rmtlr_test {easier}R Documentation

Regularized Multi-Task Linear Regression (RMTLR) model predictions

Description

Computes the predictions as a matrix multiplication using both the features input data and the features estimated weights.

Usage

rmtlr_test(x_test, coef_matrix)

Arguments

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).

Value

Numeric matrix of predicted values (rows = samples; columns = tasks).

Examples

## 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)

[Package easier version 0.99.12 Index]