predict_with_rmtlr: Predict single-view immune response

In olapuentesantana/easier: Estimate Systems Immune Response from RNA-seq data

Predict single-view immune response

Description

Obtains predictions of immune response for individual quantitative descriptors by using a cancer-specific model learned with Regularized Multi-Task Linear Regression algorithm (RMTLR).

Usage

predict_with_rmtlr(
  view_name,
  view_info,
  view_data,
  opt_model_cancer_view_spec,
  opt_xtrain_stats_cancer_view_spec,
  verbose = TRUE
)

Arguments

view_name	character string containing the name of the input view.
view_info	character string informing about the family of the input data.
view_data	list containing the data for each input view.
opt_model_cancer_view_spec	cancer-view-specific model feature parameters learned during training. These are available from easierData package through easierData::get_opt_models().
opt_xtrain_stats_cancer_view_spec	cancer-view-specific features mean and standard deviation of the training set. These are available from easierData package through easierData::get_opt_xtrain_stats().
verbose	logical flag indicating whether to display messages about the process.

Value

A list of predictions, one for each task, in a matrix format (rows = samples; columns = [runs).

Examples

# 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
)

view_name <- "tfs"
view_info <- c(tfs = "gaussian")
view_data <- list(tfs = as.data.frame(tf_activities))

# Retrieve internal data
opt_models <- suppressMessages(easierData::get_opt_models())
opt_xtrain_stats <- suppressMessages(easierData::get_opt_xtrain_stats())

opt_model_cancer_view_spec <- lapply(view_name, function(X) {
  return(opt_models[[cancer_type]][[X]])
})
names(opt_model_cancer_view_spec) <- view_name
opt_xtrain_stats_cancer_view_spec <- lapply(view_name, function(X) {
  return(opt_xtrain_stats[[cancer_type]][[X]])
})
names(opt_xtrain_stats_cancer_view_spec) <- view_name

# Predict using rmtlr
prediction_view <- predict_with_rmtlr(
  view_name = view_name,
  view_info = view_info,
  view_data = view_data,
  opt_model_cancer_view_spec = opt_model_cancer_view_spec,
  opt_xtrain_stats_cancer_view_spec = opt_xtrain_stats_cancer_view_spec
)

olapuentesantana/easier documentation built on Feb. 25, 2024, 3:39 p.m.