rmtlr_test {easier} | R Documentation |
Computes the predictions as a matrix multiplication using both the features input data and the features estimated weights.
rmtlr_test(x_test, coef_matrix)
x_test |
numeric matrix containing features values (rows = samples; columns = features). |
coef_matrix |
numeric matrix containing the parameters values derived from model training (rows = features; columns = tasks). |
Numeric matrix of predicted values (rows = samples; columns = tasks).
## Not run: # 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"]] # 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 ) # Parameters values should be defined as a matrix # with features as rows and tasks as columns estimated_parameters <- matrix(rnorm(n = (ncol(tf_activities) + 1) * 10), nrow = ncol(tf_activities) + 1, ncol = 10 ) rownames(estimated_parameters) <- c("(Intercept)", colnames(tf_activities)) colnames(estimated_parameters) <- c( "CYT", "Ock_IS", "Roh_IS", "chemokines", "Davoli_IS", "IFNy", "Ayers_expIS", "Tcell_inflamed", "RIR", "TLS" ) # Compute predictions using parameters values pred_test <- rmtlr_test( x_test = tf_activities, coef_matrix = estimated_parameters ) ## End(Not run)