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molnet
View on CRAN: Click
here
Download and install molnet package within the R console
Install from CRAN:
install.packages("molnet")
Install from Github:
library("remotes")
install_github("cran/molnet")
Install by package version:
library("remotes")
install_version("molnet", "0.1.0")
Attach the package and use:
library("molnet")
Maintained by
Katharina Baum
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
First Published: 2021-08-06
Latest Update: 2021-08-06
Description:
Networks provide a means to incorporate molecular interactions into
reasoning, but on the omics-level, they are currently mainly used to combine
genomic and proteomic information. We here present a novel network analysis
pipeline that enables integrative analysis of multi-omics data including
metabolomics. It allows for comparative conclusions between two different
conditions, such as tumor subgroups, healthy vs. disease, or generally control
vs. perturbed.
Our approach focuses on interactions and their strength instead of on node
properties and includes molecules with low abundance and unknown function. We
use correlation-induced networks that are reduced and combined to form
heterogeneous, multi-omics molecular networks. Prior information such as
metabolite-protein interactions are incorporated. A semi-local, path-based
integration step denoises the network and ensures integrative conclusions. As
case studies, we investigate differential drug response in breast cancer tumor
datasets providing proteomics, transcriptomics, phospho-proteomics and
metabolomics data and contrasting patients with different estrogen receptor
status.
Our proposed pipeline leverages multi-omics data for differential predictions,
e.g. on drug response, and includes prior information on interactions.
The case study presented in the vignette uses data published by
Krug (2020) . The package license applies only
to the software and explicitly not to the included data.
How to cite:
Katharina Baum (2021). molnet: Predicting Differential Drug Response using Multi-Omics Networks. R package version 0.1.0, https://cran.r-project.org/web/packages/molnet. Accessed 28 Mar. 2025.
Previous versions and publish date:
0.1.0 (2021-08-06 10:30)
Other packages that cited molnet R package
View molnet citation profile
Other R packages that molnet depends,
imports, suggests or enhances
Functions, R codes and Examples using
the molnet R package
Some associated functions: calculate_interaction_score . check_connection . check_drug_target . check_drug_targets_in_layers . check_input . check_layer . check_sensible_connections . chunk . chunk_2gether . combine_graphs . combined_graphs_example . corPvalueStudentParallel . create_unique_layer_node_ids . determine_drug_targets . differential_score . differential_score_graph_example . drug_gene_interactions . drug_response_score_example . drug_target_interaction_example . drug_targets_example . find_targets . generate_combined_graphs . generate_individual_graphs . generate_reduced_graph . get_drug_response_score . get_layer . get_layer_setting . graph_metrics . individual_graphs_example . install_python_dependencies . inter_layer_edgelist_by_id . inter_layer_edgelist_by_table . interaction_score . interaction_score_graphs_example . interaction_score_graphs_vignette . layers_example . load_interaction_score_output . make_connection . make_drug_target . make_layer . metabolite_data . metabolite_protein_interaction . molnet_settings . mrna_data . network_reduction_by_p_value . network_reduction_by_pickHardThreshold . phosphoprotein_data . pickHardThreshold_alternative . pipe . protein_data . return_errors . sample_size . scaleFreeFitIndex_alternative . set_cluster . shutdown_cluster . start_pipeline . target_edge_list . write_interaction_score_input .
Some associated R codes: alternative_pickHardThreshold.R . checks.R . data_documentations.R . drug_targets.R . graph_combination.R . interaction_score.R . network_generation.R . network_reduction.R . pipeline_functions.R . settings.R . utilities.R . utils-pipe.R . Full molnet package functions and examples
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