library(ELMER.data) library(ELMER) library(DT) library(dplyr) library(BiocStyle)
This step identifies regulatory TF whose expression associates with TF binding motif
DNA methylation and it is carried out by function get.TFs
.
When a group of enhancers is coordinately altered in a specific sample subset, this is often the result of an altered upstream master regulator transcription factor in the gene regulatory network. ELMER identifies master regulator TFs corresponding to each of the TF binding motifs enriched from the previous analysis step. For each enriched motif, ELMER takes the mean DNA methylation of all distal probes (in significant probe-gene pairs) that contain that motif occurrence (within a $\pm 250bp$ region) and compares this mean DNA methylation to the expression of each gene annotated as a human TF.
In the Unsupervised
mode, a statistical test is performed for each motif-TF pair, as follows. All samples
are divided into two groups: the $M$ group, which consists
of the 20\% of samples with the highest average methylation at all motif-adjacent
probes, and the $U$ group, which consisted of the 20\% of samples with the lowest
methylation. This step is performed by the get.TFs
function, which takes minSubgroupFrac
as an input parameter, again with a default of 20\%.
For each candidate motif-TF pair, the Mann-Whitney U test is used to test
the null hypothesis that overall gene expression in group $M$ is greater or equal
than that in group $U$. This non-parametric test was used in order to minimize the
effects of expression outliers, which can occur across a very wide dynamic range.
For each motif tested, this results in a raw p-value ($P_r$) for each of the human TFs.
The new Supervised
mode uses the same approach as described for the identification of putative target gene(s) step. The $U$ and $M$ groups are one of the the label group of samples and the minSubgroupFrac
parameter is set to 100\% to use all samples from both groups in the statistical test. This also can result in greater statistical power when using the Supervised
mode.
Finally, all TFs were ranked by the $-log_{10}(P_{r})$, and those falling within the top 5\% of this ranking were considered candidate upstream regulators. By default, the top 3 most anti-correlated TFs, and all TF classified by TFClass database in the same (sub)family are highlighted.
[@yao2015inferring,@yao2015demystifying]
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. |
| group.col | A column defining the groups of the sample. You can view the available columns using: colnames(MultiAssayExperiment::colData(data))
. # Load results from previous sections mae <- get(load("mae.rda")) load("result/getMotif.hypo.enriched.motifs.rda")
## identify regulatory TF for the enriched motifs TF <- get.TFs(data = mae, group.col = "definition", group1 = "Primary solid Tumor", group2 = "Solid Tissue Normal", mode = "unsupervised", enriched.motif = enriched.motif, dir.out = "result", cores = 1, label = "hypo")
# get.TFs automatically save output files. # getTF.hypo.TFs.with.motif.pvalue.rda contains statistics for all TF with average # DNA methylation at sites with the enriched motif. # getTF.hypo.significant.TFs.with.motif.summary.csv contains only the significant probes. dir(path = "result", pattern = "getTF") # TF ranking plot based on statistics will be automatically generated. dir(path = "result/TFrankPlot/", pattern = "pdf")
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