    data_omics = identifyPWs(data_omics_plus)
    data_omics = identifyTFs(data_omics)
    data_omics = identifyRsofTFs(data_omics,
    noTFs_inPW = 1, order_neighbors = 10)
    data_omics = identifyPWTFTGs(data_omics)
    statConsNet = staticConsensusNet(data_omics)
    consDynNet = consDynamicNet(data_omics, statConsNet)
    clusterTimeProfiles(consDynNet)

    ## End(Not run)

[Package pwOmics version 1.26.0 Index]