Description Usage Arguments Value Author(s) See Also Examples
Given a bin, it is possible to calculate PSI/PIR metric using junctions to estimate changes in the use of it along different conditions.
1 2 3 4 5 6 7 8 | AsDiscover(counts,
targets,
features,
bam,
l,
pair,
threshold,
cores)
|
counts |
An object of class ASpliCounts. |
targets |
A dataframe containing sample, bam and condition columns |
features |
An object of class ASpliFeatures. |
bam |
A list with BAM files |
l |
Read length of sequenced read. Default 100L |
pair |
Vector of length two, either numeric or character, providing the pair of groups to be compared |
threshold |
Minimun number of reads supporting junctions. Default=5 |
cores |
Number of proccesors to use |
An object of class ASpliAS
irPIR |
reports: event, e1i counts (J1), ie1 counts (J2), j_within (J3), PIR by condition. J1, J2, J3 sum of junctions (J1, J2, J3) by condition. |
altPSI |
reports: event, J1 (start), J2 (end), J3 (exclusion), PSI. J1, J2, J3 sum of junctions (J1, J2, J3) by condition. |
esPSI |
reports: event, J1 (start), J2 (end), J3 (exclusion), PSI. J1, J2, J3 sum of junctions (J1, J2, J3) by condition. |
junctionsPIR |
PIR metric for each experimental junction using e1i and ie2 counts. Exclusion junction is the junction itself. This output helps to discover new introns as well as new retention events |
junctionsPSI |
Given a junction, it is possible to analyze if it shares start, end or both with another junction. If so, is because there is more than one way for/of splicing. Using strand information it is possible to classify those pair of junctions into Alt5'ss, Alt3'ss or ES. Ratio between them along samples is reported. |
Estefania Mancini, Marcelo Yanovsky and Ariel Chernomoretz
Accesors: irPIR, altPSI, esPSI, junctionsPIR,junctionsPSI
Export: writeAS
1 2 3 4 5 6 7 8 9 10 11 12 | library(RNAseqData.HNRNPC.bam.chr14)
chr14 <- system.file("extdata","chr14.sqlite", package="ASpli")
genome <- loadDb(chr14)
features <- binGenome(genome)
targets <- data.frame(bam=RNAseqData.HNRNPC.bam.chr14_BAMFILES,
condition=c(rep("CT",4),rep("KD",4)))
bam <- loadBAM(targets)
counts <- readCounts(features, bam, l=100L, maxISize=50000)
group <- factor(c(rep("CT",4),rep("KD",4)))
pair <- c("CT","KD")
as <- AsDiscover(counts, targets, features, bam, l=100L, pair=pair)
writeAS(as=as, output.dir="only_as")
|
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