gs_mds | R Documentation |
Multi Dimensional Scaling plot for gene sets, extracted from a res_enrich
object
gs_mds(
res_enrich,
res_de,
annotation_obj,
gtl = NULL,
n_gs = nrow(res_enrich),
gs_ids = NULL,
similarity_measure = "kappa_matrix",
mds_k = 2,
mds_labels = 0,
mds_colorby = "z_score",
gs_labels = NULL,
plot_title = NULL,
return_data = FALSE
)
res_enrich |
A |
res_de |
A |
annotation_obj |
A |
gtl |
A |
n_gs |
Integer value, corresponding to the maximal number of gene sets to
be included (from the top ranked ones). Defaults to the number of rows of
|
gs_ids |
Character vector, containing a subset of |
similarity_measure |
Character, currently defaults to |
mds_k |
Integer value, number of dimensions to compute in the multi dimensional scaling procedure |
mds_labels |
Integer, defines the number of labels to be plotted on top of the scatter plot for the provided gene sets. |
mds_colorby |
Character specifying the column of |
gs_labels |
Character vector, containing a subset of |
plot_title |
Character string, used as title for the plot. If left |
return_data |
Logical, whether the function should just return the
data.frame of the MDS coordinates, related to the original |
A ggplot
object
create_kappa_matrix()
is used to calculate the similarity between
gene sets
library("macrophage")
library("DESeq2")
library("org.Hs.eg.db")
library("AnnotationDbi")
# dds object
data("gse", package = "macrophage")
dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)
# annotation object
anno_df <- data.frame(
gene_id = rownames(dds_macrophage),
gene_name = mapIds(org.Hs.eg.db,
keys = rownames(dds_macrophage),
column = "SYMBOL",
keytype = "ENSEMBL"
),
stringsAsFactors = FALSE,
row.names = rownames(dds_macrophage)
)
# res object
data(res_de_macrophage, package = "GeneTonic")
res_de <- res_macrophage_IFNg_vs_naive
# res_enrich object
data(res_enrich_macrophage, package = "GeneTonic")
res_enrich <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive)
res_enrich <- get_aggrscores(res_enrich, res_de, anno_df)
gs_mds(res_enrich,
res_de,
anno_df,
n_gs = 200,
mds_labels = 10
)
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