"gosummaries"(x, p.value = 0.05, lfc = 1, adjust.method = "fdr", exp = NULL, annotation = NULL, components = 1:ncol(x), show_genes = FALSE, gconvert_target = "NAME", n_genes = 30, organism = "hsapiens", ...)
MArrayLM
data.frame
describing the samples, its row names
should match with column names of exp
(Optional)gconvert
, if NULL
original IDs are shown.gosummaries.default
It is possible to show the gene names instead of GO annotations in the
wordclouds. The word sizes in wordclouds are defined by the limma p-values.
As the gene identifiers in expression matrices are usually rather
unintelligible then they are automatically converted into gene names using
gconvert
function. It is possible to show also the original
identifiers by setting gconvert_target
to NULL. This can be useful if
the values do not correspond to genes, but for example metabolites.
## Not run:
# data(tissue_example)
#
# # Do the t-test comparisons
# mm = model.matrix(~ factor(tissue_example$annot$Tissue) - 1)
# colnames(mm) = make.names(levels(factor(tissue_example$annot$Tissue)))
#
# contrast = limma::makeContrasts(brain - cell.line,
# hematopoietic.system - muscle,
# cell.line - hematopoietic.system,
# levels = colnames(mm))
#
# fit = limma::lmFit(tissue_example$exp, mm)
# fit = limma::contrasts.fit(fit, contrast)
# fit = limma::eBayes(fit)
#
# gs_limma = gosummaries(fit)
# gs_limma_exp = gosummaries(fit, exp = tissue_example$exp,
# annotation = tissue_example$annot)
#
# plot(gs_limma, fontsize = 8)
# plot(gs_limma, panel_height = 0, fontsize = 8)
# plot(gs_limma_exp, classes = "Tissue", fontsize = 8)
# ## End(Not run)
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