View source: R/application_prediction.R
convert_single_cell_expression_to_settings | R Documentation |
convert_single_cell_expression_to_settings
Prepare single-cell expression data to perform ligand activity analysis
convert_single_cell_expression_to_settings(cell_id, expression_matrix, setting_name, setting_from, regression = FALSE)
cell_id |
Identity of the cell of interest |
expression_matrix |
Gene expression matrix of single-cells |
setting_name |
Name of the dataset |
setting_from |
Character vector giving the gene symbols of the potentially active ligands you want to define ligand activities for. |
regression |
Perform regression-based ligand activity analysis (TRUE) or classification-based ligand activity analysis (FALSE) by considering the genes expressed higher than the 0.975 quantiles as genes of interest. Default: FALSE. |
A list with slots $name, $from and $response respectively containing the setting name, potentially active ligands and the response to predict (whether genes belong to gene set of interest; i.e. most strongly expressed genes in a cell)
## Not run:
weighted_networks = construct_weighted_networks(lr_network, sig_network, gr_network,source_weights_df)
ligands = list("TNF","BMP2","IL4")
ligand_target_matrix = construct_ligand_target_matrix(weighted_networks, ligands, ltf_cutoff = 0, algorithm = "PPR", damping_factor = 0.5, secondary_targets = FALSE)
potential_ligands = c("TNF","BMP2","IL4")
genes = c("SOCS2","SOCS3","IRF1","ICAM1","ID1","ID2","ID3")
cell_ids = c("cell1","cell2")
expression_scaled = matrix(rnorm(length(genes)*2, sd = 0.5, mean = 0.5), nrow = 2)
rownames(expression_scaled) = cell_ids
colnames(expression_scaled) = genes
settings = convert_single_cell_expression_to_settings(cell_id = cell_ids[1], expression_matrix = expression_scaled, setting_name = "test", setting_from = potential_ligands)
## End(Not run)
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