Builds all prior knowledge data required by NicheNet. For this it calls a multitude of methods to download and combine data from various databases according to the settings. The content of the prior knowledge data is highly customizable, see the documentation of the related functions. After the prior knowledge is ready, it performs parameter optimization to build a NicheNet model. This results a weighted ligand- target matrix. Then, considering the expressed genes from user provided data, a gene set of interest and background genes, it executes the NicheNet ligand activity analysis.
Usage
nichenet_main(
only_omnipath = FALSE,
expressed_genes_transmitter = NULL,
expressed_genes_receiver = NULL,
genes_of_interest = NULL,
background_genes = NULL,
use_weights = TRUE,
n_top_ligands = 42,
n_top_targets = 250,
signaling_network = list(),
lr_network = list(),
gr_network = list(),
small = FALSE,
tiny = FALSE,
make_multi_objective_function_param = list(),
objective_function_param = list(),
mlrmbo_optimization_param = list(),
construct_ligand_target_matrix_param = list(),
results_dir = NULL,
quality_filter_param = list()
)Arguments
- only_omnipath
Logical: use only OmniPath for network knowledge. This is a simple switch for convenience, further options are available by the other arguments. By default we use all available resources. The networks can be customized on a resource by resource basis, as well as providing custom parameters for individual resources, using the parameters `signaling_network`, `lr_network` and `gr_network`.
- expressed_genes_transmitter
Character vector with the gene symbols of the genes expressed in the cells transmitting the signal.
- expressed_genes_receiver
Character vector with the gene symbols of the genes expressed in the cells receiving the signal.
- genes_of_interest
Character vector with the gene symbols of the genes of interest. These are the genes in the receiver cell population that are potentially affected by ligands expressed by interacting cells (e.g. genes differentially expressed upon cell-cell interaction).
- background_genes
Character vector with the gene symbols of the genes to be used as background.
- use_weights
Logical: calculate and use optimized weights for resources (i.e. one resource seems to be better than another, hence the former is considered with a higher weight).
- n_top_ligands
How many of the top ligands to include in the ligand-target table.
- n_top_targets
How many of the top targets (for each of the top ligands) to consider in the ligand-target table.
- signaling_network
A list of parameters for building the signaling network, passed to
nichenet_signaling_network.- lr_network
A list of parameters for building the ligand-receptor network, passed to
nichenet_lr_network.- gr_network
A list of parameters for building the gene regulatory network, passed to
nichenet_gr_network.- small
Logical: build a small network for testing purposes, using only OmniPath data. It is also a high quality network, it is reasonable to try the analysis with this small network.
- tiny
Logical: build an even smaller network for testing purposes. As this involves random subsetting, it's not recommended to use this network for analysis.
- make_multi_objective_function_param
Override parameters for
smoof::makeMultiObjectiveFunction.- objective_function_param
Override additional arguments passed to the objective function.
- mlrmbo_optimization_param
Override arguments for
nichenetr::mlrmbo_optimization.- construct_ligand_target_matrix_param
Override parameters for
nichenetr::construct_ligand_target_matrix.- results_dir
Character: path to the directory to save intermediate and final outputs from NicheNet methods.
- quality_filter_param
Arguments for
filter_intercell_network(quality filtering of the OmniPath ligand-receptor network). It is recommended to check these parameters and apply some quality filtering. The defaults already ensure certain filtering, but you might want more relaxed or stringent options.
Value
A named list with the intermediate and final outputs of the pipeline: `networks`, `expression`, `optimized_parameters`, `weighted_networks` and `ligand_target_matrix`.
Details
About small and tiny networks: Building a NicheNet model
is computationally demanding, taking several hours to run. As this is
related to the enormous size of the networks, to speed up testing we can
use smaller networks, around 1,000 times smaller, with few thousands of
interactions instead of few millions. Random subsetting of the whole
network would result disjunct fragments, instead we load only a few
resources. To run the whole pipeline with tiny networks use
nichenet_test.
Examples
if (FALSE) {
nichenet_results <- nichenet_main(
# altering some network resource parameters, the rest
# of the resources will be loaded according to the defaults
signaling_network = list(
cpdb = NULL, # this resource will be excluded
inbiomap = NULL,
evex = list(min_confidence = 1.0) # override some parameters
),
gr_network = list(only_omnipath = TRUE),
n_top_ligands = 20,
# override the default number of CPU cores to use
mlrmbo_optimization_param = list(ncores = 4)
)
}