Rank Multienrichment clusters
rank_mem_clusters(
mem,
clusters,
choose = NULL,
per_cluster = Inf,
byCols = c("composite_rank", "minp_rank", "gene_count_rank"),
verbose = FALSE,
...
)list object output from multiEnrichMap()
list containing set names, that must match
colnames(mem$memIM) and rownames(mem$enrichIM).
optional vector indicating which clusters to
return. If an integer vector, it refers to the elements
in the input clusters. If a character vector, it must
contain values in names(clusters). When choose is NULL,
all clusters are returned.
integer vector with the number of entries
to return for each cluster. Values will be recycled to the
length of the clusters to be returned, defined by choose
or by length(clusters) when choose is NULL.
character vector used to sort the resulting
data.frame within each cluster. This argument is passed
directly to jamba::mixedSortDF().
logical indicating whether to print verbose output.
additional arguments are ignored.
data.frame sorted by the criteria defined by byCols,
with colname "set" to indicate the pathway/set name.
This function takes list output from multiEnrichMap(), and
a list of clusters, and returns a data.frame that contains
several rank order metrics. It is intended to be used with
column clusters following mem_gene_path_heatmap(),
see examples.
The argument per_cluster is intended to make it convenient
to pick the top exemplar pathways, especially when argument
byCols is defined so that it sorts by the rank columns.
The argument choose is intended to make it easy to retrieve
pathways from specific clusters.
Other jam utility functions:
avg_angles(),
avg_colors_by_list(),
call_fn_ellipsis_deprecated(),
cell_fun_bivariate(),
collapse_mem_clusters(),
colorRamp2D(),
deconcat_df2(),
display_colorRamp2D(),
enrichList2geneHitList(),
filter_mem_genes(),
filter_mem_sets(),
find_colname(),
get_hull_data(),
get_igraph_layout(),
gsubs_remove(),
handle_igraph_param_list(),
isColorBlank(),
make_legend_bivariate(),
make_point_hull(),
mem_find_overlap(),
order_colors(),
rotate_coordinates(),
subgraph_jam(),
subset_mem(),
summarize_node_spacing(),
xyAngle()
## Start with mem
# mem <- multiEnrichMap(...);
# gp_hm <- mem_gene_path_heatmap(mem, column_split=4);
## Retrieve clusters from the Heatmap output, there should be 4 clusters
# clusters <- heatmap_column_order(gp_hm)
# clusters_df <- rank_mem_clusters(mem, clusters)