Package 'mesa'

Description

Scan BAM files listed in a qseaSet and add per-region counts and library summaries using proper pairs (with MAPQ and insert-size filters) and, optionally, high-quality unpaired R1 reads extended to a fragment length. Parallelisation via BiocParallel is supported.

Usage

addBamCoveragePairedAndUnpaired(
  qs,
  fragmentLength = NULL,
  minMapQual = 30,
  maxInsertSize = 1000,
  minInsertSize = 100,
  minReferenceLength = 30,
  parallel = getMesaParallel(),
  properPairsOnly = FALSE
)

Arguments

Details

Per-region counts are computed from fragment midpoints to align with qsea’s counting scheme. When properPairsOnly = FALSE, unpaired R1 reads are extended from the 5' end by fragmentLength. If fragmentLength is not supplied, it is estimated from proper pairs in the same BAM.

Value

A qseaSet with updated slots:

• Counts: updated count matrix.

• Library: updated library table with summary metrics.

• Parameters: appended record of MAPQ/size thresholds.

Parallelisation

Parallel execution uses BiocParallel::bplapply() with the currently registered backend (see BiocParallel::register()). Pass parallel = TRUE and set a multi-core/cluster backend for speed.

See Also

getBamCoveragePairedAndUnpairedR1() (internal low-level worker), qsea::getRegions(), qsea::getSampleTable(), BiocParallel::register()

Description

Runs a CNV pipeline on each sample: counts fragments in fixed-size genomic windows, normalises for GC and mappability (HMMcopy), segments, and stores per-window copy-number in the qseaSet. Supports parallel execution. Only GRCh38/hg38 is currently supported.

Usage

addHMMcopyCNV(
  qs,
  inputColumn = "input_file",
  windowSize = 1e+06,
  fragmentLength = NULL,
  plotDir = NULL,
  parallel = getMesaParallel(),
  maxInsertSize = 1000,
  minInsertSize = 50,
  minReferenceLength = 30,
  minMapQual = 30,
  properPairsOnly = FALSE,
  hmmCopyGC = NULL,
  hmmCopyMap = NULL
)

Arguments

Details

Coverage is obtained (pairs + optional R1) over tiled windows, corrected with HMMcopy (HMMcopy::correctReadcount() followed by HMMcopy::HMMsegment()), and merged back into the qseaSet. You must supply GC/mappability tracks binned at the same windowSize (or use package-provided defaults where applicable). When parallel = TRUE, computation uses the currently registered backend (see BiocParallel::register()).

Value

The input qseaSet with CNV information attached, including:

• per-window CNV tracks accessible via downstream CNV accessors,

• library/sample tables updated with relevant CNV/coverage summaries,

• (optionally) per-sample diagnostic PDFs if plotDir is provided.

See Also

runHMMcopy(), plotCNVheatmap(), getBamCoveragePairedAndUnpairedR1() (internal low-level worker), HMMcopy::correctReadcount(), HMMcopy::HMMsegment(), BiocParallel::register()

Other CNV: plotCNVheatmap(), removeCNV(), runHMMCopy()

Examples

## Not run: 
data(exampleTumourNormal, package = "mesa")
exampleTumourNormal %>%

    addHMMcopyCNV(
        inputColumn = "input_file",
        windowSize = 1e6,
        hmmCopyGC = gc_hg38_1000kb, # GRanges with 'gc' column
        hmmCopyMap = map_hg38_1000kb, # GRanges with 'map' column
        properPairsOnly = TRUE,
        parallel = FALSE,
        plotDir = tempdir()
    )

## End(Not run)

Description

For each sample, compute the proportion of Edgar et al. “ultra-stable” windows that are clearly methylated (beta >= minBeta) after filtering by CpG density (>= minDensity). The result is added to the sample table as hyperStableEdgar (overwriting any existing column of that name).

Usage

addHyperStableFraction(qseaSet, minDensity = 5, minBeta = 0.8)

Arguments

Details

• Genome restriction. Only supported for "BSgenome.Hsapiens.NCBI.GRCh38"; the function stops otherwise (checked via qsea::getParameters()).

• Regions used. Uses mesa::hg38UltraStableProbes, a GRCh38 set derived from array data (Edgar et al., 2014).

• Computation. Windows overlapping the ultra-stable set are extracted, beta values are computed via qsea::makeTable(norm_methods = "beta"), windows with CpG_density < minDensity are dropped, NA beta are treated as 0 for thresholding, and the per-sample fraction with beta >= minBeta is returned.

Value

The input qseaSet with its sampleTable augmented by a numeric column hyperStableEdgar (range 0–1) giving, per sample, the fraction of ultra-stable windows called methylated.

References

Edgar R, Tan PPC, Portales-Casamar E, Pavlidis P (2014). Meta-analysis of human methylomes reveals stably methylated sequences surrounding CpG islands associated with high gene expression. https://pubmed.ncbi.nlm.nih.gov/25493099/

See Also

qsea::makeTable(), qsea::getSampleTable(), qsea::getSampleNames(), hg38UltraStableProbes, getSampleQCSummary()

Examples

data(exampleTumourNormal, package = "mesa")

# Run only if the example windows overlap the ultra-stable probes
exampleTumourNormal %>%
    {
        has_ov <- sum(
            GenomicRanges::countOverlaps(
                qsea::getRegions(.),
                mesa::hg38UltraStableProbes
            )
        ) > 0

        if (has_ov) addHyperStableFraction(.) else {
            message(
                "No overlap with hg38UltraStableProbes in this toy dataset;",
                " skipping."
            )
            .
        }
    } %>%
    qsea::getSampleTable() %>%
    dplyr::select(sample_name, dplyr::any_of("hyperStableEdgar")) %>%
    head()

Description

Append library information to the sampleTable of a qseaSet. Columns are taken from qseaSet@libraries$file_name, and when available, from qseaSet@libraries$input_file (prefixed with "input_"). Existing columns are not duplicated.

Usage

addLibraryInformation(qseaSet)

Arguments

Value

A qseaSet object:

• addLibraryInformation(): returns the same qseaSet with its sampleTable augmented by library-level columns.

Examples

data(exampleTumourNormal, package = "mesa")
qs <- addLibraryInformation(exampleTumourNormal)
head(qsea::getSampleTable(qs))

Description

For each sample, compute MEDIPS-style CpG enrichment metrics (relH, GoGe, and read counts) from its BAM and append the results to the qseaSet sample metadata. Supports both pulldown (default) and input libraries.

Usage

addMedipsEnrichmentFactors(
  qseaSet,
  exportPath = NULL,
  nonEnrich = FALSE,
  extend = 0,
  shift = 0,
  uniq = 0,
  chr.select = NULL,
  paired = TRUE,
  file_name = "file_name",
  nCores = 1
)

Arguments

Details

Internally calls calculateCGEnrichment() per sample to derive relH/GoGe and counts, then merges the results into the ⁠@libraries⁠ slot (⁠$file_name⁠ for pulldown, ⁠$input_file⁠ for input when nonEnrich = TRUE). Parallelisation uses parallel::mclapply(); on non-Unix systems, nCores > 1 is ignored.

Value

A qseaSet with new MEDIPS-style metrics appended:

• Sample/library metrics (added under the appropriate library frame): relH, GoGe, nReads, nReadsWithoutPattern, n100bpReads, n100bpReadsWithoutPattern.

• Provenance: any export artefacts (PDF/RDS) written to exportPath if provided.

See Also

calculateCGEnrichment(), calculateCGEnrichmentGRanges()

Examples

# Requires BAM files and the MEDIPS package; see \dontrun{} for a full
# usage example.
## Not run: 
if (requireNamespace("MEDIPS", quietly = TRUE) &&
    requireNamespace("BSgenome.Hsapiens.UCSC.hg19", quietly = TRUE)) {
    bam <- system.file(
        "extdata",
        "hESCs.Input.chr22.bam",
        package = "MEDIPSData"
    )

    data.frame(
        sample_name = "hESC_Input_chr22",
        file_name = bam,
        group = "Input",
        input_file = bam,
        stringsAsFactors = FALSE
    ) %>%
        qsea::createQseaSet("BSgenome.Hsapiens.UCSC.hg19") %>%
        addMedipsEnrichmentFactors(
            exportPath = tempdir(),
            chr.select = paste0("chr22"),
            paired     = FALSE
            # If your qsea returns a BSgenome object and as.character()
            # doesn't help, uncomment:
            # , BSgenome_pkg = "BSgenome.Hsapiens.UCSC.hg19"
        ) %>%
        qsea::getSampleTable() %>%
        print()
}

## End(Not run)

Description

Apply a set of qsea normalisation steps to a qseaSet, including:

• setting library factors to 1 (disabling TMM),

• adding offsets based on an enrichment pattern, and

• configuring enrichment parameters across a CpG-density window range.

Usage

addNormalisation(
  qseaSet,
  enrichmentMethod = "blind1-15",
  maxPatternDensity = 0.05
)

Arguments

Details

This function encapsulates a recommended default pipeline for qsea normalisation. It ensures consistency across runs and simplifies code by bundling commonly applied steps.

#' Methods available for enrichmentMethod:

• "blind1-15": the blind calibration method detailed in the qsea paper, fitting a straight line of decreasing expected average methylation levels from ~76% at CpG density = 1 to ~25% at CpG density = 15.

• "none": no enrichment normalisation is performed.

Value

A qseaSet with updates to:

• Library factors — set to 1 via qsea::addLibraryFactors().

• Offsets — added via qsea::addOffset() using getPattern() and maxPatternDensity.

