Description Usage Arguments Value Examples
Show results of a Slalom model
1 2 |
object |
an object of class |
n_active |
number of terms (factors) to be plotted (default is 20) |
mad_filter |
numeric(1), filter factors by this mean absolute deviation to exclude outliers. For large datasets this can be set to 0 |
annotated |
logical(1), should annotated factors be plotted? Default is
|
unannotated_dense |
logical(1), should dense unannotated factors be
plotted? Default is |
unannotated_sparse |
logical(1), should sparse unannotated factors be
plotted? Default is |
data.frame with factors ordered by relevance, showing term
(term names), relevance
, type
(factor type: known, annotated
or unannotated), n_prior
(number of genes annotated to the gene
set/factor), n_gain
(number of genes added/switched on for the
factor), n_loss
(number of genes turned off for the factor).
1 2 3 4 5 6 7 | gmtfile <- system.file("extdata", "reactome_subset.gmt", package = "slalom")
genesets <- GSEABase::getGmt(gmtfile)
data("mesc")
model <- newSlalomModel(mesc, genesets, n_hidden = 5, min_genes = 10)
model <- initSlalom(model)
model <- trainSlalom(model, nIterations = 10)
topTerms(model)
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