retrieve_easier_score {easier} | R Documentation |
Calculates easier score and if applicable, both weighted average and penalized score based on the combination of easier score and TMB.
retrieve_easier_score( predictions_immune_response = NULL, TMB_values, easier_with_TMB = c("weighted_average", "penalized_score"), weight_penalty, verbose = TRUE )
predictions_immune_response |
list containing the predictions
for each quantitative descriptor and for each task. This is the
output from |
TMB_values |
numeric vector containing patients' tumor mutational burden (TMB) values. |
easier_with_TMB |
character string indicating which approach should be used to integrate easier with TMB: "weighted_average" (default) and "penalized_score". |
weight_penalty |
integer value from 0 to 1, which is used to define the weight or penalty for combining easier and TMB scores based on a weighted average or penalized score, in order to derive a score of patient's likelihood of immune response. The default value is 0.5. |
verbose |
logical flag indicating whether to display messages about the process. |
A data.frame with samples in rows and easier scores in columns.
# using a SummarizedExperiment object library(SummarizedExperiment) # Using example exemplary dataset (Mariathasan et al., Nature, 2018) # from easierData. Original processed data is available from # IMvigor210CoreBiologies package. library("easierData") dataset_mariathasan <- easierData::get_Mariathasan2018_PDL1_treatment() RNA_tpm <- assays(dataset_mariathasan)[["tpm"]] cancer_type <- metadata(dataset_mariathasan)[["cancertype"]] # Select a subset of patients to reduce vignette building time. pat_subset <- c( "SAM76a431ba6ce1", "SAMd3bd67996035", "SAMd3601288319e", "SAMba1a34b5a060", "SAM18a4dabbc557" ) RNA_tpm <- RNA_tpm[, colnames(RNA_tpm) %in% pat_subset] # Computation of TF activity (Garcia-Alonso et al., Genome Res, 2019) tf_activities <- compute_TF_activity( RNA_tpm = RNA_tpm ) # Predict patients' immune response predictions <- predict_immune_response( tfs = tf_activities, cancer_type = cancer_type, verbose = TRUE ) # retrieve clinical response patient_ICBresponse <- colData(dataset_mariathasan)[["BOR"]] names(patient_ICBresponse) <- colData(dataset_mariathasan)[["pat_id"]] # retrieve TMB TMB <- colData(dataset_mariathasan)[["TMB"]] names(TMB) <- colData(dataset_mariathasan)[["pat_id"]] patient_ICBresponse <- patient_ICBresponse[names(patient_ICBresponse) %in% pat_subset] TMB <- TMB[names(TMB) %in% pat_subset] easier_derived_scores <- retrieve_easier_score(predictions_immune_response = predictions, TMB_values = TMB, easier_with_TMB = c("weighted_average", "penalized_score"), weight_penalty = 0.5) ## Not run: RNA_counts <- assays(dataset_mariathasan)[["counts"]] RNA_counts <- RNA_counts[, colnames(RNA_counts) %in% pat_subset] # Computation of cell fractions (Finotello et al., Genome Med, 2019) cell_fractions <- compute_cell_fractions(RNA_tpm = RNA_tpm) # Computation of pathway scores (Holland et al., BBAGRM, 2019; # Schubert et al., Nat Commun, 2018) pathway_activities <- compute_pathway_activity( RNA_counts = RNA_counts, remove_sig_genes_immune_response = TRUE ) # Computation of ligand-receptor pair weights lrpair_weights <- compute_LR_pairs( RNA_tpm = RNA_tpm, cancer_type = "pancan" ) # Computation of cell-cell interaction scores ccpair_scores <- compute_CC_pairs( lrpairs = lrpair_weights, cancer_type = "pancan" ) # Predict patients' immune response predictions <- predict_immune_response( pathways = pathway_activities, immunecells = cell_fractions, tfs = tf_activities, lrpairs = lrpair_weights, ccpairs = ccpair_scores, cancer_type = cancer_type, verbose = TRUE ) easier_derived_scores <- retrieve_easier_score(predictions_immune_response = predictions, TMB_values = TMB, easier_with_TMB = c("weighted_average", "penalized_score"), weight_penalty = 0.5) ## End(Not run)