• Enrichment parameters — configured over windows with CpG density in ⁠[1, 15]⁠ using a linear signal schedule and bins = seq(0.5, 40.5, 0.5).

• Parameters slot — enrichmentMethod recorded in qseaSet@parameters$enrichmentMethod.

See Also

qsea::addLibraryFactors(), qsea::addOffset(), qsea::addEnrichmentParameters(), getPattern()

Examples

# addNormalisation requires windows spanning a range of CpG densities for
# background estimation; the pre-filtered example datasets lack these, so
# a synthetic qseaSet is used here.

# Run normalisation on a toy qseaSet with ~100k reads
qsea::getExampleQseaSet(expSamplingDepth = 100000) %>%
    addNormalisation(maxPatternDensity = 0.5)

# Run with no enrichment normalisation
qsea::getExampleQseaSet(expSamplingDepth = 1e5) %>%
    addNormalisation(enrichmentMethod = "none", maxPatternDensity = 0.5)

# Access the recorded enrichment method
qsea::getExampleQseaSet(expSamplingDepth = 1e5) %>%
    addNormalisation(maxPatternDensity = 0.5) %>%
    getParameters() %>%
    purrr::pluck("enrichmentMethod")

Description

Compute per-sample summaries across windows and left-join them to the qseaSet sample table.

Usage

addSummaryAcrossWindows(
  qseaSet,
  regionsToOverlap = NULL,
  fn = mean,
  suffix = "",
  normMethod = c("nrpm", "beta"),
  naMethod = "impute",
  minEnrichment = 3
)

Arguments

Details

Internally, this calls summariseAcrossWindows() to compute per-sample summaries for the requested normMethod(s), then left-joins those summaries to the sampleTable. Output column names typically encode the method and the summary function (and include suffix when provided).

Value

A qseaSet with new summary columns appended to its sampleTable.

See Also

summariseAcrossWindows(), getDataTable()

Other window-summaries: countWindowsAboveCutoff(), summariseAcrossWindows()

Examples

data(exampleTumourNormal, package = "mesa")

# Add mean NRPM and beta across all windows 
exampleTumourNormal %>%
  addSummaryAcrossWindows(
    fn         = mean,
    normMethod = c("nrpm","beta")
  ) %>%
  getSampleTable()

# Maximum NRPM across a small genomic slice
exampleTumourNormal %>%
  addSummaryAcrossWindows(
    regionsToOverlap = data.frame(
        seqnames = 7,
        start = 25002001,
        end = 25017900
    ),
    fn         = max,
    normMethod = "nrpm"
  ) %>%
  getSampleTable()

# Add summaries repeatedly with different regions using a suffix
exampleTumourNormal %>%
  addSummaryAcrossWindows(
    regionsToOverlap = data.frame(
        seqnames = 7,
        start = 25002001,
        end = 25017900
    ),
    fn         = median,
    normMethod = "nrpm",
    suffix     = "subset"
  ) %>%
  addSummaryAcrossWindows(
    fn         = median,
    normMethod = "nrpm",
    suffix     = "all"
  ) %>%
  getSampleTable()

Description

Uses ChIPseeker::annotatePeak() to assign transcript-relative annotations (promoter, exon, intron, intergenic, etc.) to genomic windows in dataTable. If a genome is specified or globally set via setMesaGenome(), defaults and convenience datasets are applied for GRCh38. CpG island context and FANTOM enhancer overlaps can be added if the corresponding ranges are provided (or left NULL to use mesa defaults for GRCh38).

Usage

annotateWindows(
  dataTable,
  genome = .getMesaGenome(),
  TxDb = .getMesaTxDb(),
  annoDb = .getMesaAnnoDb(),
  CpGislandsGR = NULL,
  FantomRegionsGR = NULL
)

Arguments

Details

• Conversion: Non-GRanges inputs are converted via qseaTableToChrGRanges().

• Genome-aware defaults (GRCh38/hg38): Missing TxDb/annoDb/contexts are filled using the packages noted above when installed.

• Seqlevels style: Output seqnames have the "chr" prefix removed (e.g., "chr1" → "1").

• ChIPseeker call: Uses tssRegion = c(-2000, 500), level = "transcript", overlap = "all", verbose = FALSE.

• Output columns: Includes annotation (full label), shortAnno (without the trailing parenthetical), and—when contexts are provided—landscape (Island/Shore/Shelf/Open Sea) and inFantom (overlap count with FANTOM).

Value

A tibble with the input windows augmented by ChIPseeker annotations and, when available, CpG island landscape and Fantom overlap.

See Also

ChIPseeker::annotatePeak(), setMesaGenome(), setMesaTxDb(), setMesaAnnoDb(), qseaTableToChrGRanges(), liftOverHg19()

Examples

data(exampleTumourNormal, package = "mesa")

# Derive some regions (e.g., DMRs) then annotate using GRCh38 defaults
exampleTumourNormal %>%
  calculateDMRs(variable = "tumour", contrasts = "first") %>%
  annotateWindows(genome = "hg38")

# Or specify TxDb/annoDb explicitly
exampleTumourNormal %>%
  calculateDMRs(variable = "tumour", contrasts = "first") %>%
  annotateWindows(
    TxDb   = "TxDb.Hsapiens.UCSC.hg38.knownGene",
    annoDb = "org.Hs.eg.db"
  )

# Mouse example (mm10): supply mouse TxDb and OrgDb
data(exampleMouse, package = "mesa")
exampleMouse %>%
  getRegions() %>%
  annotateWindows(
    TxDb   = "TxDb.Mmusculus.UCSC.mm10.knownGene",
    annoDb = "org.Mm.eg.db"
  )

# You can also set defaults globally using setMesaTxDb and setMesaAnnoDb:
  setMesaGenome("hg38"); 
  setMesaTxDb("TxDb.Hsapiens.UCSC.hg38.knownGene"); 
  setMesaAnnoDb("org.Hs.eg.db")

Description

Extends dplyr::arrange() to reorder samples of a qseaSet based on columns in its sampleTable.

Usage

## S3 method for class 'qseaSet'
arrange(.data, ..., .by_group = FALSE)

Arguments

Value

A qseaSet object with samples reordered. Both the count_matrix and the sampleTable are updated consistently.

See Also

qsea::getSampleTable(), subsetQset(), sort.qseaSet() (for natural sorting of sample names).

Examples

data(exampleTumourNormal, package = "mesa")

# Reorder samples by age
exampleTumourNormal %>% arrange(age)

# Reorder by tissue
exampleTumourNormal %>% arrange(tissue)

# Reorder by tissue (descending), then tumour type
exampleTumourNormal %>% arrange(desc(tissue), tumour)

Description

Accept a GRanges or a data frame containing window coordinates and return a valid GenomicRanges::GRanges. Supported data-frame schemas:

• seqnames, start, end

• chr, start, end (renamed to seqnames)

• chr, window_start, window_end (renamed to seqnames/start/end)

Usage

asValidGranges(object)

Arguments

Value

A GenomicRanges::GRanges built from object. Errors if no supported schema is found.

See Also

as_granges, qseaTableToChrGRanges

Examples

# From GRanges (passes through)
GenomicRanges::GRanges("chr1", IRanges::IRanges(100, 200)) %>%
    asValidGranges()

# From data.frame (seqnames/start/end)
data.frame(seqnames = "chr2", start = 10, end = 20) %>%
    asValidGranges()

# From data.frame (chr/window_start/window_end)
data.frame(chr = "1", window_start = 1000, window_end = 1100) %>%
    asValidGranges()

Description

A data frame containing cached relH and GoGe values for different BSgenomes, generated with calculateGenomicCGDistribution

Usage

BSgenome.Hsapiens.UCSC.hg19.CpG.distribution

BSgenome.Hsapiens.NCBI.GRCh38.CpG.distribution

BSgenome.Mmusculus.UCSC.mm10.CpG.distribution

Format

A data frame with 1 row and 2 columns

A data frame with 1 row and 2 columns

A data frame with 1 row and 2 columns

Description

Compute CpG enrichment metrics (relH and GoGe) from aligned reads in a BAM file. Uses MEDIPS to obtain fragment ranges and Biostrings to interrogate the reference genome. Optionally exports a fragment-length density plot (PDF) and a serialized RDS with the GRanges of reads.

Usage

calculateCGEnrichment(
  file = NULL,
  BSgenome = NULL,
  exportPath = NULL,
  extend = 0,
  shift = 0,
  uniq = 0,
  chr.select = NULL,
  paired = TRUE
)

Arguments

Details

Reads are scanned for "CG" dinucleotides. Counts are normalised against genome-wide expectations (relH, GoGe).

• For GRCh38 and hg19, cached genomic distributions are bundled with mesa for efficiency.

• For other genomes, calculateGenomicCGDistribution() is invoked.

Value

A data.frame with columns:

• file — input BAM path.

• relH — sample relH normalised by genome relH.

• GoGe — sample GoGe normalised by genome GoGe.

• nReads — total reads considered.

• nReadsWithoutPattern — reads lacking "CG" motif.

• n100bpReads — reads with length >= 100 bp.

• n100bpReadsWithoutPattern — reads >= 100 bp lacking "CG".

