ELMER (version 1.4.2)

get.TFs: get.TFs to identify regulatory TFs.

Description

get.TFs is a function to identify regulatory TFs based on motif analysis and association analysis between the probes containing a particular motif and expression of all known TFs. If save is ture, two files will be saved: getTF.XX.significant.TFs.with.motif.summary.csv and getTF.hypo.TFs.with.motif.pvalue.rda (see detail).

Usage

get.TFs(mee, enriched.motif, TFs, motif.relavent.TFs, percentage = 0.2, 
dir.out = "./", label = NULL, cores = NULL,save=TRUE)

Arguments

mee

A MEE.data object contains at least meth, exp, probeInfo, geneInfo.

enriched.motif

A list containing output of get.enriched.motif function or a path of XX.rda file containing output of get.enriched.motif function.

TFs

A data.frame containing TF GeneID and Symbol or a path of XX.csv file containing TF GeneID and Symbol. If missing, human.TF list will be used (human.TF data in ELMER.data). For detail information, refer the reference paper.

motif.relavent.TFs

A list containing motif as names and relavent TFs as contents for each list element or a path of XX.rda file containing a list as above. If missing, motif.relavent.TFs will be used (motif.relavent.TFs data in ELMER.data). For detail information, refer the reference paper.

percentage

A number ranges from 0 to 1 specifying the percentage of samples of control and experimental groups used to link probes to genes. Default is 0.2.

dir.out

A path specifies the directory for outputs of get.pair function. Default is current directory

cores

A interger which defines the number of cores to be used in parallel process. Default is NULL: no parallel process.

label

A character labels the outputs.

save

A logic. If save is ture, two files will be saved: getTF.XX.significant.TFs.with.motif.summary.csv and getTF.hypo.TFs.with.motif.pvalue.rda (see detail). If save is false, a data frame contains the same content with the first file.

Value

• Potential responsible TFs will be reported.

Details

save: If save is ture, two files will be saved. The first file is getTF.XX.significant.TFs.with.motif.summary.csv (XX depends on option lable). This file contain the regulatory TF significantly associate with average DNA methylation at particular motif sites. The second file is getTF.hypo.TFs.with.motif.pvalue.rda (XX depends on option label). This file contains a matrix storing the statistic results for significant associations between TFs (row) and average DNA methylation at motifs (column). If save is false, a data frame which contains the same content with the first file will be reported.

References

Yao L, Shen H, Laird PW, Farnham PJ,Berman BP: Inferring Regulatory Element Landscapes and Transcription Factor Networks from Cancer Methylomes. in revision of Genome Biology

Examples

load(system.file("extdata","mee.example.rda",package = "ELMER"))
enriched.motif <- list("TP53"= c("cg00329272", "cg10097755", "cg08928189",
                                 "cg17153775", "cg21156590", "cg19749688", "cg12590404",
                                 "cg24517858", "cg00329272", "cg09010107", "cg15386853",
                                 "cg10097755", "cg09247779", "cg09181054"))
TF <- get.TFs(mee, enriched.motif,
              TFs=data.frame(GeneID=c("ID7157","ID8626","ID7161"),
              Symbol=c("TP53","TP63","TP73"),
              stringsAsFactors = FALSE),
              label="hypo")