AsDiscover {ASpli} | R Documentation |
Given a bin, it is possible to calculate PSI/PIR metric using junctions to estimate changes in the use of it along different conditions. PSI or PIR metric is calculated for each bin and experimental condition. The selection of which metric is used is based on the kind of splicing event associated with each bin.
AsDiscover( counts, targets, features, bam, readLength, threshold , cores )
counts |
An object of class ASpliCounts. |
targets |
A dataframe containing sample, bam and experimental factors as columns and samples as rows. |
features |
An object of class ASpliFeatures. |
bam |
A list with BAM files contents. |
readLength |
Read length of sequenced read. Default 100L |
threshold |
Minimum number of reads supporting junctions. Default=5 |
cores |
Number of processing cores 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. Ratio between them along samples is reported. |
Estefania Mancini, Marcelo Yanovsky and Ariel Chernomoretz
Accesors: irPIR
, altPSI
, esPSI
,
junctionsPIR
, junctionsPSI
Export: writeAS
# Create a transcript DB from gff/gtf annotation file. # Warnings in this examples can be ignored. library(GenomicFeatures) genomeTxDb <- makeTxDbFromGFF( system.file('extdata','genes.mini.gtf', package="ASpli") ) # Create an ASpliFeatures object from TxDb features <- binGenome( genomeTxDb ) # Define bam files, sample names and experimental factors for targets. bamFileNames <- c( "A_C_0.bam", "A_C_1.bam", "A_C_2.bam", "A_D_0.bam", "A_D_1.bam", "A_D_2.bam" ) targets <- data.frame( row.names = paste0('Sample_',c(1:6)), bam = system.file( 'extdata', bamFileNames, package="ASpli" ), factor1 = c( 'C','C','C','D','D','D') ) # Load reads from bam files bams <- loadBAM( targets ) # Read counts from bam files counts <- readCounts( features, bams, targets, cores = 1, readLength = 100, maxISize = 50000 ) # Calculate differential usage of genes, bins and junctions du <- DUreport( counts, targets ) du <- junctionDUreport( counts, targets, appendTo = du) # Calculate PSI / PIR for bins and junction. as <- AsDiscover( counts, targets, features, bams, readLength = 100, threshold = 5, cores = 1 ) writeAS( as = as, output.dir = "only_as" )