See Also

calculateCGEnrichmentGRanges(), calculateGenomicCGDistribution(), MEDIPS, BSgenome

Examples

if (requireNamespace("MEDIPS", quietly = TRUE) &&
    requireNamespace("MEDIPSData", quietly = TRUE) &&
    requireNamespace("BSgenome.Hsapiens.UCSC.hg19", quietly = TRUE) &&
    require("GenomicRanges", quietly = TRUE)) {
    calculateCGEnrichment(
        file = system.file(
            "extdata",
            "hESCs.Input.chr22.bam",
            package = "MEDIPSData"
        ),
        BSgenome   = "BSgenome.Hsapiens.UCSC.hg19",
        exportPath = tempdir(),
        paired     = FALSE
    )
}

Description

Compute CpG enrichment metrics (relH and GoGe) from a GenomicRanges::GRanges of read spans, using the specified BSgenome. Useful when reads are already represented as genomic ranges rather than extracted directly from a BAM file.

Usage

calculateCGEnrichmentGRanges(
  readGRanges = NULL,
  BSgenome = NULL,
  chr.select = NULL
)

Arguments

Details

relH and GoGe are computed by counting "CG" dinucleotides in the provided fragments and normalising by genome-wide expectations (from calculateGenomicCGDistribution()). Unlike calculateCGEnrichment(), this function assumes reads are already summarised as genomic ranges.

Value

A tibble with one row and the following columns:

• relH — sample relH normalised by genome relH.

• GoGe — sample GoGe normalised by genome GoGe.

• nReads — total reads (fragments).

• nReadsWithoutPattern — reads lacking "CG" motif.

• n100bpReads — reads with length >= 100 bp.

• n100bpReadsWithoutPattern — reads >= 100 bp lacking "CG".

See Also

calculateCGEnrichment(), calculateGenomicCGDistribution(), GenomicRanges, BSgenome

Examples

# Runnable toy example with synthetic reads over chr1 (requires a BSgenome)
if (requireNamespace("BSgenome.Hsapiens.NCBI.GRCh38", quietly = TRUE)) {
    n <- 200
    gr <- GenomicRanges::GRanges(
        seqnames = rep(1, n),
        ranges = IRanges::IRanges(
            start = sample(1e6:2e6, n),
            width = sample(80:180, n, replace = TRUE)
        ),
        strand = "*"
    )
    calculateCGEnrichmentGRanges(
        readGRanges = gr,
        BSgenome    = "BSgenome.Hsapiens.NCBI.GRCh38",
        chr.select  = 1
    )
}

Description

Calculate one or more contrasts to find Differentially Methylated Regions (DMRs).

Usage

calculateDMRs(
  qseaSet,
  variable = NULL,
  covariates = NULL,
  contrasts = NULL,
  minReadCount = 0,
  minNRPM = 1,
  checkPVals = TRUE,
  FDRthres = 0.05,
  keepPvals = FALSE,
  formula = NULL,
  keepContrastMeans = TRUE,
  keepData = FALSE,
  keepGroupMeans = FALSE,
  direction = "both",
  calcDispersionAll = FALSE
)

Arguments

Details

Contrast strings are parsed as follows:

• "A_vs_B" creates a single contrast A vs B;

• "All"/"all" expands to all pairwise contrasts among levels of variable (via makeAllContrasts());

• "All_vs_X" creates each level vs X;

• "X_vs_All" creates X vs each other level;

• "first" constructs only the first possible pair (based on factor order). Internally, this function:

1. filters windows by minReadCount/minNRPM, 2) fits a Negative Binomial Generalised Linear Model including dispersion estimates, 3) adds contrasts as requested, calculating adjusted p-values (adjPval), log2 fold changes (log2FC) and differences in beta values (deltaBeta), 4) creates an output table showing any window which is differentially methylated (with adjusted p-value below FDRthres) in at least one contrast. Additional columns will be added containing data values if keepData and keepGroupMeans are true.

Value

A tibble with one row per significant region/window containing:

• per-contrast statistics (e.g., log2FC, deltaBeta, test statistic),

• multiple-testing results (FDR; optionally raw p-values if keepPvals),

• optional group means (if keepGroupMeans = TRUE),

• optional per-sample columns (if keepData = TRUE),

• metadata columns for genomic coordinates and window attributes.

See Also

Other DMR-detection: fitQseaGLM(), getDMRsData(), makeAllContrasts()

Examples

data(exampleTumourNormal, package = "mesa")

# A single explicit contrast
exampleTumourNormal %>%
    calculateDMRs(variable = "type", contrasts = "LUAD_vs_NormalLung")

# All pairwise contrasts among levels of 'type'
exampleTumourNormal %>%
    calculateDMRs(variable = "type", contrasts = "all")

# Tighter FDR threshold
exampleTumourNormal %>%
    calculateDMRs(variable = "type", contrasts = "all", FDRthres = 1e-4)

# Return all per-sample columns for the first available contrast on 'tumour'
exampleTumourNormal %>%
    calculateDMRs(variable = "tumour", contrasts = "first", keepData = TRUE)

Description

For each sample, compute the proportion of windows with counts ⁠>= numCountsNeeded⁠ that overlap regionsToOverlap, relative to all windows with counts ⁠>= numCountsNeeded⁠ in that sample.

Usage

calculateFractionReadsInGRanges(qseaSet, regionsToOverlap, numCountsNeeded)

Arguments

Details

Operates on the window × sample count matrix from qsea::getCounts(). Counts are converted to a logical indicator (count ⁠>= numCountsNeeded⁠) and summed per sample:

• initialOverBackNum: windows meeting the threshold genome-wide.

• afterOverBackNum: windows meeting the threshold within regionsToOverlap (via filterByOverlaps()). The reported fraction is afterOverBackNum / initialOverBackNum.

Note: despite the function name, this computes a window-based fraction using a count threshold, not a direct fraction of raw reads.

Value

A tibble with one row per sample containing:

• sample_name

• initialOverBackNum — windows >= threshold genome-wide

• afterOverBackNum — windows >= threshold within regionsToOverlap

• fraction — afterOverBackNum / initialOverBackNum followed by columns from the sample table (left-joined by sample_name).

See Also

qsea::getCounts(), qsea::getSampleTable(), filterByOverlaps(), hg38CpGIslands, subsetWindowsBySignal()

Other window-helpers: convertToArrayBetaTable(), downSample(), subsetWindowsBySignal(), subsetWindowsOverBackground()

Examples

data(exampleTumourNormal, package = "mesa")

# Using the shipped ultra-stable probes (GRCh38) as the region set
exampleTumourNormal %>%
  calculateFractionReadsInGRanges(
    regionsToOverlap = mesa::hg38CpGIslands,
    numCountsNeeded   = 5
  ) %>%
  dplyr::select(sample_name, initialOverBackNum, afterOverBackNum, fraction) 

# Define a small GRanges subset from the object's own windows
gr <- exampleTumourNormal %>%
  qsea::getRegions() %>%
  head(n = 100)
  
calculateFractionReadsInGRanges(
      qseaSet           = exampleTumourNormal,
      regionsToOverlap  = gr,
      numCountsNeeded   = 3) %>%
  dplyr::select(sample_name, initialOverBackNum, afterOverBackNum, fraction)

Description

Compute genome-wide CpG metrics for a given BSgenome:

• relH: relative CpG frequency (% of dinucleotides that are "CG").

• GoGe: enrichment statistic $(nCG * genome_length) / (nC * nG)$.

Usage

calculateGenomicCGDistribution(BSgenome)

Arguments

Details

These values are used as denominators to normalise sample-level CpG enrichment.

• Chromosomes with names containing "rand" or "chrUn" are excluded.

• The BSgenome indicated by BSgenome is loaded dynamically.

• Uses Biostrings and BSgenome utilities to count CpG, C, and G occurrences across the genome.

Value

A data.frame with two numeric columns:

• genome.relH — percent of "CG" dinucleotides genome-wide.

• genome.GoGe — GoGe enrichment statistic.

See Also

calculateCGEnrichment(), calculateCGEnrichmentGRanges(), BSgenome, Biostrings

Examples

# Example: compute genome-wide CpG metrics for a S. cerevisae genome.
# It could be done for GRCh38 genome instead, but it will take more time to
# run (package must be installed)
if (requireNamespace("BSgenome.Scerevisiae.UCSC.sacCer3", quietly = TRUE)) {
    calculateGenomicCGDistribution("BSgenome.Scerevisiae.UCSC.sacCer3")
}

Description

S4 method for colnames that returns the column names of a qseaSet's sample metadata table (i.e., qsea::getSampleTable()).

Usage

## S4 method for signature 'qseaSet'
colnames(x)

Arguments

Value

character(). A character vector containing the column names of the sample table.

See Also

qsea::getSampleTable(), qsea::getSampleNames()

Examples

data(exampleTumourNormal, package = "mesa")

# Retrieve the column names of the sample metadata
colnames(exampleTumourNormal)

Description

Merge two qseaSet objects (or .rds files containing them) into a single qseaSet. This is the pairwise worker used internally by combineQsetsList().

Usage

combineQsets(
  qseaSet1,
  qseaSet2,
  checkParams = FALSE,
  regionsToKeep = NULL,
  dropDuplicates = FALSE
)

Arguments

Value

A qseaSet object containing:

• Samples from both inputs.

• Regions restricted by regionsToKeep if supplied.

• Duplicates handled according to dropDuplicates.

• Global parameters optionally checked for consistency.

See Also

combineQsetsList() for merging more than two inputs.

Examples

data(exampleTumourNormal, package = "mesa")

# Split a cohort into Tumour and Normal, then combine back
tumours <- exampleTumourNormal %>% filter(tumour == "Tumour")
normals <- exampleTumourNormal %>% filter(tumour == "Normal")
combineQsets(tumours, normals)

Description

Merge a list of qseaSet objects (or .rds files containing them) into a single qseaSet. Useful when cohorts were processed separately (e.g., tumour/normal, multiple runs) and need to be combined into one container.

