clusterTimeProfiles: Clustering of time profiles.
In pwOmics: Pathway-based data integration of omics data

Description Usage Arguments Value References Examples

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.

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

`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.

output dataframe of mfuzz function.

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.

## 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)