calc_z_score: Perform matrix Z-score normalization

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

Perform matrix Z-score normalization

Description

Applies z-score normalization on a numeric matrix per column. Z-score values are calculated based on the input matrix. If mean and standard deviation values are provided, these are used instead.

Usage

calc_z_score(X, mean, sd)

Arguments

X	numeric matrix.
mean	numeric vector with mean values.
sd	numeric vector with standard deviation values.

Value

A numeric matrix with values as z-scores.

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]

# apply z-score normalization
tpm_zscore <- calc_z_score(t(RNA_tpm))

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