make_ref2compressed | R Documentation |
Create a ref2compressed function to compress GR gaps
make_ref2compressed(
gr,
gapWidth = 200,
keepValues = FALSE,
upstream = 50000,
upstreamGapWidth = gapWidth * 3,
downstream = 50000,
downstreamGapWidth = gapWidth * 3,
nBreaks = 7,
verbose = FALSE,
...
)
gr |
GRanges object containing regions not to compress. Regions which are unstranded gaps are compressed to fixed width. |
gapWidth |
integer value used for fixed gap width, or when NULL the gap width is defined as 3 times the median feature width. |
keepValues |
logical indicating whether to keep feature values in the GRanges data. |
upstream , downstream , upstreamGapWidth , downstreamGapWidth |
used to define the compression of coordinates upstream and downstream the supplied GRanges. In reality, the upstream range and upstream gap width defines a multiplier, and all upstream coordinates are compressed through zero. Similarly, all downstream coordinates are compressed to 10 billion, which is roughly 3 times the size of the human genome. |
nBreaks |
the default number of x-axis coordinate breaks used in ggplot labeling. |
verbose |
logical indicating whether to print verbose output. |
... |
additional arguments are ignored. |
This function takes a set of GRanges which are to be maintained with fixed aspect ratio, and it defines a function to compress coordinates of the gaps between GRanges features.
list with trans_grc
which is class "trans"
suitable
for use in ggplot2 functions; transform
a function that converts
chromosome coordinates to compressed coordinates; inverse
a function
that converts compressed coordinates to chromosome coordinates;
scale_x_grc
a function used similar to ggplot2::scale_x_continuous()
during ggplot2 creation; gr
a function that compresses coordinates
in a GRanges
object; grl
a function that compresses coordinates in
a GRangesList
object. Attributes "lookupCoordDF"
is a two-column
data.frame with chromosome coordinates and compressed coordinates,
which is used to create the other transformation functions via
stats::approx()
; "gapWidth"
the gap width used, since it can
be programmatically defined; "gr"
the GRanges
input data used
to train the transformation.
grl2df()
, test_junc_wide_gr
Other jam GRanges functions:
addGRLgaps()
,
addGRgaps()
,
annotateGRLfromGRL()
,
annotateGRfromGR()
,
assignGRLexonNames()
,
closestExonToJunctions()
,
combineGRcoverage()
,
exoncov2polygon()
,
findOverlapsGRL()
,
flattenExonsBy()
,
getFirstStrandedFromGRL()
,
getGRLgaps()
,
getGRcoverageFromBw()
,
getGRgaps()
,
grl2df()
,
jam_isDisjoint()
,
sortGRL()
,
spliceGR2junctionDF()
,
stackJunctions()
Other splicejam core functions:
exoncov2polygon()
,
gene2gg()
,
grl2df()
,
plotSashimi()
,
prepareSashimi()
Other jam RNA-seq functions:
assignGRLexonNames()
,
closestExonToJunctions()
,
combineGRcoverage()
,
defineDetectedTx()
,
detectedTxInfo()
,
exoncov2polygon()
,
flattenExonsBy()
,
getGRcoverageFromBw()
,
groups2contrasts()
,
internal_junc_score()
,
makeTx2geneFromGtf()
,
prepareSashimi()
,
runDiffSplice()
,
sortSamples()
,
spliceGR2junctionDF()
suppressPackageStartupMessages(library(GenomicRanges));
suppressPackageStartupMessages(library(ggplot2));
data(test_exon_wide_gr);
# To plot a simple GRanges object
widedf <- grl2df(test_exon_wide_gr);
ggWide <- ggplot(widedf, aes(x=x, y=y, group=id, fill=feature_type)) +
geom_polygon() +
colorjam::theme_jam() +
colorjam::scale_fill_jam() +
xlab("chr1") +
ggtitle("exons (introns as-is)")
print(ggWide);
# Now compress the introns keeping axis labels
ref2c <- make_ref2compressed(test_exon_wide_gr,
nBreaks=10);
ggWide2 <- ggWide +
scale_x_continuous(trans=ref2c$trans_grc) +
xlab("chr1 (compressed introns)") +
ggtitle("exons (compressed introns)")
print(ggWide2);
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