tests/testthat/test-analyse.R

In TCGAbiolinks: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data

context("Analyse")

test_that("TCGAanalyze_survival creates pdf", {
    clin <- data.frame(
        vital_status = c(
            "alive",
            "alive",
            "alive",
            "dead",
            "alive",
            "alive",
            "dead",
            "alive",
            "dead",
            "alive"
        ),
        days_to_death = c(NA, NA, NA, 172, NA, NA, 3472, NA, 786, NA),
        days_to_last_follow_up = c(3011, 965, 718, NA, 1914, 423, NA, 5, 656, 1417),
        gender = c(rep("male", 5), rep("female", 5))
    )
    TCGAanalyze_survival(clin, clusterCol = "gender", filename = "test.pdf")
    expect_true(file.exists("test.pdf"))
    unlink("test.pdf")
})


test_that("TCGAanalyze_DMC ask for the missing parameters", {
    nrows <- 2
    ncols <- 20
    counts <- matrix(c(rep(0.9, 20), rep(0.1, 20)), nrows)
    rowRanges <- GenomicRanges::GRanges((rep("chr1", 2)),
                                        IRanges::IRanges(c(2000, 2000), width =
                                                             100),
                                        strand = c("+", "-"),
                                        feature_id = sprintf("ID%03d", 1:2)
    )
    colData <-
        S4Vectors::DataFrame(
            Treatment = rep(c("ChIP", "Input"), 5),
            row.names = LETTERS[1:20],
            group = rep(c(
                "group1", "group2", "group3", "group4"
            ), c(5, 5, 5, 5))
        )
    data <- SummarizedExperiment::SummarizedExperiment(
        assays = S4Vectors::SimpleList(counts = counts),
        rowRanges = rowRanges,
        colData = colData
    )
    expect_null(TCGAanalyze_DMC(data, p.cut = 0.85))
    expect_message(TCGAanalyze_DMC(data, p.cut = 0.85),
                   "Please, set the groupCol parameter")
    expect_null(TCGAanalyze_DMC(data, p.cut = 0.85, "group"))
    expect_message(
        TCGAanalyze_DMC(data, p.cut = 0.85, "group"),
        "Please, set the group1 and group2 parameters"
    )
})

test_that("TCGAanalyze_DMC is handling NAs correctly", {
    nrows <- 2
    ncols <- 20
    counts <- matrix(c(rep(0.9, 20), rep(0.1, 20)), nrows)
    counts[1, 1] <- NA
    rowRanges <- GenomicRanges::GRanges((rep("chr1", 2)),
                                        IRanges::IRanges(c(2000, 2000), width =
                                                             100),
                                        strand = c("+", "-"),
                                        feature_id = sprintf("ID%03d", 1:2)
    )
    colData <-
        S4Vectors::DataFrame(
            Treatment = rep(c("ChIP", "Input"), 5),
            row.names = LETTERS[1:20],
            group = rep(c("group1", "group2"), c(10, 10))
        )
    data <- SummarizedExperiment::SummarizedExperiment(
        assays = S4Vectors::SimpleList(counts = counts),
        rowRanges = rowRanges,
        colData = colData
    )
    SummarizedExperiment::colData(data)$group <-
        c(rep("group1", 10),  rep("group2", 10))
    hypo.hyper <-
        TCGAanalyze_DMC(data, p.cut = 0.85, "group", "group1", "group2")
    result <- hypo.hyper[1,]
    expect_equal(result$mean.group1, 0.9)
    expect_equal(result$mean.group2, 0.1)
    expect_equal(result$mean.group1.minus.mean.group2 , 0.8)
    expect_equal(result$status , "Hypermethylated in group1")

    counts[1, ] <- NA
    data <- SummarizedExperiment::SummarizedExperiment(
        assays = S4Vectors::SimpleList(counts = counts),
        rowRanges = rowRanges,
        colData = colData
    )
    expect_error(
        TCGAanalyze_DMC(data, p.cut = 0.85, "group", "group1", "group2"),
        "Sorry, but we found some probes with NA for all samples in your data, please either remove/or replace them"
    )
})

test_that(
    "Results of TCGAanalyze_DEA inverting groups changes signal and order of the signals are right",
    {
        dataNorm <-
            TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)
        dataFilt <-
            TCGAanalyze_Filtering(tabDF = dataBRCA,
                                  method = "quantile",
                                  qnt.cut =  0.25)

