runJunctionSeqAnalyses
function, and thus for most purposes users should not need to call this
function directly. It may be useful to advanced users performing non-standard
analyses.
testForDiffUsage( jscs, test.formula0 = formula(~ sample + countbin), test.formula1 = formula(~ sample + countbin + condition : countbin), method.GLM = c(c("advanced","DESeq2-style"), c("simpleML","DEXSeq-v1.8.0-style")), dispColumn="dispersion", nCores=1, keep.hypothesisTest.fit = FALSE, meanCountTestableThreshold = "auto", optimizeFilteringForAlpha = 0.01, method.cooksFilter = TRUE, cooksCutoff, pAdjustMethod = "BH", verbose = TRUE)
JunctionSeqCountSet
. Usually initially created by
readJunctionSeqCounts
. Dispersions and size factors must be
set, usually using functions estimateJunctionSeqSizeFactors
and
estimateJunctionSeqDispersions
.
fData(jscs)
column in which the model dispersion is stored.
TRUE
, save both complete hypothesis test model fits for every gene. This will require a lot of memory, but may be useful for statistical diagnostics. Default: FALSE
.
meanCountTestableThreshold
is set to "auto" then this sets the adjusted-p-value threshold to optimize against.
TRUE
, use the cook's filter to detect and remove outliers.
p.adjust
function.
data(exampleDataSet,package="JctSeqData"); jscs <- testForDiffUsage(jscs); ## Not run: # ######################################## # #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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