gs_mds {GeneTonic} | 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 )