        # 5 samples
        samplesNT <-
            TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))

        # 5 samples
        samplesTP <-
            TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))

        # Get one line for example
        A <- rowMeans(dataFilt["CLDN6|9074", samplesNT])
        B <- rowMeans(dataFilt["CLDN6|9074", samplesTP])

        # Should give the same signal as  dataDEGs["CLDN6|9074",]
        log2FC <- log2(B) - log2(A)

        # Should give the same signal as  dataDEGs.inv["CLDN6|9074",]
        log2FC.inv <- log2(A) - log2(B)

        suppressMessages({
            dataDEGs <- TCGAanalyze_DEA(
                mat1 = dataFilt[, samplesNT],
                mat2 = dataFilt[, samplesTP],
                Cond1type = "Normal",
                Cond2type =  "Tumor"
            )
        })
        expect_equal(dataDEGs["CLDN6|9074", ]$logFC > 0, (log2FC > 0)[[1]])

        suppressMessages({
            dataDEGs.inv <- TCGAanalyze_DEA(
                mat1 = dataFilt[, samplesTP],
                mat2 = dataFilt[, samplesNT],
                Cond1type =  "Tumor",
                Cond2type = "Normal"
            )
        })
        expect_equal(dataDEGs$logFC, -1 * dataDEGs.inv$logFC)
        expect_equal(dataDEGs.inv["CLDN6|9074", ]$logFC > 0, (log2FC.inv > 0)[[1]])
        suppressMessages({
            dataDEGs <- TCGAanalyze_DEA(
                mat1 = dataFilt[, samplesNT],
                mat2 = dataFilt[, samplesTP],
                Cond1type = "Normal",
                Cond2type =  "Tumor",
                method = "glmLRT"
            )
        })
        expect_equal(dataDEGs["CLDN6|9074", ]$logFC > 0, (log2FC > 0)[[1]])

        suppressMessages({
            dataDEGs.inv <- TCGAanalyze_DEA(
                mat1 = dataFilt[, samplesTP],
                mat2 = dataFilt[, samplesNT],
                Cond1type =  "Tumor",
                Cond2type = "Normal",
                method = "glmLRT"
            )
        })
        expect_equal(dataDEGs$logFC, -1 * dataDEGs.inv$logFC)
        expect_equal(dataDEGs.inv["CLDN6|9074", ]$logFC > 0, (log2FC.inv > 0)[[1]])

    }
)

test_that("Results from TCGAanalyze_DMC are correct", {
    nrows <- 2
    ncols <- 20
    counts <- matrix(c(rep(0.9, 20), rep(0.1, 20)), nrows,
                     dimnames = list(paste0("cg", 1:2), LETTERS[1:20]))
    rowRanges <- GenomicRanges::GRanges((rep("chr1", 2)),
                                        IRanges::IRanges(c(2000, 2000), width =
                                                             100),
                                        strand = c("+", "-"),
                                        feature_id = sprintf("ID%03d", 1:2)
    )
    colData <-
        S4Vectors::DataFrame(
            Treatment = rep(c("ChIP", "Input"), 5),
            row.names = LETTERS[1:20],
            group = rep(c("group1", "group2"), c(10, 10))
        )
    data <- SummarizedExperiment::SummarizedExperiment(
        assays = S4Vectors::SimpleList(counts = counts),
        rowRanges = rowRanges,
        colData = colData
    )
    SummarizedExperiment::colData(data)$group <-
        c(rep("group1", 10),  rep("group2", 10))
    hypo.hyper <- TCGAanalyze_DMC(data, p.cut = 0.85, "group", "group1", "group2")
    result <- hypo.hyper[1,]
    expect_equal(result$mean.group1, 0.9)
    expect_equal(result$mean.group2, 0.1)
    expect_equal(result$mean.group1.minus.mean.group2 , 0.8)
    expect_equal(result$status, "Hypermethylated in group1")
})