Usage

combineQsetsList(
  qseaSets,
  firstQset = NULL,
  dropDuplicates = TRUE,
  checkParams = TRUE,
  regionsToKeep = NULL
)

Arguments

Value

A qseaSet object:

• Combined samples from all inputs (or from firstQset plus inputs).

• Duplicates handled according to dropDuplicates.

• Regions optionally prefiltered by regionsToKeep prior to merge.

See Also

combineQsets() for pairwise merging.

Examples

data(exampleTumourNormal, package = "mesa")

# Split a cohort into Tumour and Normal, create a list, then combine back
tumours <- exampleTumourNormal %>% filter(tumour == "Tumour")
normals <- exampleTumourNormal %>% filter(tumour == "Normal")
qsetList <- list(tumours, normals)
combineQsetsList(qsetList)

Description

Build a probe × sample beta table by mapping qseaSet windows to array probe coordinates. Currently supports Illumina Infinium 450k probes on GRCh38; a custom GRanges of probes can also be supplied.

Usage

convertToArrayBetaTable(qseaSet, arrayDetails = "Infinium450k")

Arguments

Details

Probe coordinates are matched to qseaSet windows (e.g., via overlap). When multiple windows are relevant for a probe, the package's implementation is used to derive a single beta value per probe. Ensure the probe genome build matches the qseaSet (e.g., GRCh38) to avoid mismatches.

Value

A data.frame with one row per probe (including an ID column) and one column per sample containing the corresponding beta values. Probes with no matching/overlapping window receive NA.

See Also

getDataTable(), dataset mesa::hg38_450kArrayGR

Other window-helpers: calculateFractionReadsInGRanges(), downSample(), subsetWindowsBySignal(), subsetWindowsOverBackground()

Examples

data(exampleTumourNormal, package = "mesa")

# Use the built-in keyword for 450k probes (GRCh38)
exampleTumourNormal %>%
  convertToArrayBetaTable(arrayDetails = "Infinium450k") %>%
  head()

# Supply an explicit GRanges of 450k probes (must have metadata column 'ID')
exampleTumourNormal %>%
  convertToArrayBetaTable(arrayDetails = mesa::hg38_450kArrayGR) %>%
  head()

Description

For a given set of genomic windows, count (per sample) how many windows exceed a chosen threshold using the selected normalisation/measure.

Usage

countWindowsAboveCutoff(
  qseaSet,
  GRanges,
  samples = NULL,
  cutoff = 0,
  normMethod = "nrpm"
)

Arguments

Details

The function builds a window × sample matrix over GRanges for the chosen normMethod, optionally restricts to samples, and counts, per sample, the number of windows with value ⁠>= cutoff⁠. It also reports how many windows were evaluated (totalWindowsUsed). Results are joined with the sample table.

Value

A tibble with one row per sample containing:

• sample_name

• numOverCutoff — number of windows ⁠>= cutoff⁠

• totalWindowsUsed — number of windows evaluated followed by columns from the sample table (left-joined).

See Also

getDataTable(), summariseAcrossWindows()

Other window-summaries: addSummaryAcrossWindows(), summariseAcrossWindows()

Examples

data(exampleTumourNormal, package = "mesa")

#' # Count windows with NRPM >= 1 over the first 100 regions
countWindowsAboveCutoff(exampleTumourNormal, 
                               qsea::getRegions(exampleTumourNormal)[1:100], 
                               cutoff = 1, 
                               normMethod = "nrpm")

Description

Randomly subsample reads to a fixed depth across all samples in a qseaSet. Useful for equalising library sizes prior to comparison.

Usage

downSample(qseaSet, nReads)

Arguments

Details

Downsampling is performed independently per sample. To obtain reproducible results across runs, call set.seed() before invoking downSample(). Samples with total reads ⁠< nReads⁠ are not upsampled.

Value

A qseaSet with downsampled counts. Library metadata (valid_fragments, offset, library_factor) are updated accordingly.

See Also

getDataTable(), subsetWindowsBySignal(), mixSamples()

Other window-helpers: calculateFractionReadsInGRanges(), convertToArrayBetaTable(), subsetWindowsBySignal(), subsetWindowsOverBackground()

Examples

data(exampleTumourNormal, package = "mesa")

set.seed(1)
# Downsample to 100 fragments per sample, then check per-sample totals
exampleTumourNormal %>%
  downSample(100) %>%
  getCountTable() %>%
  dplyr::select(tidyselect::matches("_[NT]")) %>%
  colSums() %>%
  range()  # should be ~ c(100, 100) if all samples had >= 100 reads

# Also inspect the updated library sizes recorded in the sample table
exampleTumourNormal %>%
  downSample(100) %>%
  qsea::getSampleTable() %>%
  dplyr::select(patient, type, gender)

Description

The 2019 ENCODE list of regions that are poorly mapped (2019 https://doi.org/10.1038/s41598-019-45839-z). List downloaded from https://github.com/Boyle-Lab/Blacklist/blob/master/lists/ hg38-blacklist.v2.bed.gz and edited to remove "chr".

Usage

ENCODEbadRegions

Format

A Granges object with 636 ranges:

seqnames

chromosome

ranges

position on the chromosome

Source

https://github.com/Boyle-Lab/Blacklist/blob/master/lists/ hg38-blacklist.v2.bed.gz

Description

A qseaSet object containing a small region of chromosome 5 for a mouse cell line, 2 replicates of 2 conditions (processed within CRUK-MI CBC).

Usage

exampleMouse

Format

A qseaSet object with 153 regions and 4 samples:

Description

A qseaSet object containing a small region of chromosome 7 for 5 paired tumour/normal samples (commercially bought from Origene and processed within CRUK-MI CBC).

Usage

exampleTumourNormal

Format

A qseaSet object with 819 regions and 10 samples:

Description

A data frame containing FANTOM5 enhancer regions, hg38

Usage

FantomRegions

Format

A data frame with 63285 rows and 3 variables:

Description

Extends dplyr::filter() to subset samples in a qseaSet based on predicates applied to its sampleTable.

Usage

## S3 method for class 'qseaSet'
filter(.data, ..., .preserve = FALSE)

Arguments

Details

Filtering is performed on the result of qsea::getSampleTable(.data). Matching rows determine the sample_names retained in the qseaSet. Grouped filtering (group_by) is not supported for qseaSet.

Value

A qseaSet object containing only the samples whose sampleTable rows satisfied the filter expressions. If no samples remain, an empty qseaSet is returned (with a message but not an error).

See Also

qsea::getSampleTable() to inspect metadata, subsetQset() for explicit sample subsetting, selectQset() for column selection in the sampleTable.

Examples

data(exampleTumourNormal, package = "mesa")

# Keep only older patients
exampleTumourNormal %>% filter(age >= 70)

# Restrict to colon tissue
exampleTumourNormal %>% filter(tissue == "Colon")

# Select only tumour samples
exampleTumourNormal %>% filter(tumour != "Normal")

# Combine predicates: lung tumours only
exampleTumourNormal %>%
    filter(stringr::str_detect(sample_name, "Lung"), tumour != "Normal")

# If no rows match, returns empty qseaSet (with a message)
exampleTumourNormal %>% filter(tissue == "Liver")

Description

filterByOverlaps() retains windows that overlap, while filterByNonOverlaps() retains windows that do not overlap a set of genomic regions. Inputs may use different chromosome styles (with or without the "chr" prefix); styles are harmonized automatically before overlap computation.

Usage

filterByOverlaps(qseaSet, regionsToOverlap)

filterByNonOverlaps(qseaSet, regionsToOverlap)

Arguments

Details

Chromosome naming is aligned using GenomeInfoDb::seqlevelsStyle() so that overlap computation is consistent even when one input uses "chr1" and the other uses "1". Only the window set is filtered; sample metadata and counts are subset accordingly.

Value

A qseaSet object:

• filterByOverlaps(): returns the input qseaSet restricted to windows that overlap regionsToOverlap.

• filterByNonOverlaps(): returns the input qseaSet restricted to windows that do not overlap regionsToOverlap.

See Also

filter_by_overlaps, filter_by_non_overlaps, GRanges-class

Examples

data(exampleTumourNormal, package = "mesa")

# Data frame input
rgns <- data.frame(seqnames = 7L, start = 25002001L, end = 25017900L)
filterByOverlaps(exampleTumourNormal, regionsToOverlap = rgns)
filterByNonOverlaps(exampleTumourNormal, regionsToOverlap = rgns)

# GRanges input
gr <- GenomicRanges::GRanges("chr7", IRanges::IRanges(25002001L, 25017900L))
filterByOverlaps(exampleTumourNormal, regionsToOverlap = gr)
filterByNonOverlaps(exampleTumourNormal, regionsToOverlap = gr)

Description

Filters the regions of a qseaSet using dplyr::filter() predicates applied to its regions (as a data frame), then subsets the object accordingly.

Usage

filterWindows(qseaSet, ...)

Arguments

Value

A qseaSet object with only regions matching the filter conditions. Both the count_matrix and ⁠@regions⁠ slot are updated consistently.

