poolFromGRlist {MethylIT}R Documentation

Methylation pool from a list of GRanges objects with methylation read counts


This function will build a GRanges methylation pool from a list of GRanges objects


poolFromGRlist(LR, stat = c("mean", "median", "jackmean", "sum"),
  num.cores = 1, tasks = 0L, prob = FALSE, column = 1L,
  jstat = c("sum", "mean", "median"), verbose = TRUE, ...)



list of GRanges objects to build a virtual individual (methylation pool)


statistic used to estimate the methylation pool: row "mean", row "median", row "sum", or Jacknife row mean ("jackmean") of methylated and unmethylated read counts across individuals. Notice that, for only two samples, "jackmean" makes not sense. Since the centrality statistics are sensitive to extreme values, stat = 'sum' is an atractive option. However, in this last case, a further correction for the minimun coverage for the reference sample must be taken into account in a furhter estimation of the Hellinger divergence of methylation levels, which is explained in the detail section from the help of function estimateDivergence. A conservative option is "mean", which will return the group centroid.


The number of cores to use, i.e. at most how many child processes will be run simultaneously (see bplapply function from BiocParallel package).


integer(1). The number of tasks per job. Value must be a scalar integer >= 0L. In this documentation a job is defined as a single call to a function, such as bplapply, bpmapply etc. A task is the division of the X argument into chunks. When tasks == 0 (default), X is divided as evenly as possible over the number of workers (see MulticoreParam from BiocParallel package).


Logic. Whether the variable for pooling is between 0 and 1 (a probability), e.g., methylation levels. If TRUE, then Fisher's transformation is applied, the row mean is computed for each cytosine site and returned in the original measurement scale between 0 and 1 by using the inverse of Fisher's transformation.


If prob == TRUE, then the 'column' from the LR metacolumns where the prob values are found must be provided. Otherwise, column = 1L.


If stat = "jackmean", then any of the 'stat' possible values: "sum", "mean", or "median" can be used to compute, for each cytosine site, the Jacknife vector of the selected statistics and then to compute the corresponding mean. Default is jstat = "sum".


If TRUE, prints the function log to stdout


Additional parameters for 'uniqueGRanges' function.


The list of GRanges objects (LR) provided to build a virtual methylome should be an output of the function 'readCounts2GRangesList' or at least each GRanges must have the columns named "mC" and "uC", for the read counts of methylated and unmethylated cytosines, respectively.


A GRanges object


gr1 <- makeGRangesFromDataFrame(
    data.frame(chr = "chr1", start = 11:15, end = 11:15,
               strand = '*', mC = 1, uC = 1:5),
    keep.extra.columns = TRUE)
gr2 <- makeGRangesFromDataFrame(
    data.frame(chr = "chr1", start = 11:15, end = 11:15,
               strand = '*', mC = 1, uC = 1:5),
    keep.extra.columns = TRUE)

answer <- poolFromGRlist(list(gr1, gr2), stat = 'mean', verbose = FALSE)

[Package MethylIT version 0.3.1 ]