get.TFs {ELMER} | R Documentation

## 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 true, 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(data,
              enriched.motif,
              TFs,
              group.col,
              group1,
              group2,
              mode = "unsupervised",
              correlation = "negative",
              diff.dir = NULL,
              motif.relevant.TFs,
              minSubgroupFrac = 0.4,
              dir.out = "./",
              label = NULL,
              save.plots = FALSE,
              cores = 1,
              topTFper = 0.05,
              save = TRUE)

### Arguments

`data` | A multiAssayExperiment with DNA methylation and Gene Expression data. See `createMAE` function.
`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.
`group.col` | A column defining the groups of the sample. You can view the available columns using: colnames(MultiAssayExperiment::colData(data)).
`group1` | A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2.
`group2` | A group from group.col. ELMER will run group1 vs group2. That means, if direction is hyper, get probes hypermethylated in group 1 compared to group 2.
`mode` | A character. Can be "unsupervised" or "supervised". If unsupervised is set the U (unmethylated) and M (methylated) groups will be selected among all samples based on methylation of each probe. Otherwise U group and M group will set as the samples of group1 or group2 as described below: If diff.dir is "hypo, U will be the group 1 and M the group2. If diff.dir is "hyper" M group will be the group1 and U the group2.
`correlation` | Type of correlation to evaluate (negative or positive). Negative checks if hypomethylated is upregulated. Positive if hypermethylated is upregulated.
`diff.dir` | A character can be "hypo" or "hyper", showing differential methylation direction in group 1. It can be "hypo" which means the probes are hypomethylated in group1; "hyper" which means the probes are hypermethylated in group1; This argument is used only when mode is supervised nad it should be the same value from get.diff.meth function.
`motif.relevant.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.
`minSubgroupFrac` | A number ranging from 0 to 1 specifying the percentage of samples used to create the groups U (unmethylated) and M (methylated) used to link probes to TF expression. Default is 0.4 (lowest quintile of all samples will be in the U group and the highest quintile of all samples in the M group).
`dir.out` | A path specifies the directory for outputs of get.pair function. Default is current directory
`label` | A character labels the outputs.
`save.plots` | Create TF ranking plots ?
`cores` | A interger which defines the number of cores to be used in parallel process. Default is 1: no parallel process.
`topTFper` | Top ranked TF to be retrieved (default "0.05" - 5 percent)
`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.

### 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.

### Value

Potential responsible TFs will be reported in a dataframe with 4 columns:

- motif: the names of motif.

- top.potential.TF.family: the highest ranking upstream TFs which are known recognized the motif. First item in potential.TFs.family

- top.potential.TF.subfamily: the highest ranking upstream TFs which are known recognized the motif. First item in potential.TFs.subfamily

- potential.TFs.family: TFs which are within top 5% list and are known recognized the motif (considering family classification).

- potential.TFs.subfamily: TFs which are within top 5% list and are known recognized the motif (considering subfamily classification).

- top_5percent: all TFs which are within top 5% list.

### Author(s)

Lijing Yao (creator: lijingya@usc.edu) Tiago C Silva (maintainer: tiagochst@usp.br)

### References

Yao, Lijing, et al. "Inferring regulatory element landscapes and transcription factor networks from cancer methylomes." Genome biology 16.1 (2015): 1.

### Examples

    data <- tryCatch(
      ELMER:::getdata("elmer.data.example"),
      error = function(e) {
        message(e)
         data(elmer.data.example, envir = environment())
      })
    enriched.motif <- list("P53_HUMAN.H11MO.1.A"= c("cg00329272", "cg10097755", "cg08928189",
                                     "cg17153775", "cg21156590", "cg19749688", "cg12590404",
                                     "cg24517858", "cg00329272", "cg09010107", "cg15386853",
                                     "cg10097755", "cg09247779", "cg09181054"))
    TF <- get.TFs(data,
                  enriched.motif,
                  group.col = "definition",
                  group1 = "Primary solid Tumor",
                  group2 = "Solid Tissue Normal",
                  TFs = data.frame(
                         external_gene_name=c("TP53","TP63","TP73"),
                         ensembl_gene_id= c("ENSG00000141510",
                                            "ENSG00000073282",
                                            "ENSG00000078900"),
                         stringsAsFactors = FALSE),
                 label="hypo")
    # This case will use Uniprot dabase to get list of Trasncription factors
    TF <- get.TFs(data,
                  group.col = "definition",
                  group1 = "Primary solid Tumor",
                  group2 = "Solid Tissue Normal",
                  enriched.motif,
                  label="hypo")

[Package ELMER version 2.12.0 Index]