See Also

qsea::getRegions(), filter.qseaSet() (for filtering samples instead of regions), dplyr::filter()

Examples

data(exampleTumourNormal, package = "mesa")

# Keep regions starting before position 25,020,000
exampleTumourNormal %>% filterWindows(start <= 25020000)

# Keep regions with CpG density > 10
exampleTumourNormal %>% filterWindows(CpG_density > 10)

# Combine multiple conditions
exampleTumourNormal %>%
    filterWindows(seqnames == "chr7", CpG_density > 5)

Description

Fit a negative-binomial GLM using qsea::fitNBglm() on counts from a qsea::qseaSet, and add one or more contrasts. This is the low-level worker used by calculateDMRs().

Usage

fitQseaGLM(
  qseaSet,
  variable = NULL,
  covariates = NULL,
  contrasts = NULL,
  keepIndex = NULL,
  minReadCount = 0,
  minNRPM = 1,
  checkPVals = TRUE,
  formula = NULL,
  calcDispersionAll = FALSE
)

Arguments

Details

• Dispersion is estimated across the windows retained after filtering.

• When calcDispersionAll = TRUE, all samples contribute to dispersion estimation even if not part of any contrast.

• Contrasts are added sequentially with qsea::addContrast().

• By default, beta-normalised counts are used as the outcome (as in qsea).

Value

A qseaGLM object containing:

• the fitted negative-binomial GLM;

• (optionally) one or more added contrasts (group1 vs group2);

• filtering metadata (based on keepIndex, minReadCount, minNRPM);

• model specification (variable, covariates, or formula).

See Also

Other DMR-detection: calculateDMRs(), getDMRsData(), makeAllContrasts()

Examples

data(exampleTumourNormal, package = "mesa")
qs <- exampleTumourNormal
contr <- tibble::tibble(group1 = "LUAD", group2 = "NormalLung")
fitQseaGLM(qs, variable = "type", contrasts = contr)

Description

Fixed-size bins with average GC content per bin. Bins are ordered by chromosome size (not strictly numeric).

Usage

data(gc_hg38_1000kb); data(gc_hg38_500kb); data(gc_hg38_50kb)

gc_hg38_50kb

gc_hg38_500kb

Format

One of:

1. A data.table/data.frame with columns:

   chrom

   Chromosome (e.g., "chr1", ..., "chrX", "chrY").

   start

   0-based start of the bin.

   end

   1-based end of the bin.

   gc

   Average GC fraction in the bin (0–1).

2. A GRanges with genomic bins and mcols(.)$gc storing the average GC fraction (0–1) per bin.

An object of class data.table (inherits from data.frame) with 61775 rows and 4 columns.

An object of class data.table (inherits from data.frame) with 6188 rows and 4 columns.

Value

gc_hg38_1000kb, gc_hg38_500kb, and gc_hg38_50kb return objects as described in @format with bin sizes of 1000 kb, 500 kb, and 50 kb, respectively.

Source

https://github.com/broadinstitute/ichorCNA

Description

Convenience wrapper around getDataTable() to extract beta values (fraction methylated). Returns a window × sample (or group) table for downstream summaries/plots.

Usage

getBetaTable(
  qseaSet,
  useGroupMeans = FALSE,
  minEnrichment = 3,
  addMethodSuffix = FALSE,
  verbose = TRUE
)

Arguments

Value

A tibble/data.frame with one row per genomic window and columns for each sample (or group), plus any window metadata included by getDataTable().

See Also

getDataTable(), getCountTable(), getNRPMTable()

Other table-helpers: getCountTable(), getDataTable(), getNRPMTable(), getSampleGroups2(), makeTransposedTable(), removeLibraryFactors(), removeNormMethodSuffix()

Examples

data(exampleTumourNormal, package = "mesa")

# Per-sample beta (first rows/columns), with a minimum read threshold
exampleTumourNormal %>%
  getBetaTable(minEnrichment = 3) %>%
  dplyr::select(1:6) %>%
  head()

# Group means instead of individual samples
exampleTumourNormal %>%
  getBetaTable(useGroupMeans = TRUE, minEnrichment = 3) %>%
  dplyr::select(1:6) %>%
  head()

# Keep the method suffix in column names
exampleTumourNormal %>%
  getBetaTable(addMethodSuffix = TRUE) %>%
  names() %>%
  head()

# Inspect how many NAs arise from the minEnrichment filter
exampleTumourNormal %>%
  getBetaTable(minEnrichment = 5) %>%
  dplyr::select(dplyr::where(is.numeric)) %>%
  is.na() %>%
  colSums() %>%
  head()

Description

Convenience wrapper that returns genomic positions of the "CG" motif for the specified BSgenome and chromosomes, via MEDIPS::MEDIPS.getPositions().

Usage

getCGPositions(BSgenome, chr.select)

Arguments

Value

A GRanges-class of motif positions.

See Also

calculateCGEnrichment, calculateCGEnrichmentGRanges, MEDIPS

Examples

# Requires MEDIPS and a BSgenome package
# if (requireNamespace("BSgenome.Hsapiens.NCBI.GRCh38", quietly = TRUE)) {
#   getCGPositions("BSgenome.Hsapiens.NCBI.GRCh38", chr.select = 22)
# }

Description

Convenience wrapper around getDataTable() to extract raw counts. Returns a window × sample (or group) table for downstream summaries/plots.

Usage

getCountTable(
  qseaSet,
  useGroupMeans = FALSE,
  addMethodSuffix = FALSE,
  verbose = TRUE
)

Arguments

Value

A tibble/data.frame with one row per genomic window and columns for each sample (or group), plus any window metadata included by getDataTable().

See Also

getDataTable(), getNRPMTable(), getBetaTable()

Other table-helpers: getBetaTable(), getDataTable(), getNRPMTable(), getSampleGroups2(), makeTransposedTable(), removeLibraryFactors(), removeNormMethodSuffix()

Examples

data(exampleTumourNormal, package = "mesa")

# Per-sample counts (first rows/columns)
exampleTumourNormal %>%
  getCountTable() %>%
  dplyr::select(1:6) %>%
  head()

# Group means instead of individual samples
exampleTumourNormal %>%
  getCountTable(useGroupMeans = TRUE) %>%
  dplyr::select(1:6) %>%
  head()

# Keep the method suffix in column names
exampleTumourNormal %>%
  getCountTable(addMethodSuffix = TRUE) %>%
  names() %>%
  head()

Description

Build a per-window table for one or more normalisation methods, returning values per sample or group means.

Usage

getDataTable(
  qseaSet,
  normMethod = "nrpm",
  useGroupMeans = FALSE,
  minEnrichment = 3,
  addMethodSuffix = FALSE,
  verbose = TRUE
)

Arguments

Details

The result is a window × sample (or group) table. Window coordinates and any available region metadata (e.g., seqnames, start, end, densities) are kept alongside the requested measures. Column naming: when a single method is requested and addMethodSuffix = FALSE, columns are bare sample / group names; otherwise a ⁠_{method}⁠ (and for group means ⁠_{method}_means⁠) suffix is appended.

For "beta", windows with reads ⁠< minEnrichment⁠ are set to NA before aggregation. Use removeNormMethodSuffix() afterwards if you want to strip method tags in wide tables.

Value

A tibble/data.frame with one row per genomic window and one column per sample (or group), plus window metadata columns.

See Also

getCountTable(), getNRPMTable(), getBetaTable(), removeNormMethodSuffix()

Other table-helpers: getBetaTable(), getCountTable(), getNRPMTable(), getSampleGroups2(), makeTransposedTable(), removeLibraryFactors(), removeNormMethodSuffix()

Examples

data(exampleTumourNormal, package = "mesa")

# Default: NRPM per window (first few columns)
exampleTumourNormal %>%
  getDataTable() %>%
  dplyr::select(1:6) %>%
  head()

# Multiple methods together (suffixes retained automatically)
exampleTumourNormal %>%
  getDataTable(normMethod = c("nrpm","beta"), minEnrichment = 3) %>%
  dplyr::select(1:8) %>%
  head()

# Group means instead of individual samples
exampleTumourNormal %>%
  getDataTable(useGroupMeans = TRUE, normMethod = "nrpm") %>%
  dplyr::select(1:6) %>%
  head()

# Keep (or later remove) the method suffix
exampleTumourNormal %>%
  getDataTable(normMethod = "nrpm", addMethodSuffix = TRUE) %>%
  names() %>%
  head()

Description

Given a qsea::qseaGLM object with one or more contrasts, extract a wide results table of statistics, adjusted p-values, and (optionally) group means and per-sample values.

Usage

getDMRsData(
  qseaSet,
  qseaGLM,
  sampleNames = NULL,
  variable = NULL,
  keepData = FALSE,
  keepGroupMeans = FALSE,
  FDRthres = 0.05,
  keepPvals = FALSE,
  keepFragmentInfo = FALSE,
  direction = "both"
)

Arguments

Details

Significant rows are determined using FDRthres and direction via qsea::isSignificant(). When keepData = TRUE and sampleNames = NULL, per-sample columns are included for all samples in the fitted model (or the subset relevant to each contrast, depending on the internal representation). Group means require variable to identify the groupings used in contrasts.

Value

A tibble with one row per DMR (window) containing:

• Per-contrast statistics (e.g., log2FC, deltaBeta, test statistic).

• Multiple-testing output (FDR; optionally raw p-values if keepPvals).

• Optional group summaries if keepGroupMeans = TRUE.

• Optional per-sample columns if keepData = TRUE (for sampleNames).

• Optional fragment/MAPQ metrics if keepFragmentInfo = TRUE.

