extractPatterns: extractPatterns
In BBCG/epialleleR: Fast, Epiallele-Aware Methylation Caller and Reporter

extractPatterns

R Documentation

extractPatterns

Description

This function extracts methylation patterns (epialleles) for a given genomic region of interest.

Usage

extractPatterns(
  bam,
  bed,
  bed.row = 1,
  zero.based.bed = FALSE,
  match.min.overlap = 1,
  extract.context = c("CG", "CHG", "CHH", "CxG", "CX"),
  min.context.freq = 0.01,
  clip.patterns = FALSE,
  strand.offset = c(CG = 1, CHG = 2, CHH = 0, CxG = 0, CX = 0)[extract.context],
  highlight.positions = c(),
  ...,
  verbose = TRUE
)

Arguments

`bam`	BAM file location string OR preprocessed output of `preprocessBam` function. Read more about BAM file requirements and BAM preprocessing at `preprocessBam`.
`bed`	Browser Extensible Data (BED) file location string OR object of class `GRanges` holding genomic coordinates for regions of interest. It is used to match sequencing reads to the genomic regions prior to eCDF computation. The style of seqlevels of BED file/object must match the style of seqlevels of the BAM file/object used. The BED/`GRanges` rows are not sorted internally.
`bed.row`	single non-negative integer specifying what 'bed' region should be included in the output (default: 1).
`zero.based.bed`	boolean defining if BED coordinates are zero based (default: FALSE).
`match.min.overlap`	integer for the smallest overlap between read's and BED/`GRanges` start or end positions during matching of capture-based NGS reads (default: 1).
`extract.context`	string defining cytosine methylation context used to report: "CG" (the default) – CpG cytosines (called as zZ) "CHG" – CHG cytosines (xX) "CHH" – CHH cytosines (hH) "CxG" – CG and CHG cytosines (zZxX) "CX" – all cytosines
`min.context.freq`	real number in the range [0;1] (default: 0.01). Genomic positions that are covered by smaller fraction of patterns (e.g., with erroneous context) won't be included in the report.
`clip.patterns`	boolean if patterns should not extend over the edge of 'bed' region (default: FALSE).
`strand.offset`	single non-negative integer specifying the offset of bases at the reverse (-) strand compared to the forward (+) strand. Allows to "merge" genomic positions when methylation is symmetric (in CG and CHG contexts). By default, equals 1 for 'extract.context'=="CG", 2 for "CHG", or 0 otherwise.
`highlight.positions`	integer vector with genomic positions of bases to include in every overlapping pattern. Allows to visualize the distribution of single-nucleotide variations (SNVs) among methylation patterns. 'highlight.positions' takes precedence if any of these positions overlap with within-the-context positions of methylation pattern.
`...`	other parameters to pass to the `preprocessBam` function. Options have no effect if preprocessed BAM data was supplied as an input.
`verbose`	boolean to report progress and timings (default: TRUE).

Details

The function matches reads (for paired-end sequencing alignment files - read pairs as a single entity) to the genomic region provided in a BED file/GRanges object, extracts methylation statuses of bases within those reads, and returns a data frame which can be used for plotting of DNA methylation patterns.

Value

data.table object containing per-read (pair) base methylation information for the genomic region of interest. The report columns are:

seqnames – read (pair) reference sequence name
strand – read (pair) strand
start – start of the read (pair)
end – end of the read (pair)
nbase – number of within-the-context bases for this read (pair)
beta – beta value of this read (pair), calculated as a ratio of the number of methylated within-the-context bases to the total number of within-the-context bases
pattern – hex representation of 64-bit FNV-1a hash calculated for all reported base positions and bases in this read (pair). This hash value depends only on included genomic positions and their methylation call string chars (hHxXzZ) or nucleotides (ACGT, for highlighted bases only), thus it is expected to be unique for every methylation pattern, although equal for identical methylation patterns independently on read (pair) start, end, or strand (when correct 'strand.offset' is given)
... – columns for each genomic position that hold corresponding methylation call string char, or NA if position is not present in the read (pair)

Examples

  # amplicon data
  amplicon.bam <- system.file("extdata", "amplicon010meth.bam",
                              package="epialleleR")
  amplicon.bed <- system.file("extdata", "amplicon.bed",
                              package="epialleleR")
  
  # let's get our patterns
  patterns <- extractPatterns(bam=amplicon.bam, bed=amplicon.bed, bed.row=3)
  nrow(patterns)  # read pairs overlap genomic region of interest
  
  # these are positions of bases
  base.positions <- grep("^[0-9]+$", colnames(patterns), value=TRUE)
  
  # let's make a summary table with counts of every pattern
  patterns.summary <- patterns[, c(lapply(.SD, unique), .N),
                               by=.(pattern, beta), .SDcols=base.positions]
  nrow(patterns.summary)  # unique methylation patterns
  
  # let's melt and plot them
  plot.data <- data.table::melt.data.table(patterns.summary,
    measure.vars=base.positions, variable.name="pos", value.name="base")
  
  # continuous positions, nonunique patterns according to their counts
  if (require("ggplot2", quietly=TRUE) & require("ggstance", quietly=TRUE)) {
    ggplot(na.omit(plot.data)[N>1],
           aes(x=as.numeric(as.character(pos)), y=factor(N),
               group=pattern, color=factor(base, levels=c("z","Z")))) +
      geom_line(color="grey", position=position_dodgev(height=0.5)) +
      geom_point(position=position_dodgev(height=0.5)) +
      scale_colour_grey(start=0.8, end=0) +
      theme_light() +
      labs(x="position", y="count", title="epialleles", color="base")
  }
  
  # upset-like plot of all patterns, categorical positions, sorted by counts
  if (require("ggplot2", quietly=TRUE) & require("gridExtra", quietly=TRUE)){
    grid.arrange(
      ggplot(na.omit(plot.data),
             aes(x=pos, y=reorder(pattern,N),
                 color=factor(base, levels=c("z","Z")))) +
        geom_line(color="grey") +
        geom_point() +
        scale_colour_grey(start=0.8, end=0) +
        theme_light() +
        scale_x_discrete(breaks=function(x){x[c(rep(FALSE,5), TRUE)]}) +
        theme(axis.text.y=element_blank(), legend.position="none") +
        labs(x="position", y=NULL, title="epialleles", color="base"),
      
      ggplot(unique(na.omit(plot.data)[, .(pattern, N, beta)]),
             aes(x=N+0.5, y=reorder(pattern,N), alpha=beta, label=N)) +
        geom_col() +
        geom_text(alpha=0.5, nudge_x=0.2, size=3) +
        scale_x_log10() +
        theme_minimal() +
        theme(axis.text.y=element_blank(), legend.position="none") +
        labs(x="count", y=NULL, title=""),
      ncol=2, widths=c(0.75, 0.25)
    )
  }

BBCG/epialleleR documentation built on March 24, 2024, 11:32 p.m.