regionPerReadLength | R Documentation |
This is defined as: Given some transcript region (like CDS), get coverage per position. By default only returns positions that have hits, set drop.zero.dt to FALSE to get all 0 positions.
regionPerReadLength(
grl,
reads,
acceptedLengths = NULL,
withFrames = TRUE,
scoring = "transcriptNormalized",
weight = "score",
exclude.zero.cov.grl = TRUE,
drop.zero.dt = TRUE,
BPPARAM = bpparam()
)
grl |
a |
reads |
a |
acceptedLengths |
an integer vector (NULL), the read lengths accepted. Default NULL, means all lengths accepted. |
withFrames |
logical TRUE, add ORF frame (frame 0, 1, 2), starting on first position of every grl. |
scoring |
a character (transcriptNormalized), which meta coverage scoring ? one of (zscore, transcriptNormalized, mean, median, sum, sumLength, fracPos), see ?coverageScorings for more info. Use to decide a scoring of hits per position for metacoverage etc. Set to NULL if you do not want meta coverage, but instead want per gene per position raw counts. |
weight |
(default: 'score'), if defined a character name of valid meta column in subject. GRanges("chr1", 1, "+", score = 5), would mean score column tells that this alignment region was found 5 times. Formats which loads a score column like this: Bigwig, wig, ORFik ofst, collapsed bam, bedoc and .bedo. As do CAGEr CAGE files and many other package formats. You can also assign a score column manually. |
exclude.zero.cov.grl |
logical, default TRUE. Do not include ranges that does not have any coverage (0 reads on them), this makes it faster to run. |
drop.zero.dt |
logical, default TRUE. If TRUE and as.data.table is TRUE, remove all 0 count positions. This greatly speeds up and most importantly, greatly reduces memory usage. Will not change any plots, unless 0 count positions are used in some sense. |
BPPARAM |
how many cores/threads to use? default: bpparam() |
a data.table with lengths by coverage.
Other coverage:
coverageScorings()
,
metaWindow()
,
scaledWindowPositions()
,
windowPerReadLength()
# Raw counts per gene per position
cds <- GRangesList(tx1 = GRanges("1", 100:129, "+"))
reads <- GRanges("1", seq(79,129, 3), "+")
reads$size <- 28 # <- Set read length of reads
regionPerReadLength(cds, reads, scoring = NULL)
## Sum up reads in each frame per read length per gene
regionPerReadLength(cds, reads, scoring = "frameSumPerLG")
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