See Also

Other DMR-detection: calculateDMRs(), fitQseaGLM(), makeAllContrasts()

Examples

data(exampleTumourNormal, package = "mesa")
qs <- exampleTumourNormal
contr <- tibble::tibble(group1 = "LUAD", group2 = "NormalLung")
glmfit <- fitQseaGLM(qs, variable = "type", contrasts = contr)
getDMRsData(qs, glmfit, variable = "type", FDRthres = 0.1)

Description

Annotate windows, then summarise signal and counts by CpG landscape (Island/Shore/Shelf/Open Sea) and short genomic annotation (e.g., Promoter/Exon/Intron/Intergenic).

Usage

getGenomicFeatureDistribution(
  qseaSet,
  cutoff = 1,
  normMethod = "nrpm",
  minEnrichment = 3
)

Arguments

Details

Windows are annotated via annotateWindows() (which can add CpG landscape and a concise shortAnno label). The function then groups by sample_name, landscape, and shortAnno to compute:

• nWindows: number of windows in the group,

• sum: sum of the selected measure across those windows,

• nOverCutoff: number of windows with value ⁠>= cutoff⁠. When using beta, windows failing minEnrichment are treated as NA before summarisation.

This function requires a transcript database and OrgDb to annotate windows (via annotateWindows()). Either:

• set them globally with setMesaGenome() (recommended for GRCh38/hg38), or

• provide compatible TxDb/annoDb packages yourself.

Value

A tibble with columns: sample_name, landscape, shortAnno, nWindows, sum, nOverCutoff, followed by sample-table metadata joined from qseaSet.

See Also

annotateWindows(), summariseAcrossWindows(), getDataTable()

Other annotation-summaries: plotGenomicFeatureDistribution()

Examples

# Ensure annotation defaults are available (GRCh38/hg38)
setMesaGenome("hg38")

data(exampleTumourNormal, package = "mesa")

# NRPM-based context summary
exampleTumourNormal %>%
  getGenomicFeatureDistribution(cutoff = 1, normMethod = "nrpm") %>%
  head()

# Beta-based summary (apply a minimum enrichment for non-NA betas)
exampleTumourNormal %>%
  getGenomicFeatureDistribution(
      cutoff = 0.7,
      normMethod = "beta",
      minEnrichment = 3
  ) %>%
  head()

Description

Get annotation DB for current or specified genome

Usage

getMesaAnnoDb(genome = NULL)

Arguments

Value

Character string of annotation database name (e.g., "org.Hs.eg.db", "org.Mm.eg.db")

Description

Get current mesa genome setting

Usage

getMesaGenome()

Value

Character string of current genome setting (e.g., "hg38", "hg19", "mm10")

Description

Get TxDb for current or specified genome

Usage

getMesaTxDb(genome = NULL)

Arguments

Value

A TxDb object for the specified genome

Description

Convenience wrapper around getDataTable() to extract NRPM values (normalised reads per million). Returns a window × sample (or group) table for downstream summaries/plots.

Usage

getNRPMTable(
  qseaSet,
  useGroupMeans = FALSE,
  addMethodSuffix = FALSE,
  verbose = TRUE
)

Arguments

Value

A tibble/data.frame with one row per genomic window and columns for each sample (or group), plus any window metadata included by getDataTable().

See Also

getDataTable(), getCountTable(), getBetaTable()

Other table-helpers: getBetaTable(), getCountTable(), getDataTable(), getSampleGroups2(), makeTransposedTable(), removeLibraryFactors(), removeNormMethodSuffix()

Examples

data(exampleTumourNormal, package = "mesa")

# Per-sample NRPM (first rows/columns)
exampleTumourNormal %>%
  getNRPMTable() %>%
  dplyr::select(1:6) %>%
  head()

# Group means instead of individual samples
exampleTumourNormal %>%
  getNRPMTable(useGroupMeans = TRUE) %>%
  dplyr::select(1:6) %>%
  head()

# Keep the method suffix in column names
exampleTumourNormal %>%
  getNRPMTable(addMethodSuffix = TRUE) %>%
  names() %>%
  head()

Description

Scan the region metadata of a qseaSet for columns ending in "_density" (e.g., "CpG_density") and return the base pattern names (e.g., "CpG").

Usage

getPattern(qseaSet)

Arguments

Details

This function inspects mcols(qsea::getRegions(qseaSet)), selects column names matching the regex "_density$", and strips that suffix. It does not compute densities or modify the object. If no matching columns exist, a zero-length character vector is returned.

Value

character() vector of pattern names discovered (length 0 if none).

See Also

qsea::addPatternDensity(), qsea::getRegions(), S4Vectors::mcols()

Examples

data(exampleTumourNormal, package = "mesa")
# Returns character(0) if no *_density columns are present
exampleTumourNormal %>% getPattern()

Description

Convenience wrapper around getDimRed() with method = "PCA".

Convenience wrapper around getDimRed() with method = "UMAP".

Run PCA or UMAP on methylation signal matrices extracted from a qsea::qseaSet. This is the core workhorse for dimensionality reduction; wrappers such as getPCA() and getUMAP() provide simplified access.

Usage

getPCA(
  qseaSet,
  dataTable = NULL,
  regionsToOverlap = NULL,
  normMethod = "beta",
  minEnrichment = 3,
  useGroupMeans = FALSE,
  minDensity = 0,
  topVarNum = 1000,
  topVarSamples = NULL,
  center = TRUE,
  scale = FALSE,
  nPC = 5,
  returnDataTable = FALSE,
  verbose = TRUE
)

getUMAP(
  qseaSet,
  dataTable = NULL,
  regionsToOverlap = NULL,
  normMethod = "beta",
  minEnrichment = 3,
  useGroupMeans = FALSE,
  minDensity = 0,
  topVarNum = 1000,
  topVarSamples = NULL,
  returnDataTable = FALSE,
  verbose = TRUE,
  ...
)

getDimRed(
  qseaSet,
  dataTable = NULL,
  method = "PCA",
  regionsToOverlap = NULL,
  normMethod = "beta",
  minEnrichment = 3,
  useGroupMeans = FALSE,
  minDensity = 0,
  topVarNum = 1000,
  topVarSamples = NULL,
  center = TRUE,
  scale = FALSE,
  nPC = 5,
  returnDataTable = FALSE,
  verbose = TRUE,
  ...
)

Arguments

Details

:

• For method = "PCA", DR is performed via stats::prcomp() with center and scale controls.

• For method = "UMAP", DR is performed via uwot::umap().

• Prior to DR, optional filtering is applied: enrichment cut-off (minEnrichment), CpG density (minDensity), region restriction (regionsToOverlap), and most-variable windows (topVarNum).

• When topVarNum or topVarSamples are vectorised/lists, multiple DR runs are performed and collected in res.

Value

A mesaDimRed object with:

• res: Named list of DR results; each element is a mesaPCA (for PCA) or mesaUMAP (for UMAP). Names correspond to entries in params$topVar.

• samples: Character vector of sample IDs used.

• dataTable (optional): The numeric matrix used for DR when returnDataTable = TRUE. It reflects filtering by regionsToOverlap and minDensity, removal of rows with missing values, and may include columns of window SDs when applicable (named per params$topVar).

• params: List of parameters used (method, selection/filter settings, etc.).

• sampleTable: Copy of qsea::getSampleTable(qseaSet).

Functions

• getPCA(): Principal component analysis of a qseaSet.

• getUMAP(): Uniform manifold approximation and projection of a qseaSet.

See Also

getPCA(), getUMAP(), mesaDimRed, mesaPCA, mesaUMAP, qsea::normMethod(), uwot::umap(), stats::prcomp()

Other dimred-helpers: plotDimRed(), plotUMAP()

Other dimred-helpers: plotDimRed(), plotUMAP()

Other dimred-helpers: plotDimRed(), plotUMAP()

Examples

data(exampleTumourNormal, package = "mesa")

# Default PCA (beta-normalised, top 1000 most variable windows, 5 PCs)
exampleTumourNormal %>%
    getPCA()

# Request only the top 3 PCs
exampleTumourNormal %>%
    getPCA(nPC = 3)

# Use multiple cutoffs for most-variable windows
# (returns list of PCA results)
exampleTumourNormal %>%
    getPCA(topVarNum = c(10, 100, 500, NA)) %>%
    slot("res") %>%
    names()
# Default UMAP
exampleTumourNormal %>%
    getUMAP(n_neighbors = 5)

# Alter UMAP hyperparameters
exampleTumourNormal %>%
    getUMAP(n_neighbors = 5, min_dist = 1)

# Use top 500 most variable windows only
exampleTumourNormal %>%
    getUMAP(n_neighbors = 5, topVarNum = 500)
data(exampleTumourNormal, package = "mesa")

# PCA on default beta-normalised matrix, keep 3 PCs
exampleTumourNormal %>%
    getDimRed(method = "PCA", nPC = 3)

# UMAP on the top 500 most variable windows
exampleTumourNormal %>%
    getDimRed(method = "UMAP", topVarNum = 500, n_neighbors = 5)

# Restrict DR to a set of regions (coerced from data.frame)
regs <- data.frame(seqnames = "chr7", start = 25002001, end = 25027900)
exampleTumourNormal %>%
    getDimRed(method = "PCA", regionsToOverlap = regs)

# Vectorised topVarNum runs (returns a list of results)
exampleTumourNormal %>%
    getDimRed(method = "UMAP", topVarNum = c(500, 2000), n_neighbors = 5) %>%
    slot("res") %>%
    names()

Description

Return a tidy summary of the most relevant QC fields from a qseaSet sampleTable. If core library metrics are missing, they are first added via addLibraryInformation().

