readAnnotationData,
readJunctionSeqCounts,
estimateJunctionSeqSizeFactors, estimateJunctionSeqDispersions,
fitJunctionSeqDispersionFunction, testForDiffUsage, and
estimateEffectSizes.
runJunctionSeqAnalyses(sample.files, sample.names, condition, flat.gff.file, analysis.type = c("junctionsAndExons","junctionsOnly","exonsOnly"), meanCountTestableThreshold = "auto", nCores = 1, use.covars, test.formula0 = formula(~ sample + countbin), test.formula1 = formula(~ sample + countbin + condition : countbin), effect.formula = formula(~ condition + countbin + condition : countbin), geneLevel.formula = formula(~ condition), use.exons, use.junctions, use.known.junctions = TRUE, use.novel.junctions = TRUE, use.multigene.aggregates = FALSE, gene.names, method.GLM = c(c("advanced","DESeq2-style"), c("simpleML","DEXSeq-v1.8.0-style")), method.dispFit = c("parametric", "local", "mean"), method.dispFinal = c("shrink","max","fitted","noShare"), method.sizeFactors = c("byGenes","byCountbins"), method.countVectors = c("geneLevelCounts","sumOfAllBinsForGene", "sumOfAllBinsOfSameTypeForGene"), method.expressionEstimation = c("feature-vs-gene", "feature-vs-otherFeatures"), method.cooksFilter = TRUE, optimizeFilteringForAlpha = 0.01, fitDispersionsForExonsAndJunctionsSeparately = TRUE, keep.hypothesisTest.fit = FALSE, keep.estimation.fit = FALSE, replicateDEXSeqBehavior.useRawBaseMean = FALSE, verbose = TRUE, debug.mode = FALSE)analysis.type parameter.
If TRUE, then exonic region loci will be included in the analyses and will be tested for
differential usage. If this parameter is set, then parameter use.junctions must also be set.
analysis.type parameter.
If TRUE, then splice junction loci will be included in the analyses and will be tested for
differential usage. If this parameter is set, then parameter use.exons must also be set.
TRUE, then known splice junctions will not be filtered out prior to analysis. Note: this is overidden if use.junctions is FALSE or if analysis.type is set to "exonsOnly".
TRUE, then novel splice junctions will not be filtered out prior to analysis. Note: this is overidden if use.junctions is FALSE or if analysis.type is set to "exonsOnly".
Determines the method used to arrive at a "final" dispersion estimate. The default, "shrink" uses the maximum a posteriori estimate, combining information from both the fitted and feature-specific dispersion estimates. This is the method used by DESeq2 and DEXSeq v1.12.0 and above.
Determines the method used to calculate normalization size factors. By default JunctionSeq uses gene-level expression. As an alternative, feature-level counts can be used as they are in DEXSeq. In practice the difference is almost always negligible.
Determines the type of count vectors to be used in the model framework. By default JunctionSeq compares the counts for a specific feature against the counts across the rest of the gene minus the counts for the specific feature. Alternatively, the sum of all other features on the gene can be used, like in DEXSeq. The advantage to the default JunctionSeq behavior is that no read or read-pair is ever counted more than once in any model. Under DEXSeq, some reads may cover many exonic segments and thus be counted repeatedly.
Determines the methodology used to generate feature expression estimates and relative fold changes. By default each feature is modeled separately. Under the default count-vector method, this means that the resultant relative fold changes will be a measure of the relative fold change between the feature and the gene as a whole. Alternatively, the "feature-vs-otherFeatures" method builds a large, complex model containing all features belonging to the gene. The coefficients for each feature are then "balanced" using linear contrasts weighted by the inverse of their variance. In general we have found this method to produce very similar results but less efficiently and less consistently. Additionally, this alternative method "multi-counts" reads that cover more than one feature. This can result in over-weighting of exonic regions with a large number of annotated variations in a small genomic area, as each individual read or read-pair may be counted many times in the model. Under the default option, no read or read-pair is ever counted more than once in a given model.
TRUE, use the cook's filter to detect and remove outliers.
meanCountTestableThreshold is set to "auto" then this sets the adjusted-p-value threshold to optimize against.
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.
TRUE, save the complete model fits for every gene. This will require a lot of memory, but may be useful for statistical diagnostics. Default: FALSE.
TRUE, the
baseMean and baseVar variables will be computed using raw counts rather than normalized counts.
This is used for internal tests in which DEXSeq functionality is replicated precisely and the results are compared against equivalent DEXSeq results.
Without this option the results would differ slightly (generally by less than 1 hundreth of a percent).
USED ONLY FOR INTERNAL TESTING! NOT INTENDED FOR ACTUAL USE!
JunctionSeqCountSet object, containing the complete analysis dataset and results.
## 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$sample.ID,
# "/QC.spliceJunctionAndExonCounts.withNovel.forJunctionSeq.txt.gz"),
# package="JctSeqData");
# ########################################
#
# jscs <- runJunctionSeqAnalyses(sample.files = countFiles,
# sample.names = decoder$sample.ID,
# condition=factor(decoder$group.ID),
# flat.gff.file = gff.file,
# analysis.type = "junctionsAndExons"
# );
#
# ## End(Not run)
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