BigWig Import and Export
These functions support the import and export of the UCSC BigWig format, a compressed, binary form of WIG/BEDGraph with a spatial index and precomputed summaries. These functions do not work on Windows.
## S4 method for signature 'BigWigFile,ANY,ANY'
import(con, format, text,
selection = BigWigSelection(which, ...),
which = con,
as = c("GRanges", "RleList", "NumericList"), ...)
import.bw(con, ...)
## S4 method for signature 'ANY,BigWigFile,ANY'
export(object, con, format, ...)
## S4 method for signature 'GenomicRanges,BigWigFile,ANY'
export(object, con, format,
dataFormat = c("auto", "variableStep", "fixedStep",
"bedGraph"), compress = TRUE, fixedSummaries = FALSE)
export.bw(object, con, ...)con |
A path, URL or |
object |
The object to export, should be an |
format |
If not missing, should be “bigWig” or “bw” (case insensitive). |
text |
Not supported. |
as |
Specifies the class of the return object. Default is
|
selection |
A |
which |
A range data structure coercible to |
dataFormat |
Probably best left to “auto”. Exists only for historical reasons. |
compress |
If |
fixedSummaries |
If |
... |
Arguments to pass down to methods to other methods. For
import, the flow eventually reaches the |
A GRanges (default), RleList or NumericList.
GRanges return ranges with non-zero score values in a score
metadata column. The length of the NumericList is the same length
as the selection argument (one list element per range).
The return order in the NumericList matches the order of the
BigWigSelection object.
BigWigFile objectsA BigWigFile object, an extension of
RTLFile is a reference to a BigWig file. To cast
a path, URL or connection to a BigWigFile, pass it to the
BigWigFile constructor.
BigWig files are more complex than most track files, and there are a
number of methods on BigWigFile for accessing the additional
information:
seqinfo(x):
Gets the Seqinfo object
indicating the lengths of the sequences for the intervals in the
file. No circularity or genome information is available.
summary(ranges = as(seqinfo(object), "GenomicRanges"), size
= 1L, type = c("mean", "min", "max", "coverage", "sd"),
defaultValue = NA_real_),
as = c("GRangesList", "RleList", "matrix"), ...:
Aggregates the intervals in the file
that fall into ranges, which should be something
coercible to GRanges. The aggregation essentially
compresses each sequence to a length of size. The
algorithm is specified by type; available algorithms
include the mean, min, max, coverage (percent sequence covered
by at least one feature), and standard deviation. When a window
contains no features, defaultValue is assumed. The result
type depends on as, and can be a GRangesList, RleList or
matrix, where the number elements (or rows) is equal to the
length of ranges. For as="matrix", there must be
one unique value of size, which is equal to the number of
columns in the result. The as="matrix" case is the only
one that supports a size greater than the width of the
corresponding element in ranges, where values are
interpolated to yield the matrix result. The
driving use case for this is visualization of coverage when the
screen space is small compared to the viewed portion of the
sequence. The operation is very fast, as it leverages cached
multi-level summaries present in every BigWig file.
If a summary statistic is not available / cannot be computed
for a given range a warning is thrown and the defaultValue
NA_real_ is returned.
When accessing remote data, the UCSC library caches data in the
‘/tmp/udcCache’ directory. To clean the cache, call
cleanBigWigCache(maxDays), where any files older than
maxDays days old will be deleted.
BigWigFileList objectsA BigWigFileList() provides a convenient way of managing a list
of BigWigFile instances.
Michael Lawrence
wigToBigWig for converting a WIG file to BigWig.
if (.Platform$OS.type != "windows") {
test_path <- system.file("tests", package = "rtracklayer")
test_bw <- file.path(test_path, "test.bw")
## GRanges
## Returns ranges with non-zero scores.
gr <- import(test_bw)
gr
which <- GRanges(c("chr2", "chr2"), IRanges(c(1, 300), c(400, 1000)))
import(test_bw, which = which)
## RleList
## Scores returned as an RleList is equivalent to the coverage.
## Best option when 'which' or 'selection' contain many small ranges.
mini <- narrow(unlist(tile(which, 50)), 2)
rle <- import(test_bw, which = mini, as = "RleList")
rle
## NumericList
## The 'which' is stored as metadata:
track <- import(test_bw, which = which, as = "NumericList")
metadata(track)
## Not run:
test_bw_out <- file.path(tempdir(), "test_out.bw")
export(test, test_bw_out)
## End(Not run)
bwf <- BigWigFile(test_bw)
track <- import(bwf)
seqinfo(bwf)
summary(bwf) # for each sequence, average all values into one
summary(bwf, range(head(track))) # just average the first few features
summary(bwf, size = seqlengths(bwf) / 10) # 10X reduction
summary(bwf, type = "min") # min instead of mean
summary(bwf, track, size = 10, as = "matrix") # each feature 10 windows
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.