## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup1, message=FALSE, warning=FALSE, eval=FALSE-------------------------
# install.packages('DrDimont')
## ----setup2, message=FALSE----------------------------------------------------
library(DrDimont)
## ---- echo=TRUE, warning=FALSE, eval=FALSE------------------------------------
# install_python_dependencies(package_manager="pip")
## ---- echo=TRUE, warning=FALSE, eval=FALSE------------------------------------
# install_python_dependencies(package_manager="conda")
## ----Load data----------------------------------------------------------------
data("mrna_data")
data("protein_data")
data("phosphosite_data")
data("metabolite_data")
data("metabolite_protein_interactions")
data("drug_gene_interactions")
## -----------------------------------------------------------------------------
# Data inspection
mrna_data$groupA[1:3, 1:5]
protein_data$groupA[1:3, 1:5]
phosphosite_data$groupA[1:3, 1:5]
metabolite_data$groupA[1:3, 1:5]
## ----Create layers------------------------------------------------------------
# Create individual layers
mrna_layer <- make_layer(name="mrna",
data_groupA=mrna_data$groupA[,-1],
data_groupB=mrna_data$groupB[,-1],
identifiers_groupA=data.frame(gene_name=mrna_data$groupA$gene_name),
identifiers_groupB=data.frame(gene_name=mrna_data$groupB$gene_name))
protein_layer <- make_layer(name="protein",
data_groupA=protein_data$groupA[, c(-1,-2)],
data_groupB=protein_data$groupB[, c(-1,-2)],
identifiers_groupA=data.frame(gene_name=protein_data$groupA$gene_name,
ref_seq=protein_data$groupA$ref_seq),
identifiers_groupB=data.frame(gene_name=protein_data$groupB$gene_name,
ref_seq=protein_data$groupB$ref_seq))
phosphosite_layer <- make_layer(name="phosphosite",
data_groupA=phosphosite_data$groupA[, c(-1,-2, -3)],
data_groupB=phosphosite_data$groupB[, c(-1,-2, -3)],
identifiers_groupA=data.frame(phosphosite_data$groupA[, 1:3]),
identifiers_groupB=data.frame(phosphosite_data$groupB[, 1:3]))
metabolite_layer <- make_layer(name="metabolite",
data_groupA=metabolite_data$groupA[, c(-1,-2, -3)],
data_groupB=metabolite_data$groupB[, c(-1,-2, -3)],
identifiers_groupA=data.frame(metabolite_data$groupA[, 1:3]),
identifiers_groupB=data.frame(metabolite_data$groupB[, 1:3]))
## ----Make layers list---------------------------------------------------------
all_layers <- list(mrna_layer, protein_layer, phosphosite_layer, metabolite_layer)
## ----eval=FALSE---------------------------------------------------------------
# # (i) make inter-layer connection
# make_connection(from='mrna', to='protein', connect_on='gene_name', weight=1, group="both")
## -----------------------------------------------------------------------------
# Data inspection
metabolite_protein_interactions[1:3, ]
## ----eval=FALSE---------------------------------------------------------------
# # (ii) make inter-layer connection
# make_connection(from='protein', to='metabolite',
# connect_on=metabolite_protein_interactions,
# weight='combined_score', group="both")
## ----Inter-layer connections--------------------------------------------------
all_inter_layer_connections = list(
make_connection(from='mrna', to='protein', connect_on='gene_name', weight=1, group="both"),
make_connection(from='protein', to='phosphosite', connect_on='gene_name', weight=1, group="both"),
make_connection(from='protein', to='metabolite',
connect_on=metabolite_protein_interactions, weight='combined_score', group="both")
)
## -----------------------------------------------------------------------------
# Data inspection
drug_gene_interactions[1:3, ]
## ----Make drug-target interaction---------------------------------------------
all_drug_target_interactions <- make_drug_target(
target_molecules='protein',
interaction_table=drug_gene_interactions,
match_on='gene_name')
## -----------------------------------------------------------------------------
return_errors(check_input(layers=all_layers,
