rmtlr_test | 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).
# 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
)
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