meta_plot: Candidate Drivers Search Plot

In RC-88/CaDrA: Candidate Driver Analysis

Candidate Drivers Search Plot

Description

By utilizing the top N results obtained from candidate_search(), we can find the best meta-feature among the top N searches using topn_best(). meta_plot() is then used to produce graphics including a tile plot for the top meta-features that associated with a molecular phenotype of interest (e.g. input_score), the KS enrichment plot of the meta-features, and lastly, a density diagram of the distribution of the observed input scores sorted from largest to smallest at the top.

Usage

meta_plot(topn_best_list, input_score_label = NULL)

Arguments

topn_best_list	a list of objects returned from candidate_search() corresponding to the search of a set of top N features given by top_N value. The topn_best_list contains a list of returned meta-feature set, its corresponding best score, and observed input scores.
input_score_label	a label that references to the input_score variable that was used to compute the top N best features. Default is NULL.

Value

a density diagram of observed input scores (sorted from largest to lowest), a tile plot of the top features within the meta-feature set, and a KS enrichment plot of the meta-feature set for a given distribution (here, this will correspond to the logical OR of the features)

Examples

# Load pre-computed Top-N list generated for sim_FS dataset
data(topn_list)

# With the results obtained from top-N evaluation,
# We can find the combination of features that gives the best score in
# top N searches
topn_best_meta <- topn_best(topn_list = topn_list)

# Now we can plot this set of best meta-feature
meta_plot(topn_best_list = topn_best_meta)

RC-88/CaDrA documentation built on March 28, 2023, 12:18 a.m.