plot_interaction_model {MethReg} | R Documentation

## Plot interaction model results

### Description

Create several plots to show interaction data TF expression with target gene interaction using a linear model

log2(RNA target) = log2(TF) + DNAm + log2(TF) * DNAm

To consider covariates, RNA can also be the residuals.

log2(RNA target residuals) = log2(TF residual) + DNAm + log2(TF residual) * DNAm

### Usage

    plot_interaction_model(
      triplet.results,
      dnam,
      exp,
      metadata,
      tf.activity.es = NULL,
      tf.dnam.classifier.pval.thld = 0.001,
      label.dnam = "beta-value",
      label.exp = "expression",
      genome = "hg38",
      add.tf.vs.exp.scatter.plot = FALSE
    )

### Arguments

`triplet.results` | Output from function interaction_model with Region ID, TF (column name: TF), and target gene (column name: target), p-values and estimates of interaction
`dnam` | DNA methylation matrix or SummarizedExperiment object (columns: samples same order as met, rows: regions/probes)
`exp` | gene expression matrix or a SummarizedExperiment object (columns: samples same order as met, rows: genes)
`metadata` | A data frame with samples as rownames and one columns that will be used to color the samples
`tf.activity.es` | A matrix with normalized enrichment scores for each TF across all samples to be used in linear models instead of TF gene expression.
`tf.dnam.classifier.pval.thld` | P-value threshold to consider a linear model significant of not. Default 0.001. This will be used to classify the TF role and DNAm effect.
`label.dnam` | Used for label text. Option "beta-value" and "residuals"
`label.exp` | Used for label text. Option "expression" and "residuals"
`genome` | Genome of reference to be added to the plot as text
`add.tf.vs.exp.scatter.plot` | Add another row to the figure if the target gene expression vs TF expression stratified by DNA methylation groups (DNAmLow - low quartile, DNAmHigh - high quartile)

### Value

A ggplot object, includes a table with results from fitting interaction model, and the the following scatter plots: 1) TF vs DNAm, 2) Target vs DNAm, 3) Target vs TF, 4) Target vs TF for samples in Q1 and Q4 for DNA methylation, 5) Target vs DNAm for samples in Q1 and Q4 for the TF

### Examples

    library(dplyr)
    dnam <- runif(20, min = 0,max = 1) %>% sort %>%
      matrix(ncol = 1) %>%  t
    rownames(dnam) <- c("chr3:203727581-203728580")
    colnames(dnam) <- paste0("Samples",1:20)

    exp.target <-  runif(20,min = 0,max = 10) %>% sort %>%
      matrix(ncol = 1) %>%  t
    rownames(exp.target) <- c("ENSG00000232886")
    colnames(exp.target) <- paste0("Samples",1:20)

    exp.tf <- runif(20,min = 0,max = 10) %>%
      matrix(ncol = 1) %>%  t
    rownames(exp.tf) <- c("ENSG00000101412")
    colnames(exp.tf) <- paste0("Samples",1:20)

    exp <- rbind(exp.tf, exp.target)

    triplet <- data.frame(
       "regionID" =  c("chr3:203727581-203728580"),
       "target" = "ENSG00000232886",
       "TF" = "ENSG00000101412"
    )

    results <- interaction_model(
      triplet = triplet,
      dnam = dnam,
      exp = exp,
      fdr = FALSE,
      filter.correlated.tf.exp.dna = FALSE,
      stage.wise.analysis = FALSE
    )
    plots <- plot_interaction_model(
        triplet.results = results,
        dnam = dnam,
        exp = exp
    )

[Package MethReg version 1.4.0 Index]