tests/testthat/test_ER_level.R

In lcolladotor/derfinder: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach

context("ER-level approach")

## Create some toy data
library("IRanges")
set.seed(20160606)
x <- Rle(round(runif(1e4, max = 10)))
y <- Rle(round(runif(1e4, max = 10)))
z <- Rle(round(runif(1e4, max = 10)))
fullCov <- list("chr21" = DataFrame(x, y, z))

## Calculate a proxy of library size
libSize <- sapply(fullCov$chr21, sum)

## Run region matrix normalizing the coverage
regionMat <- regionMatrix(
    fullCov = fullCov, maxRegionGap = 10L,
    maxClusterGap = 300L, L = 36, totalMapped = libSize, targetSize = 4e4
)

## You can alternatively use filterData() on fullCov to reduce the required
## memory before using regionMatrix(). This can be useful when mc.cores > 1
filteredCov <- lapply(fullCov, filterData,
    returnMean = TRUE, filter = "mean",
    cutoff = 5, totalMapped = libSize, targetSize = 4e4
)
regionMat2 <- regionMatrix(filteredCov,
    maxRegionGap = 10L,
    maxClusterGap = 300L, L = 36, runFilter = FALSE
)

regionMat3 <- regionMatrix(
    fullCov = fullCov, maxRegionGap = 10L,
    maxClusterGap = 30000L, L = 36, totalMapped = libSize, targetSize = 4e4
)

## fullCov with position info
fullCov2 <- list(
    "chr21" =
        list("coverage" = DataFrame(x, y, z), "position" = NULL)
)
regionMat4 <- regionMatrix(
    fullCov = fullCov2, maxRegionGap = 10L,
    maxClusterGap = 300L, L = 36, totalMapped = libSize, targetSize = 4e4
)

## fullCov with non-NULL position info
fullCov3 <- list(
    "chr21" =
        list("coverage" = DataFrame(x, y, z), "position" = Rle(rep(TRUE, 1e4)))
)
regionMat5 <- regionMatrix(
    fullCov = fullCov3, maxRegionGap = 10L,
    maxClusterGap = 300L, L = 36, totalMapped = libSize, targetSize = 4e4
)


## Filter
filtered <- filterData(fullCov3[[1]]$coverage, index = fullCov3[[1]]$position, returnMean = TRUE, cutoff = 5)

test_that("regionMatrix", {
    expect_equal(regionMat, regionMat2)
    expect_equal(
        sum(regionMat3$chr21$regions$cluster == 1),
        length(regionMat3$chr21$regions)
    )
    expect_equal(regionMat, regionMat4)
    expect_equal(regionMat, regionMat5)
    expect_lt(length(filtered$meanCoverage), 1e4)
    expect_equal(length(filtered$meanCoverage), sum(filtered$position))
})

if (.Platform$OS.type != "windows") {
    ## Get data
    library("derfinderData")

    ## Identify sample files
    sampleFiles <- rawFiles(system.file("extdata", "AMY",
        package =
            "derfinderData"
    ), samplepatt = "bw", fileterm = NULL)
    names(sampleFiles) <- gsub(".bw", "", names(sampleFiles))

    ## Create the mean bigwig file. This file is normally created by Rail
    ## but in this example we'll create it manually.
    library("GenomicRanges")
    railCov <- fullCoverage(files = sampleFiles, chrs = "chr21")
    meanCov <- Reduce("+", railCov$chr21) / ncol(railCov$chr21)
    createBw(list("chr21" = DataFrame("meanChr21" = meanCov)),
        keepGR =
            FALSE
    )

    summaryFile <- "meanChr21.bw"

    ## Get the regions
    railMat <- railMatrix(
        chrs = "chr21", summaryFiles = summaryFile,
        sampleFiles = sampleFiles, L = 76, cutoff = 5.1, maxClusterGap = 3000L
    )

    ## Changing chunksize
    railMat2 <- railMatrix(
        chrs = "chr21", summaryFiles = summaryFile,
        sampleFiles = sampleFiles, L = 76, cutoff = 5.1, maxClusterGap = 3000L,
        chunksize = 100
    )

    ## Changing maxClusterGap
    railMat3 <- railMatrix(
        chrs = "chr21", summaryFiles = summaryFile,
        sampleFiles = sampleFiles, L = 76, cutoff = 5.1, maxClusterGap = 1e6
    )

    ## Reproducing results with regionMatrix
    railMat4 <- regionMatrix(
        fullCov = railCov, L = 76, cutoff = 5.1,
        maxClusterGap = 3000L
    )

    ## Smoothing
    meanCov2 <- meanCov[9820000:9830000, ]
    createBw(list("chr21" = DataFrame("mean2Chr21" = meanCov2)),
        keepGR =
            FALSE
    )
    summaryFile2 <- "mean2Chr21.bw"

    ## First with the less memory intensive of the two methods
    railMat5 <- railMatrix(
        chrs = "chr21", summaryFiles = summaryFile2,
        sampleFiles = sampleFiles, L = 76, cutoff = 5.1, maxClusterGap = 3000L,
        smoothMean = TRUE, smoothFunction = bumphunter::loessByCluster
    )

    ## Next with the more memory intensive one
    railMat6 <- railMatrix(
        chrs = "chr21", summaryFiles = summaryFile2,
        sampleFiles = sampleFiles, L = 76, cutoff = 5.1, maxClusterGap = 3000L,
        smoothMean = TRUE, minInSpan = 76, minNum = 76, bpSpan = 300
    )

    ## Reproducing results with regionMatrix
    railCov2 <- railCov
    railCov2$chr21 <- railCov2$chr21[9820000:9830000, ]

    ## Note that railMat 5 and 6 don't have the correct coverageMatrix since
    ## the sampleFiles were not changed, only the mean was changed.
    railMat7 <- regionMatrix(
        fullCov = railCov2, L = 76, cutoff = 5.1,
        maxClusterGap = 3000L, smoothMean = TRUE,
        smoothFunction = bumphunter::loessByCluster, returnBP = FALSE
    )


    test_that("railMatrix", {
        expect_equal(railMat, railMat2)
        expect_lt(max(railMat3$chr21$regions$cluster), max(railMat$chr21$regions$cluster))
        expect_equivalent(railMat$chr21$regions, railMat4$chr21$regions)
        expect_equal(railMat$chr21$coverageMatrix, railMat4$chr21$coverageMatrix, tolerance = 0.005)
        expect_lt(railMat5$chr21$regions$value, railMat$chr21$regions[1]$value)
        expect_lt(railMat6$chr21$regions$value, railMat5$chr21$regions$value)
        expect_equal(abs(railMat6$chr21$regions$value * width(railMat6$chr21$regions)), railMat6$chr21$regions$area)
        expect_equal(railMat5$chr21$regions, railMat7$chr21$regions, tolerance = 0.0000001)
    })

    unlink("mean2Chr21.bw")
    unlink("meanChr21.bw")
}