Usage

getSampleQCSummary(qseaSet)

Arguments

Details

If the sampleTable lacks library metrics (e.g., valid_fragments), the function calls addLibraryInformation() to populate them. The output keeps sample_name and any columns whose names match the regex "valid_fragment|relH|hyperStable|ichorTumo" (if present), then orders rows by sample_name.

Value

A tibble with one row per sample containing:

• sample_name

• any available QC columns matching valid_fragment, relH, hyperStable, or ichorTumo. Columns not present in the sampleTable are silently omitted.

See Also

qsea::getSampleTable(), addLibraryInformation(), addHyperStableFraction()

Examples

data("exampleTumourNormal", package = "mesa")

# QC summary (adds library info if missing), ordered by sample_name
exampleTumourNormal %>%
    getSampleQCSummary() %>%
    head()

Description

Return character labels of the form "seqnames:start-end" for each genomic window/region.

Usage

getWindowNames(x)

Arguments

Details

If x is a qseaSet, regions are taken from qsea::getRegions(x). Otherwise the input is coerced to GRanges (see asValidGranges()) and labels are constructed as "seqnames:start-end".

Value

character() vector of window labels, one per region.

See Also

qsea::getRegions(), asValidGranges()

Examples

# From a GRanges (no intermediate objects)
GenomicRanges::GRanges(c("chr1", "chr2"),
    IRanges::IRanges(c(10, 20), c(15, 30))) %>%
    getWindowNames()

# From a data.frame (no intermediate objects)
data.frame(seqnames = c("chr1", "chr2"),
    start = c(100, 200),
    end = c(120, 220)) %>%
    getWindowNames()

# From a qseaSet shipped with mesa (if available)
data(exampleTumourNormal, package = "mesa")
exampleTumourNormal %>% getWindowNames() %>% head()

Description

Convenience wrapper for qsea::getRegions(), returning the genomic windows.

Usage

getWindows(qseaSet)

Arguments

Value

A GenomicRanges::GRanges of windows.

See Also

qsea::getRegions(), getWindowNames()

Examples

data(exampleTumourNormal, package = "mesa")
exampleTumourNormal %>% getWindows() %>% head()

Description

A chain object to liftOver hg19 to hg38

Usage

hg19ToHg38.over.chain

Format

A chain object to liftOver hg19 to hg38

Description

A GRanges object containing the Illumina Infinium 450k array probe locations for hg38

Usage

hg38_450kArrayGR

Format

A GRanges object with 485541 ranges and 2 metadata columns:

ID

Array probe ID

MASK_general

A field suggesting whether the probe could be unreliable (if TRUE) due to some kind of technical issue

Description

A GRanges object containing CpG islands in hg38 coordinates. Generated from http://hgdownload.cse.ucsc.edu/goldenpath/hg38/database/cpgIslandExt.txt.gz

Usage

hg38CpGIslands

Format

A GRanges object with 31144 ranges and 7 metadata columns:

length

Number of bases in each island

cpgNum

Number of CGs in each island

gcNum

Number of Cs or Gs in the island

perCpg

Percent CGs in the island

perGc

Percent of Cs or Gs in the island

obsExp

Unclear what this column means

Description

Genomic loci that are consistently methylated (“ultra-stable”) across many human tissues and diseases, mapped to GRCh38. Regions correspond to Illumina array probe loci (cg identifiers) derived as in Edgar et al. (2014), and are intended for QC and summary metrics (e.g., addHyperStableFraction()).

Usage

hg38UltraStableProbes

Format

A GRanges object with 974 ranges and 1 metadata column:

Probe_ID

Character cg identifier for the probe/locus (e.g., "cg11733071").

Details

• Seqnames: numeric chromosomes "1"–"22" (no "chr" prefix; UCSC style can be set if needed).

• Strand: "*" for all ranges.

• Widths: 3 bp windows (probe CpG ±1 bp).

• Genome metadata: currently unset; set to "hg38" if you need an explicit tag.

When combining with other GRanges, harmonise styles/metadata as needed, e.g. use GenomeInfoDb::seqlevelsStyle() to convert to "UCSC" ("chr1", …) and GenomeInfoDb::genome() to set "hg38".

Source

Processed from public array resources following Edgar et al. (2014).

References

Edgar R, Tan PPC, Portales-Casamar E, Pavlidis P (2014). Meta-analysis of human methylomes reveals stably methylated sequences surrounding CpG islands associated with high gene expression. https://pubmed.ncbi.nlm.nih.gov/25493099/

See Also

addHyperStableFraction()

Examples

data(hg38UltraStableProbes)

# Basic overview
hg38UltraStableProbes
length(hg38UltraStableProbes) # 974
all(GenomicRanges::width(hg38UltraStableProbes) == 3) # TRUE (3-bp windows)

# The object uses numeric chromosomes and an unset genome tag
GenomeInfoDb::seqlevels(hg38UltraStableProbes)[1:5]
GenomeInfoDb::genome(hg38UltraStableProbes) # empty

# Harmonise style/metadata if needed
gr <- hg38UltraStableProbes
GenomeInfoDb::seqlevelsStyle(gr) <- "UCSC" # or "NCBI" / "Ensembl"
#' #   (for 1,2... instead of chr1, chr2...)
GenomeInfoDb::genome(gr) <- "hg38" # or "GRCh38"

# Count overlaps with a toy region
toy <- GenomicRanges::GRanges("1", IRanges::IRanges(951160, 951170))
GenomicRanges::countOverlaps(toy, hg38UltraStableProbes)

# With UCSC-style example
toy_ucsc <- GenomicRanges::GRanges("chr1", IRanges::IRanges(951160, 951170))
GenomicRanges::countOverlaps(toy_ucsc, gr) # after style harmonisation above

Description

Predicate helper used in input validation and branching logic.

Usage

is.qseaSet(x)

Arguments

Value

logical(1): TRUE if x inherits from class "qseaSet", otherwise FALSE.

See Also

qsea::createQseaSet(), qsea::getSampleTable(), base::inherits()

Examples

data(exampleTumourNormal, package = "mesa")

# TRUE for qseaSet objects
exampleTumourNormal %>% is.qseaSet()

# FALSE for non-qseaSet objects
iris %>% is.qseaSet()

Description

Extend dplyr::left_join() to operate on the sampleTable slot of a mesaDimRed object.

Usage

## S3 method for class 'mesaDimRed'
left_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  keep = NULL,
  ...
)

Arguments

Value

A mesaDimRed object:

• sampleTable updated with y via a left join.

Examples

new <- data.frame(sample_name = c("Colon1_N", "Colon2_N"), foo = c(1, 2))

exampleTumourNormal %>%
    getPCA() %>%
    left_join(new, by = join_by(sample_name)) %>%
    getSampleTable()

Description

Extends dplyr::left_join() to merge a data frame into a qseaSet's sampleTable. Useful for adding annotations or metadata to samples.

Usage

## S3 method for class 'qseaSet'
left_join(
  x,
  y,
  by = NULL,
  copy = FALSE,
  suffix = c(".x", ".y"),
  keep = NULL,
  ...
)

Arguments

Value

A qseaSet object with its sampleTable updated:

• existing rows preserved,

• additional columns from y merged by sample name or other keys,

• duplicate column names disambiguated with suffix.

See Also

qsea::getSampleTable() to view metadata, mutate.qseaSet(), filter.qseaSet() for other dplyr verbs, dplyr::left_join() for join semantics.

Examples

data(exampleTumourNormal, package = "mesa")

# Join 'patient' (y) to 'sample_name' (x)
newData <- tibble::tibble(
    patient = c("Colon1", "Colon2", "Lung1", "Lung3"),
    new = 1:4
)
exampleTumourNormal %>%
    left_join(newData, by = c("sample_name" = "patient")) %>%
    qsea::getSampleTable()

Description

Convert genomic intervals from UCSC hg19 to hg38/GRCh38 using a preloaded chain object.

Usage

liftOverHg19(grOrDf)

Arguments

Details

Internally, the input is coerced to a GRanges (adding "chr" if needed), then lifted over via rtracklayer with the chain stored in mesa::hg19ToHg38.over.chain. Unmappable or split mappings are handled by rtracklayer::liftOver(), and unmapped intervals are discarded when the result is unlisted. The returned object is tagged as "hg38".

Value

A GRanges-class in hg38 coordinates (with GenomeInfoDb::genome(x) <- "hg38"). Intervals that cannot be mapped by the chain are dropped (i.e., may return fewer ranges than provided).

See Also

rtracklayer::liftOver(), GenomicRanges::GRanges

Examples

# Data.frame input; mix of 'chr' and no 'chr' is fine
data.frame(
    seqnames = c("1", "chr2"),
    start    = c(100000, 200000),
    end      = c(100100, 200100)
) %>%
    liftOverHg19() %>%
    {
        GenomeInfoDb::genome(.)
    }

# GRanges input (some intervals may not map and can be dropped)
gr <- GenomicRanges::GRanges("chr1", IRanges::IRanges(100000, 100300))
liftOverHg19(gr)

Description

Construct a tibble of all pairwise contrasts between levels of a categorical variable in the sample table of a qsea::qseaSet.

Usage

makeAllContrasts(qseaSet, variable)

Arguments

Details

Levels are inferred from unique(qsea::getSampleTable(qseaSet)[[variable]]). Pairs are formed between all distinct levels; no self-contrasts are included.

Value

A tibble with two columns:

• group1 – the first level of the pair,

• group2 – the second level of the pair. Each row represents one unordered pair of distinct levels (i.e., all combinations without repetition).

