View source: R/21.get_UTR3eSet.R
get_UTR3eSet | R Documentation |
generate a UTR3eSet object with PDUI information for statistic tests
get_UTR3eSet(
sqlite_db,
normalize = c("none", "quantiles", "quantiles.robust", "mean", "median"),
...,
singleSample = FALSE
)
sqlite_db |
A path to the SQLite database for InPAS, i.e. the output of
|
normalize |
A character(1) vector, spcifying the normalization method. It can be "none", "quantiles", "quantiles.robust", "mean", or "median" |
... |
parameter can be passed into
|
singleSample |
A logical(1) vector, indicating whether data is prepared for analysis in a singleSample mode? Default, FALSE |
An object of UTR3eSet which contains following elements: usage: an GenomicRanges::GRanges object with CP sites info. PDUI: a matrix of PDUI PDUI.log2: log2 transformed PDUI matrix short: a matrix of usage of short form long: a matrix of usage of long form if singleSample is TRUE, one more element, signals, will be included.
Jianhong Ou, Haibo Liu
if (interactive()) {
library(BSgenome.Mmusculus.UCSC.mm10)
library(TxDb.Mmusculus.UCSC.mm10.knownGene)
genome <- BSgenome.Mmusculus.UCSC.mm10
TxDb <- TxDb.Mmusculus.UCSC.mm10.knownGene
## load UTR3 annotation and convert it into a GRangesList
data(utr3.mm10)
utr3 <- split(utr3.mm10, seqnames(utr3.mm10), drop = TRUE)
bedgraphs <- system.file("extdata", c(
"Baf3.extract.bedgraph",
"UM15.extract.bedgraph"
),
package = "InPAS"
)
tags <- c("Baf3", "UM15")
metadata <- data.frame(
tag = tags,
condition = c("Baf3", "UM15"),
bedgraph_file = bedgraphs
)
outdir <- tempdir()
write.table(metadata,
file = file.path(outdir, "metadata.txt"),
sep = "\t", quote = FALSE, row.names = FALSE
)
sqlite_db <- setup_sqlitedb(metadata = file.path(
outdir,
"metadata.txt"
), outdir)
addLockName(filename = tempfile())
coverage <- list()
for (i in seq_along(bedgraphs)) {
coverage[[tags[i]]] <- get_ssRleCov(
bedgraph = bedgraphs[i],
tag = tags[i],
genome = genome,
sqlite_db = sqlite_db,
outdir = outdir,
chr2exclude = "chrM"
)
}
data4CPsSearch <- setup_CPsSearch(sqlite_db,
genome,
chr.utr3 = utr3[["chr6"]],
seqname = "chr6",
background = "10K",
TxDb = TxDb,
hugeData = TRUE,
outdir = outdir,
minZ = 2,
cutStart = 10,
MINSIZE = 10,
coverage_threshold = 5
)
## polyA_PWM
load(system.file("extdata", "polyA.rda", package = "InPAS"))
## load the Naive Bayes classifier model from the cleanUpdTSeq package
library(cleanUpdTSeq)
data(classifier)
CPs <- search_CPs(
seqname = "chr6",
sqlite_db = sqlite_db,
genome = genome,
MINSIZE = 10,
window_size = 100,
search_point_START = 50,
search_point_END = NA,
cutEnd = 0,
adjust_distal_polyA_end = TRUE,
long_coverage_threshold = 2,
PolyA_PWM = pwm,
classifier = classifier,
classifier_cutoff = 0.8,
shift_range = 100,
step = 5,
outdir = outdir
)
utr3_cds_cov <- get_regionCov(
chr.utr3 = utr3[["chr6"]],
sqlite_db,
outdir,
phmm = FALSE
)
eSet <- get_UTR3eSet(sqlite_db,
normalize = "none",
singleSample = FALSE
)
test_out <- test_dPDUI(
eset = eSet,
method = "fisher.exact",
normalize = "none",
sqlite_db = sqlite_db
)
}
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