tests/testthat/test-interaction_model.R

In MethReg: Assessing the regulatory potential of DNA methylation regions or sites on gene transcription

test_that("interaction_model handles 0 cases in more than 25% of samples with zeroinflated model", {

    dnam <- c(
        runif(10, min = 0,max = 0), # Q1
        runif(10, min = 1, max = 1) # Q4
    ) %>%  matrix(ncol = 1) %>%  t
    rownames(dnam) <- c("chr3:203727581-203728580")
    colnames(dnam) <- paste0("Samples",1:20)

    exp.target <-  c(
        runif(10, min = 0, max = 0),
        runif(10, min = 5, max = 5)
    ) %>%  matrix(ncol = 1) %>%  t
    rownames(exp.target) <- c("ENSG00000232886")
    colnames(exp.target) <- paste0("Samples",1:20)

    exp.tf <-  c(
        runif(5, min = 1, max = 1),
        runif(5, min = 0, max = 0),
        runif(5, min = 5, max = 5),
        runif(5, min = 0, max = 0)
    ) %>%  matrix(ncol = 1) %>%  t
    rownames(exp.tf) <- c("ENSG00000232888")
    colnames(exp.tf) <- paste0("Samples",1:20)

    exp <- rbind(exp.tf, exp.target)
    # Map example region to closest gene
    triplet <- data.frame(
        "regionID" =  c("chr3:203727581-203728580"),
        "target" = "ENSG00000232886",
        "TF" = "ENSG00000232888"
    )

    results <- interaction_model(
        triplet = triplet,
        dnam =  dnam,
        exp = exp,
        filter.correlated.tf.exp.dna = FALSE,
        sig.threshold = 1,
        fdr = FALSE
    )

    expect_equal(results$Model.quantile, "Zero-inflated Negative Binomial Model")
    expect_true("regionID" %in% colnames(results))
    expect_true("TF" %in% colnames(results))
    expect_true("target" %in% colnames(results))
    expect_true("quant_pval_metGrp" %in% colnames(results))
    expect_true("quant_pval_rna.tf" %in% colnames(results))
    expect_true("quant_pval_metGrp:rna.tf" %in% colnames(results))
    expect_true("quant_estimate_metGrp" %in% colnames(results))
    expect_true("quant_estimate_rna.tf" %in% colnames(results))
    expect_true("quant_estimate_metGrp:rna.tf" %in% colnames(results))
})


test_that("interaction_model performs rlm if no 0", {

    dnam <- c(
        runif(10,min = 0,max = 0), # Q1
        runif(10,min = 0.8, max = 1) # Q4
    ) %>%  matrix(ncol = 1) %>%  t
    rownames(dnam) <- c("chr3:203727581-203728580")
    colnames(dnam) <- paste0("Samples",1:20)

    exp.target <-  c(
        runif(20, min = 1, max = 5)
    ) %>%  matrix(ncol = 1) %>%  t
    rownames(exp.target) <- c("ENSG00000232886")
    colnames(exp.target) <- paste0("Samples",1:20)

    exp.tf <-  c(
        runif(20, min = 1, max = 5)
    ) %>%  matrix(ncol = 1) %>%  t
    rownames(exp.tf) <- c("ENSG00000232888")
    colnames(exp.tf) <- paste0("Samples",1:20)

    exp <- rbind(exp.tf, exp.target)
    # Map example region to closest gene
    triplet <- data.frame(
        "regionID" =  c("chr3:203727581-203728580"),
        "target" = "ENSG00000232886",
        "TF" = "ENSG00000232888"
    )

    results <- interaction_model(
        triplet = triplet,
        dnam =  dnam,
        exp = exp,
        filter.correlated.tf.exp.dna = FALSE,
        sig.threshold = 1,
        fdr = FALSE
    )

    expect_equal(results$Model.quantile, "Robust Linear Model")
    expect_true("regionID" %in% colnames(results))
    expect_true("TF" %in% colnames(results))
    expect_true("target" %in% colnames(results))
    expect_true("quant_pval_metGrp" %in% colnames(results))
    expect_true("quant_pval_rna.tf" %in% colnames(results))
    expect_true("quant_pval_metGrp:rna.tf" %in% colnames(results))
    expect_true("quant_estimate_metGrp" %in% colnames(results))
    expect_true("quant_estimate_rna.tf" %in% colnames(results))
    expect_true("quant_estimate_metGrp:rna.tf" %in% colnames(results))
})