# 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

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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## Last month downloads

## 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 |

imports | Ckmeans.1d.dp (>= 4.2.2) , graphics , grDevices , igraph (>= 1.2.4) , magrittr (>= 1.5) , rje (>= 1.9) , usefun (>= 0.4.1) , utils , visNetwork (>= 2.0.7) |

suggests | testthat |

Contributors |

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