View source: R/preprocess_COSMOS_signaling_to_metabolism.R
preprocess_COSMOS_signaling_to_metabolism | R Documentation |
Runs checks on the input data and simplifies the prior knowledge network. Simplification includes the removal of (1) nodes that are not reachable from signaling nodes and (2) interactions between transcription factors and target genes if the target gene does not respond or the response is contradictory with the change in the transcription factor activity. Optionally, further TF activities are estimated via network optimization via CARNIVAL and the interactions between TF and genes are filtered again.
preprocess_COSMOS_signaling_to_metabolism(
meta_network = meta_network,
tf_regulon = load_tf_regulon_dorothea(),
signaling_data,
metabolic_data,
diff_expression_data = NULL,
diff_exp_threshold = 1,
maximum_network_depth = 8,
expressed_genes = NULL,
remove_unexpressed_nodes = TRUE,
filter_tf_gene_interaction_by_optimization = TRUE,
CARNIVAL_options = default_CARNIVAL_options("lpSolve")
)
meta_network |
prior knowledge network (PKN). A PKN released with COSMOS
and derived from Omnipath, STITCHdb and Recon3D can be used. See details on
the data |
tf_regulon |
collection of transcription factor - target interactions.
A default collection from dorothea can be obtained by the
|
signaling_data |
numerical vector, where names are signaling nodes
in the PKN and values are from {1, 0, -1}. Continuous data will be
discretized using the |
metabolic_data |
numerical vector, where names are metabolic nodes in the PKN and values are continuous values that represents log2 fold change or t-values from a differential analysis. These values are compared to the simulation results (simulated nodes can take value -1, 0 or 1) |
diff_expression_data |
(optional) numerical vector that represents the
results of a differential gene expression analysis. Names are gene
names using gene symbole and values are log fold change or
t-values. We use the “ |
diff_exp_threshold |
threshold parameter (default 1) used to binarize
the values of “ |
maximum_network_depth |
integer > 0 (default: 8). Nodes that are further
than “ |
expressed_genes |
character vector. Names of nodes that are expressed. By
default we consider all the nodes that appear in |
remove_unexpressed_nodes |
if TRUE (default) removes nodes from the PKN
that are not expressed, see input “ |
filter_tf_gene_interaction_by_optimization |
(default:TRUE), if TRUE then runs a network optimization that estimates TF activity not included in the inputs and checks the consistency between the estimated activity and change in gene expression. Removes interactions where TF and gene expression are inconsistent |
CARNIVAL_options |
list that controls the options of CARNIVAL. See details
in |
cosmos_data object with the following fields:
meta_network
Filtered PKN
tf_regulon
TF - target regulatory network
signaling_data_bin
Binarised signaling data
metabolic_data
Metabolomics data
diff_expression_data_bin
Binarized gene expression data
optimized_network
Initial optimized network if
filter_tf_gene_interaction_by_optimization is TRUE
meta_network
for meta PKN,
load_tf_regulon_dorothea
for tf regulon,
runCARNIVAL
.
data(toy_network)
data(toy_signaling_input)
data(toy_metabolic_input)
data(toy_RNA)
test_for <- preprocess_COSMOS_signaling_to_metabolism(meta_network = toy_network,
signaling_data = toy_signaling_input,
metabolic_data = toy_metabolic_input,
diff_expression_data = toy_RNA,
maximum_network_depth = 15,
remove_unexpressed_nodes = TRUE,
CARNIVAL_options = default_CARNIVAL_options("lpSolve"))
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