get_OE_bulk: Compute overall expression (OE) of the immune resistance...

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

Compute overall expression (OE) of the immune resistance program used in the computation of repressed immune resistance signature (RIR) score.

Description

This function calculates the overall expression of the immune resistance program which is based on a set of gene signatures associated with T cell exclusion, post-treatment and functional resistance.

Usage

get_OE_bulk(
  r,
  gene_sign = NULL,
  num_rounds = 1000,
  full_flag = FALSE,
  verbose = TRUE
)

Arguments

r	list containing a numeric matrix with bulk RNA-Seq data (tpm values) and a character string with the available gene names.
gene_sign	list containing different character strings associated with subsets of the resistance program.
num_rounds	integer value related to the number of random gene signatures samples to be computed for normalization. Original work indicates that 1000 random signatures were sufficient to yield an estimate of the expected value.
full_flag	logical flag indicating whether to return also random scores.
verbose	logical flag indicating whether to display messages about the process.

Details

The source code was provided by original work: https://github.com/livnatje/ImmuneResistance

Value

A numeric matrix with computed scores for each sample and subset of signatures included in the immune resistance program (rows = samples; columns = gene signatures)

References

Jerby-Arnon, L., Shah, P., Cuoco, M.S., Rodman, C., Su, M.-J., Melms, J.C., Leeson, R., Kanodia, A., Mei, S., Lin, J.-R., et al. (2018). A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell 175, 984–997.e24. https://doi.org/10.1016/j.cell.2018.09.006.

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"]]

# 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]

# Log2 transformation:
log2_RNA_tpm <- log2(RNA_tpm + 1)

# Prepare input data
r <- list()
r$tpm <- log2_RNA_tpm
r$genes <- rownames(log2_RNA_tpm)

# Gene signature of immune resistance program
score_signature_genes <- suppressMessages(easierData::get_scores_signature_genes())
RIR_gene_signature <- score_signature_genes$RIR

# Apply function to calculate OE:
res_scores <- get_OE_bulk(r, gene_sign = RIR_gene_signature, verbose = TRUE)

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