| get_UTR3eSet {InPAS} | 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))
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)
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,
removeScaffolds = TRUE,
BPPARAM = NULL)}
coverage_files <- assemble_allCov(sqlite_db,
outdir,
genome,
removeScaffolds = TRUE)
data4CPsSearch <- setup_CPsSearch(sqlite_db,
genome,
utr3,
background = "10K",
TxDb = TxDb,
removeScaffolds = TRUE,
BPPARAM = NULL,
hugeData = TRUE,
outdir = outdir)
## 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,
utr3 = utr3,
background = data4CPsSearch$background,
z2s = data4CPsSearch$z2s,
depth.weight = data4CPsSearch$depth.weight,
genome = genome,
MINSIZE = 10,
window_size = 100,
search_point_START =50,
search_point_END = NA,
cutStart = 10,
cutEnd = 0,
adjust_distal_polyA_end = TRUE,
coverage_threshold = 5,
long_coverage_threshold = 2,
PolyA_PWM = pwm,
classifier = classifier,
classifier_cutoff = 0.8,
shift_range = 100,
step = 5,
two_way = FALSE,
hugeData = TRUE,
outdir = outdir)
utr3_cds <- InPAS:::get_UTR3CDS(sqlite_db,
chr.utr3 = utr3[["chr6"]],
BPPARAM = NULL)
utr3_cds_cov <- get_regionCov(chr.utr3 = utr3[["chr6"]],
sqlite_db,
outdir,
BPPARAM = NULL,
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)
}