inter_layer_connections=all_inter_layer_connections,
drug_target_interactions=all_drug_target_interactions))
## ----Settings-----------------------------------------------------------------
example_settings <- drdimont_settings(
handling_missing_data = list(
default = "pairwise.complete.obs",
mrna = "all.obs"),
reduction_method = "pickHardThreshold",
r_squared=list(default=0.65, metabolite=0.1),
cut_vector=list(default=seq(0.2, 0.65, 0.01)),
conda=FALSE,
save_data = FALSE,
saving_path = tempdir())
# disable multi-threading for example run;
# not recommended for actual data processing
WGCNA::disableWGCNAThreads()
## ----Run pipeline, eval=FALSE-------------------------------------------------
# run_pipeline(layers=all_layers,
# inter_layer_connections=all_inter_layer_connections,
# drug_target_interactions=all_drug_target_interactions,
# settings=example_settings)
## ----Correlation matrices, message=FALSE, results='hide'----------------------
reduced_mrna_layer <- make_layer(name="mrna",
data_groupA=t(mrna_data$groupA[1:10,2:11]),
data_groupB=t(mrna_data$groupB[1:10,2:11]),
identifiers_groupA=data.frame(gene_name=mrna_data$groupA$gene_name[1:10]),
identifiers_groupB=data.frame(gene_name=mrna_data$groupB$gene_name[1:10]))
example_correlation_matrices <- compute_correlation_matrices(
layers=list(reduced_mrna_layer),
settings=example_settings)
## -----------------------------------------------------------------------------
# Data inspection
data("correlation_matrices_example")
correlation_matrices_example$annotations$groupA$protein[1:3, ]
## ----Individual graphs, message=FALSE, results='hide'-------------------------
data("correlation_matrices_example")
example_individual_graphs <- generate_individual_graphs(
correlation_matrices=correlation_matrices_example,
layers=all_layers,
settings=example_settings)
## ----Combine graphs, message=FALSE, results='hide'----------------------------
example_combined_graphs <- generate_combined_graphs(
graphs=example_individual_graphs[["graphs"]],
annotations=example_individual_graphs[["annotations"]],
inter_layer_connections=all_inter_layer_connections,
settings=example_settings)
## -----------------------------------------------------------------------------
# Data inspection
example_combined_graphs$annotations$both[1:3, ]
## ----Drug targets and their edges, message=FALSE, results='hide'--------------
example_drug_target_edges <- determine_drug_targets(
graphs=example_combined_graphs[["graphs"]],
annotations=example_combined_graphs[["annotations"]],
drug_target_interactions=all_drug_target_interactions,
settings=example_settings)
## ----Calculate interaction score, eval=FALSE, message=FALSE, results='hide'----
# example_interaction_score_graphs <- generate_interaction_score_graphs(
# graphs=example_combined_graphs[["graphs"]],
# drug_target_edgelists=example_drug_target_edges[["edgelists"]],
# settings=example_settings)
## ----Calculate differential score, message=FALSE, results='hide'--------------
data("interaction_score_graphs_example")
example_differential_graph <- generate_differential_score_graph(
interaction_score_graphs=interaction_score_graphs_example,
settings=example_settings)
# if interaction score graphs have been computed use the following:
#example_differential_score_graph <- generate_differential_score_graph(
# interaction_score_graphs=example_interaction_score_graphs,
# settings=example_settings)
## ----Drug response, message=FALSE, results='hide'-----------------------------
example_drug_response_scores <- compute_drug_response_scores(
differential_graph=example_differential_graph,
drug_targets=example_drug_target_edges[["targets"]],
settings=example_settings)
## ----Result Output------------------------------------------------------------
head(dplyr::filter(example_drug_response_scores, !is.na(drug_response_score)))