get_semi_random_OE: Compute random scores of the immune resistance program used...

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

Compute random scores of the immune resistance program used in the computation of repressed immune resistance signature (RIR) score.

Description

Calculates random scores to yield a robust estimate of the immune resistance program values. This is used by get_OE_bulk function.

Usage

get_semi_random_OE(
  r,
  genes_dist_q,
  b_sign,
  num_rounds = 1000,
  full_flag = FALSE,
  random_seed = 1234
)

Arguments

r	list containing a numeric matrix with bulk RNA-Seq data (tpm values) and a character string with the available gene names.
genes_dist_q	factor variable obtained as output from the function discretize. Original work binned genes into 50 expression bins according their average gene expression across samples.
b_sign	logical vector representing whether signature genes were found in bulk tpm matrix.
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.
random_seed	integer value to set a seed for the selection of random genes used to generate a random score.

Details

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

Value

A numeric vector containing the estimated random score for each sample.

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

# Compute gene average expression across samples
r$genes_dist <- r$genes_mean <- rowMeans(r$tpm)

# Center gene expression matrix
r$zscores <- sweep(r$tpm, 1, r$genes_mean, FUN = "-")

# Bin genes into 50 expression bins according to their average
r$genes_dist_q <- discretize(r$genes_dist, n.cat = 50)

# Match genes from exc.down signature with genes from expression matrix
b_sign <- is.element(r$genes, RIR_gene_signature[["exc.down"]])

# Compute random score:
rand_scores <- get_semi_random_OE(r, r$genes_dist_q, b_sign)

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