scTenifoldNet (version 1.3)

cpmNormalization: Performs counts per million (CPM) data normalization

Description

This function normalizes the count data present in a given matrix using counts per million normalization (CPM). Each gene count for each cell is divided by the total counts for that cell and multiplied by 1e6. No log-transformation is applied.

Usage

cpmNormalization(X)

Arguments

X

Raw counts matrix with cells as columns and genes (symbols) as rows

Value

A dgCMatrix object with the count per million (CPM) normalized values.

References

Vallejos, Catalina A., et al. "Normalizing single-cell RNA sequencing data: challenges and opportunities." Nature methods 14.6 (2017): 565.

Examples

# NOT RUN {
library(scTenifoldNet)

# Simulating of a dataset following a negative binomial distribution with high sparcity (~67%)
nCells = 2000
nGenes = 100
set.seed(1)
X <- rnbinom(n = nGenes * nCells, size = 20, prob = 0.98)
X <- round(X)
X <- matrix(X, ncol = nCells)
rownames(X) <- c(paste0('ng', 1:90), paste0('mt-', 1:10))

# Performing Single cell quality control
qcOutput <- scQC(
  X = X,
  minLibSize = 30,
  removeOutlierCells = TRUE,
  minPCT = 0.05,
  maxMTratio = 0.1
)
# Performing Counts per million Normalization (CPM)
normalizationOutput <- cpmNormalization(qcOutput)

# Visualizing the differences
oldPar <- par(no.readonly = TRUE)

par(
  mfrow = c(1, 2),
  mar = c(3, 3, 1, 1),
  mgp = c(1.5, 0.5, 0)
)
plot(
  Matrix::colSums(qcOutput),
  ylab = 'Library Size',
  xlab = 'Cell',
  main = 'Before CPM Normalization'
)
plot(
  Matrix::colSums(normalizationOutput),
  ylab = 'Library Size',
  xlab = 'Cell',
  main = 'After CPM Normalization'
)

par(oldPar)
# }