get.enriched.motif {ELMER} | R Documentation

## get.enriched.motif to identify the overrepresented motifs in a set of probes (HM450K) regions.

### Description

get.enriched.motif is a function make use of Probes.motif data from ELMER.data package to calculate the motif enrichment Odds Ratio and 95% confidence interval for a given set of probes using fisher test function, after performing the Fisher's exact test, the results for all transcription factors are corrected for multiple testing with the Benjamini-Hochberg procedure. If save is TURE, two output files will be saved: getMotif.XX.enriched.motifs.rda and getMotif.XX.motif.enrichment.csv (see detail).

### Usage

    get.enriched.motif(data, probes.motif, probes, min.motif.quality = "DS",
                       background.probes,  pvalue = 0.05, lower.OR = 1.1, min.incidence = 10,
                       dir.out = "./", label = NULL, save = TRUE, plot.title=NULL)

### Arguments

`data` | A multi Assay Experiment from `createMAE` function. If set and probes.motif/background probes are missing this will be used to get this other two arguments correctly. This argument is not require, you can set probes.motif and the backaground.probes manually.
`probes.motif` | A matrix contains motifs occurrence within probes regions. Probes.motif in ELMER.data will be used if probes.motif is missing (detail see Probes.motif.hg19.450K in ELMER.data).
`probes` | A vector lists the name of probes to define the set of probes in which motif enrichment OR and confidence interval will be calculated.
`min.motif.quality` | Minimum motif quality score to consider. Possible valules: A, B, C , D, AS (A and S), BS (A, B and S), CS (A, B , C and S), DS (all - default) Description: Each PWM has a quality rating from A to D where A represents motifs with the highest confidence, and D motifs only weakly describe the pattern with a limited applications for quantitative analyses. Special S quality marks the single-box motifs (secondary motif). Source: http://hocomoco.autosome.ru/help#description_quality_score More information: http://nar.oxfordjournals.org/content/44/D1/D116.full#sec-8
`background.probes` | A vector lists name of probes which are considered as background for motif.enrichment calculation (see detail).
`pvalue` | FDR P-value cut off (default 0.05)
`lower.OR` | A number specifies the smallest lower boundary of 95% confidence interval for Odds Ratio. The motif with higher lower boudnary of 95% confidence interval for Odds Ratio than the number are the significantly enriched motifs (detail see reference).
`min.incidence` | A non-negative integer specifies the minimum incidence of motif in the given probes set. 10 is default.
`dir.out` | A path. Specifies the directory for outputs. Default is current directory
`label` | A character. Labels the outputs such as "hypo", "hyper"
`save` | If save is TURE, two files will be saved: getMotif.XX.enriched.motifs.rda and getMotif.XX.motif.enrichment.csv (see detail).
`plot.title` | Plot title. Default: no title.

### Details

background.probes: For enhancer study, it is better to use probes within distal enhancer probes as background.probes. For promoter study, it is better to use probes within promoter regions as background.probes. Because enhancer and promoter have different CG content and harbors different clusters of TFs motif.

save: if save is TRUE, two files will be save on the disk. The first file is getMotif.XX.motif.enrichment.csv (XX depends on option label). This file reports the Odds Ratio and 95% confidence interval for these Odds Ratios which pass the significant cutoff (lower.OR and min.incidence). The second file is getMotif.XX.enriched.motifs.rda (XX depends on option lable). This file contains a list R object with enriched motifs as name and probes containing the enriched motif as contents. This object will be used in `get.TFs` function. if save is FALSE, the function will return a R object which is the same with second file.

### Value

A list contains enriched motifs with the probes regions harboring the motif.

A list (R object) with enriched motifs as name and probes containing the enriched motif as contents. And hypo.motif.enrichment.pdf plot will be generated.

### Author(s)

Lijing Yao (creator: lijingya@usc.edu)

### References

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

### Examples

    probes <- c("cg00329272","cg10097755","cg08928189", "cg17153775","cg21156590",
    "cg19749688","cg12590404","cg24517858","cg00329272","cg09010107",
    "cg15386853", "cg10097755", "cg09247779","cg09181054","cg19371916")
      data <- tryCatch(ELMER:::getdata("elmer.data.example"), error = function(e) {
      message(e)
      data(elmer.data.example, envir = environment())
      })
    bg <- rownames(getMet(data))
    data(Probes.motif.hg38.450K,package = "ELMER.data")
    enriched.motif <- get.enriched.motif(probes.motif = Probes.motif.hg38.450K,
                                         probes = probes,
                                         background.probes = bg,
                                         pvalue = 1,
                                         min.incidence = 2,
                                         label = "hypo")
    # If the MAE is set, the background and the probes.motif will
    # be automatically set
    enriched.motif <- get.enriched.motif(data = data,
                                         min.motif.quality = "DS",
                                         probes=probes,
                                         pvalue = 1,
                                         min.incidence=2,
                                         label="hypo")

[Package ELMER version 2.4.4 Index]