results,FraserDataSet-method {FRASER} | R Documentation |
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.
## 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"
# 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")