emba v0.1.1

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Ensemble Boolean Model Biomarker Analysis

Analysis and visualization of an ensemble of boolean models for biomarker discovery in cancer cell networks. The package allows to easily import the data results of a software pipeline that predicts synergistic drug combinations in cancer cell lines, developed by the DrugLogics research group in NTNU. It has generic functions that can be used to split a boolean model dataset to model groups with regards to the models predictive performance (number of true positive predictions or Matthews correlation coefficient score) or synergy prediction based on a given set of observed synergies and find the average activity difference per network node between all group pairs. Thus, given user-specific thresholds, important nodes (biomarkers) can be accessed in the sense that they make the models predict specific synergies (synergy biomarkers) or have better performance in general (performance biomarkers). Lastly, if the boolean models have a specific equation form and differ only in their link operator, link operator biomarkers can also be assessed.

Readme

emba

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Analysis and visualization of an ensemble of boolean models for biomarker discovery in cancer cell networks. The package allows to easily import the data results of a software pipeline that predicts synergistic drug combinations in cancer cell lines, developed by the DrugLogics research group in NTNU. It has generic functions that can be used to split a boolean model dataset to model groups with regards to the models predictive performance (number of true positive predictions or Matthews correlation coefficient score) or synergy prediction based on a given set of observed synergies and find the average activity difference per network node between all group pairs. Thus, given user-specific thresholds, important nodes (biomarkers) can be accessed in the sense that they make the models predict specific synergies (synergy biomarkers) or have better performance in general (performance biomarkers). Lastly, if the boolean models have a specific equation form and differ only in their link operator, link operator biomarkers can also be assessed.

For an example usage of this package's functions, see the analysis performed on multiple boolean model datasets here.

Install

devtools::install_github("bblodfon/emba")

Functions in emba

Name Description
count_models_that_predict_synergies Count models that predict a set of synergies
get_node_names Get the node names
get_avg_link_operator_diff_mat_based_on_tp_predictions Get average link operator difference matrix based on the number of true positives
get_synergy_comparison_sets Get synergy comparison sets
get_avg_activity_diff_based_on_specific_synergy_prediction Get average activity difference based on specific synergy prediction
get_synergy_subset_stats Find the number of predictive models for every synergy subset
calculate_models_synergies_fn Count the non-synergies of the observed synergies per model (FN)
get_observed_model_predictions Subset the model predictions to the (true) observed synergies
get_biomarkers Get total biomarkers from average data differences matrix
get_avg_activity_diff_based_on_synergy_set_cmp Get the average activity difference based on the comparison of two synergy sets
get_avg_activity_diff_mat_based_on_specific_synergy_prediction Get average activity difference matrix based on specific synergy prediction
construct_network Construct igraph network graph
calculate_models_synergies_tp Count the predictions of the observed synergies per model (TP)
validate_observed_synergies_data Validate observed synergies data
get_avg_activity_diff_based_on_tp_predictions Get the average activity difference based on the number of true positives
get_edges_from_topology_file Get the edges from a specified topology
get_perf_biomarkers_per_cell_line Get performance biomarkers per cell line
get_stable_state_from_models_dir Load the models stable state data
get_biomarkers_per_type Get biomarkers from average data differences matrix (per type)
get_avg_activity_diff_mat_based_on_mcc_clustering Get average activity difference matrix based on MCC clustering
get_fitness_from_models_dir Load the models fitness scores
make_barplot_on_synergy_subset_stats Bar plot of observed synergy subsets
plot_avg_link_operator_diff_graph Plot the graph of average link operator differences (igraph)
emba emba
get_alt_drugname Get alternative drug combination name
get_avg_activity_diff_mat_based_on_tp_predictions Get average activity difference matrix based on the number of true positives
get_avg_activity_diff_based_on_mcc_clustering Get the average activity difference based on MCC clustering
get_models_based_on_mcc_class_id Get models based on the MCC class id
get_synergy_biomarkers_per_cell_line Get synergy biomarkers per cell line
get_synergy_biomarkers_from_dir Get synergy biomarkers from dir
get_avg_link_operator_diff_mat_based_on_mcc_clustering Get average link operator difference matrix based on MCC clustering
get_avg_link_operator_diff_mat_based_on_specific_synergy_prediction Get average link operator difference matrix based on specific synergy prediction
get_model_names Get the model names
get_model_predictions Load the models predictions data
plot_avg_link_operator_diff_graphs Plot the graphs from an average link operator differences matrix
plot_avg_state_diff_graph Plot the graph of average state differences (igraph)
is_comb_element_of Is drug combination element of given vector?
get_node_colors Get the node colors
update_biomarker_files Update biomarker files for a specific synergy
make_barplot_on_models_stats Bar plot of model stats
print_model_and_drug_stats Print model and drug statistics
get_observed_synergies_per_cell_line Get observed synergies per cell line
get_observed_synergies Load the observed synergies data
get_link_operators_from_models_dir Load the models boolean equation link operator data
get_unobserved_model_predictions Subset the model predictions to the (false) non-observed synergies
plot_avg_state_diff_graph_vis Plot the graph of average state differences (visNetwork)
get_x_axis_values Get the refined x-axis values
plot_mcc_classes_hist Plot histogram of the MCC classes
plot_avg_state_diff_graphs Plot the graphs from an average state differences matrix
print_biomarkers_per_predicted_synergy Print biomarkers for each predicted synergy
calculate_mcc Calculate the Matthews correlation coefficient vector
calculate_models_synergies_fp Count the predictions of the non-synergistic drug combinations per model (FP)
calculate_models_synergies_tn Count the non-synergies of the non-synergistic drug combinations per model (TN)
add_numbers_above_the_bars Add numbers horizontally above the bars of a barplot
calculate_models_mcc Calculate the Matthews correlation coefficient for each model
assign_link_operator_value_to_equation Assign link operator value to boolean equation
biomarker_mcc_analysis Biomarker analysis based on MCC model classification
biomarker_tp_analysis Biomarker analysis based on TP model classification
biomarker_synergy_analysis Biomarker analysis per synergy predicted
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Details

Type Package
License MIT + file LICENSE
URL https://github.com/bblodfon/emba
BugReports https://github.com/bblodfon/emba/issues
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
NeedsCompilation no
Packaged 2019-10-18 14:20:45 UTC; john
Repository CRAN
Date/Publication 2019-10-21 14:30:02 UTC

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