This function will build a GRanges methylation pool from a list of GRanges objects
List of GRanges objects to build a virtual individual (methylation
pool). It is assumed that the list of GRanges was obtained with
readCounts2GRangesList. That is, the metacolumn from each
GRanges object must contain the columns named 'mC' (number of reads signaling
methylated cytosine) and 'uC' (number of reads signaling non-methylated
cytosine). If more than two columns are carried on each GRanges object, then
the parameter "columns" denoting the column numbers where "uC" and "mC" are
located must be passed to
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 attractive option. However,
in this last case, a further correction for the minimum 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
conservative option is 'mean', which will return the group centroid.
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'.
A parameter to pass for
If TRUE, prints the function log to stdout
Additional parameters for
A GRanges object
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.
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)