topn_list: Top-N Results for Simulated Data ('sim_FS')

In montilab/CaDrA: Candidate Driver Analysis

Top-N Results for Simulated Data (sim_FS)

Description

A list of objects returned from candidate_search using simulated dataset FS = sim_FS, input_score = sim_Scores, top_N = 7, method = "ks_pval", alternative = "less", search_method = "both", max_size = 10, and best_score_only = FALSE.

Usage

data(topn_list)

Format

A list of objects returned from candidate_search including a set of best meta-feature matrix, its corresponding best score, its observed input scores, rank of the best features based on their scores, marginal best scores, and cumulative best scores. pertaining to each top N feature searches.

See candidate_search for more information.

Details

NOTE: max_size is set to 10 as we would like to account for the presence of 10 left-skewed (i.e. true positive or TP) features in sim_FS dataset.

Value

a list of objects returned from candidate_search function

References

Kartha VK, Kern JG, Sebastiani P, Zhang L, Varelas X, Monti S (2019) CaDrA: A computational framework for performing candidate driver analyses using binary genomic features. (Frontiers in Genetics)

Examples

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

# Fetch the first meta-feature
topn_list[[1]]$feature_set

# Fetch the second meta-feature
topn_list[[2]]$feature_set

# Retrieve the meta-feature with the best score among top_N = 7 runs
topn_best_meta <- topn_best(topn_list = topn_list)

# Visualize the best meta-feature using meta_plot function
meta_plot(topn_best_list = topn_best_meta)

# Visualize overlap of meta-features across top_N = 7 
# using topn_plot function
topn_plot(topn_list = topn_list)

montilab/CaDrA documentation built on Aug. 22, 2024, 11:55 p.m.