Description Usage Arguments Value Examples
The result function extracts the results from the given analysis object based on the given options and cutoffs. The aberrant function extracts aberrant splicing events based on the given cutoffs.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ## S4 method for signature 'FraserDataSet'
results(
object,
sampleIDs = samples(object),
padjCutoff = 0.05,
zScoreCutoff = NA,
deltaPsiCutoff = 0.3,
minCount = 5,
psiType = c("psi3", "psi5", "theta"),
additionalColumns = NULL,
BPPARAM = bpparam(),
...
)
resultsByGenes(res, geneColumn = "hgncSymbol", method = "BY")
## S4 method for signature 'FraserDataSet'
aberrant(
object,
type = currentType(object),
padjCutoff = 0.05,
deltaPsiCutoff = 0.3,
zScoreCutoff = NA,
minCount = 5,
by = c("none", "sample", "feature"),
aggregate = FALSE,
...
)
|
object |
A |
sampleIDs |
A vector of sample IDs for which results should be retrieved |
padjCutoff |
The FDR cutoff to be applied or NA if not requested. |
zScoreCutoff |
The z-score cutoff to be applied or NA if not requested. |
deltaPsiCutoff |
The cutoff on delta psi or NA if not requested. |
minCount |
The minimum count value of the total coverage of an intron to be considered as significant. result |
psiType |
The psi types for which the results should be retrieved. |
additionalColumns |
Character vector containing the names of additional
columns from mcols(fds) that should appear in the result table
(e.g. ensembl_gene_id). Default is |
BPPARAM |
The BiocParallel parameter. |
... |
Further arguments can be passed to the method. If "zscores", "padjVals" or "dPsi" is given, the values of those arguments are used to define the aberrant events. |
res |
Result as created with |
geneColumn |
The name of the column in |
method |
The p.adjust method that is being used to adjust p values per sample. |
type |
Splicing type (psi5, psi3 or theta) |
by |
By default |
aggregate |
If TRUE the returned object is based on the grouped features |
For results
: GRanges object containing significant results.
For aberrant
: Either a of logical values of size
introns/genes x samples if "by" is NA or a vector with the
number of aberrant events per sample or feature depending on
the vaule of "by"
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # get data, fit and compute p-values and z-scores
fds <- createTestFraserDataSet()
# extract results: for this example dataset, z score cutoff of 2 is used to
# get at least one result and show the output
res <- results(fds, padjCutoff=NA, zScoreCutoff=3, deltaPsiCutoff=0.05)
res
# aggregate the results by genes (gene symbols need to be annotated first
# using annotateRanges() function)
resultsByGenes(res)
# get aberrant events per sample: on the example data, nothing is aberrant
# based on the adjusted p-value
aberrant(fds, type="psi5", by="sample")
# get aberrant events per gene (first annotate gene symbols)
fds <- annotateRangesWithTxDb(fds)
aberrant(fds, type="psi5", by="feature", zScoreCutoff=2, padjCutoff=NA,
aggregate=TRUE)
# find aberrant junctions/splice sites
aberrant(fds, type="psi5")
|
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