data(exampleDataSet,package="JctSeqData");
jscs <- fitJunctionSeqDispersionFunction(jscs);
## Not run:
# #Full example (from scratch):
# ########################################
# #Set up example data:
# decoder.file <- system.file(
# "extdata/annoFiles/decoder.bySample.txt",
# package="JctSeqData");
# decoder <- read.table(decoder.file,
# header=TRUE,
# stringsAsFactors=FALSE);
# gff.file <- system.file(
# "extdata/cts/withNovel.forJunctionSeq.gff.gz",
# package="JctSeqData");
# countFiles <- system.file(paste0("extdata/cts/",
# decoder$sample.ID,
# "/QC.spliceJunctionAndExonCounts.withNovel.forJunctionSeq.txt.gz"),
# package="JctSeqData");
# ########################################
# #Advanced Analysis:
#
# #Make a "design" dataframe:
# design <- data.frame(condition = factor(decoder$group.ID));
# #Read the QoRTs counts.
# jscs = readJunctionSeqCounts(countfiles = countFiles,
# samplenames = decoder$sample.ID,
# design = design,
# flat.gff.file = gff.file
# );
# #Generate the size factors and load them into the JunctionSeqCountSet:
# jscs <- estimateJunctionSeqSizeFactors(jscs);
# #Estimate feature-specific dispersions:
# jscs <- estimateJunctionSeqDispersions(jscs);
# #Fit dispersion function and estimate MAP dispersion:
# jscs <- fitJunctionSeqDispersionFunction(jscs);
# #Test for differential usage:
# jscs <- testForDiffUsage(jscs);
# #Estimate effect sizes and expression estimates:
# jscs <- estimateEffectSizes( jscs);
#
# ## End(Not run)
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