See Also

Other DMR-detection: calculateDMRs(), fitQseaGLM(), getDMRsData()

Examples

data(exampleTumourNormal, package = "mesa")

# All pairwise contrasts among levels of 'type'
exampleTumourNormal %>%
    makeAllContrasts("type")

Description

Build a qseaSet from a sampleTable that lists BAM files and sample metadata, tile the genome into fixed windows, optionally remove blacklisted regions, and load coverage and (optionally) CNV information according to the selected methods. This is typically the first step in a mesa/qsea workflow.

Usage

makeQset(
  sampleTable,
  BSgenome,
  chrSelect,
  windowSize = 300,
  CNVwindowSize = 1e+06,
  fragmentType = NULL,
  fragmentLength = NULL,
  fragmentSD = NULL,
  CNVmethod = "HMMdefault",
  coverageMethod = "PairedAndR1s",
  minMapQual = 10,
  minInsertSize = 70,
  maxInsertSize = 1000,
  minReferenceLength = 30,
  badRegions = NULL,
  properPairsOnly = FALSE,
  hmmCopyGC = NULL,
  hmmCopyMap = NULL,
  maxPatternDensity = 0.05,
  enrichmentMethod = "blind1-15",
  parallel = getMesaParallel()
)

Arguments

Details

Inputs & requirements

• sampleTable must include at least:

  • sample_name (unique per sample),

  • file_name (path to MeDIP/capture BAM),

  • group (string label). If the selected CNVmethod uses inputs ("HMMdefault", "qseaInput"), also include input_file (path to matched Input BAM).

• BAMs should be coordinate-sorted. For paired data, running ⁠samtools fixmate⁠ is recommended so MAPQ tags are available (see minMapQual note below).

• BSgenome must be an installed genome package string (e.g. "BSgenome.Hsapiens.NCBI.GRCh38"), which provides chromosome lengths and sequences for tiling windows.

Windows & blacklist

• Chromosomes in chrSelect are tiled into non-overlapping windows of size windowSize (bp). Windows overlapping badRegions (if provided) are removed.

Coverage loading

• coverageMethod = "PairedAndR1s" loads proper pairs and, optionally, high-MAPQ R1 singletons; governed by minMapQual, minInsertSize, maxInsertSize, properPairsOnly, minReferenceLength.

• coverageMethod = "qseaPaired" delegates to qsea::addCoverage() with paired settings.

• fragmentType can set defaults for fragmentLength/fragmentSD ("Sheared" or "cfDNA"); otherwise supply both explicitly.

CNV options

• "qseaInput": run qsea::addCNV() using input_file BAMs.

• "HMMdefault": use mesa's addHMMcopyCNV() with supplied defaults for GRCh38 GC/mappability tracks (objects like gc_hg38_1000kb, map_hg38_1000kb, chosen by CNVwindowSize).

• "MeCap": estimate CNV from MeCap BAMs (file_name).

• "None": do not compute CNV.

Parallelisation

• If parallel = TRUE, computation uses the currently registered BiocParallel backend (see BiocParallel::register()); otherwise runs serially. Use setMesaParallel() to toggle package-wide.

Additional processing

• Adds CpG density via qsea::addPatternDensity(pattern = "CG"), and enrichment parameters, allowing for estimation of beta values.

Value

A qseaSet containing:

• tiled (and optionally blacklisted) genomic windows;

• the input sampleTable as sample metadata;

• loaded coverage according to coverageMethod;

• CNV results depending on CNVmethod;

• CpG density and enrichment parameters;

• recorded processing parameters in ⁠@parameters⁠.

See Also

qsea::createQseaSet(), qsea::addCoverage(), qsea::addCNV(), addHMMcopyCNV(), setMesaParallel(), BSgenome::available.genomes()

Examples

# Minimal runnable sketch if MEDIPSData is installed
if (requireNamespace("MEDIPSData", quietly = TRUE)) {
    sampleTable <- data.frame(
        sample_name = c("Normal1", "Tumour1"),
        group = c("Normal", "Tumour"),
        file_name = c(
            system.file("extdata", "NSCLC_MeDIP_1N_fst_chr_20_21_22.bam",
                package = "MEDIPSData", mustWork = TRUE),
            system.file("extdata", "NSCLC_MeDIP_1T_fst_chr_20_21_22.bam",
                package = "MEDIPSData", mustWork = TRUE)
        )
    )

    sampleTable %>%
        makeQset(
            BSgenome          = "BSgenome.Hsapiens.UCSC.hg19",
            chrSelect         = paste0("chr", 20:22),
            windowSize        = 300,
            fragmentLength    = 200,
            fragmentSD        = 50,
            CNVmethod         = "None",
            coverageMethod    = "PairedAndR1s",
            properPairsOnly   = FALSE,
            minMapQual        = 10
        )
}

Description

Create a wide table for ML/visualisation where rows are samples and columns are genomic windows, using one normalisation/measure.

Usage

makeTransposedTable(qseaSet, normMethod = "nrpm", ...)

Arguments

Details

Internally obtains a window × sample matrix for normMethod, transposes it to sample × window, and (if requested) appends selected sample-table columns. Window columns are typically named "chr:start-end". If measure suffixes are present in column names, they may be cleaned to bare sample/window names (see removeNormMethodSuffix()).

Value

A tibble with one row per sample and one column per genomic window, plus any appended sample-level metadata.

See Also

getDataTable(), removeNormMethodSuffix(), countWindowsAboveCutoff()

Other table-helpers: getBetaTable(), getCountTable(), getDataTable(), getNRPMTable(), getSampleGroups2(), removeLibraryFactors(), removeNormMethodSuffix()

Examples

data(exampleTumourNormal, package = "mesa")

# NRPM features (sample × first few windows) with a group column appended
exampleTumourNormal %>%
  makeTransposedTable(normMethod = "nrpm", group) %>%
  dplyr::select(1:6) %>%
  head()

# Beta features; inspect dimensions
exampleTumourNormal %>%
  makeTransposedTable(normMethod = "beta") %>%
  dim()

Description

Fixed-size bins with average mappability per bin. Bins are ordered by chromosome size (not strictly numeric).

Usage

data(map_hg38_1000kb); data(map_hg38_500kb); data(map_hg38_50kb)

map_hg38_50kb

map_hg38_500kb

Format

One of:

1. A data.table/data.frame with columns:

   chrom

   Chromosome (e.g., "chr1", ..., "chrX", "chrY").

   start

   0-based start of the bin.

   end

   1-based end of the bin.

   map

   Average mappability in the bin (0–1).

2. A GRanges with genomic bins and mcols(.)$map storing the average mappability (0–1) per bin.

An object of class data.table (inherits from data.frame) with 61775 rows and 4 columns.

An object of class data.table (inherits from data.frame) with 6188 rows and 4 columns.

Value

map_hg38_1000kb, map_hg38_500kb, and map_hg38_50kb return objects as described in @format with bin sizes of 1000 kb, 500 kb, and 50 kb, respectively.

Source

https://github.com/broadinstitute/ichorCNA

Description

Aggregates one or more dimensionality reduction (DR) results (e.g., PCA/UMAP) computed on a common set of samples.

Usage

mesaDimRed(res, sampleTable, samples, params, dataTable = data.frame())

## S4 method for signature 'mesaDimRed'
show(object)

Arguments

Value

A mesaDimRed object:

• stores DR results in res,

• carries sample metadata in sampleTable,

• records the samples in samples,

• and persists parameters/data in params / dataTable.

Slots

res

list Individual DR result objects (e.g., mesaPCA, mesaUMAP).

sampleTable

data.frame Sample annotations; row names are sample IDs.

samples

character() Vector of sample IDs used.

params

list Parameters used to generate results in res.

dataTable

data.frame Optional matrix/data used to compute DR (for reproducibility).

See Also

mesaPCA, mesaUMAP

Examples

st <- data.frame(sample_name = c("S1", "S2"),
    group = c("A", "B"),
    row.names = "sample_name")
md <- mesaDimRed(res = list(), sampleTable = st,
    samples = rownames(st), params = list(),
    dataTable = data.frame())
md

Description

Stores a PCA fit (from stats::prcomp()) computed over methylation windows.

Usage

mesaPCA(prcomp, windows)

## S4 method for signature 'mesaPCA'
show(object)

Arguments

Value

A mesaPCA object containing the PCA fit and window IDs.

Slots

prcomp

prcomp A PCA fit returned by stats::prcomp().

windows

character() Window IDs used in the PCA.

See Also

mesaDimRed

Examples

set.seed(1)
x <- matrix(rnorm(20), nrow = 5, ncol = 4,
    dimnames = list(paste0("S", 1:5), paste0("W", 1:4)))
pc <- stats::prcomp(x, center = TRUE, scale. = FALSE)
mp <- mesaPCA(prcomp = pc, windows = colnames(x))
mp

Description

Stores per-sample coordinates from a UMAP embedding computed over methylation windows.

Usage

mesaUMAP(points, windows)

## S4 method for signature 'mesaUMAP'
show(object)

Arguments

Value

A mesaUMAP object containing UMAP coordinates and window IDs.

Slots

points

data.frame One row per sample with UMAP coordinates (e.g., UMAP1, UMAP2). Row names are sample IDs.

windows

character() Window IDs used to compute the embedding.

See Also

mesaDimRed

Examples

pts <- data.frame(UMAP1 = c(0.1, -0.2, 0.0),
    UMAP2 = c(0.3, 0.1, -0.1))
rownames(pts) <- paste0("S", 1:3)
mu <- mesaUMAP(points = pts, windows = c("w1", "w2", "w3"))
mu