scQC: Performs single-cell data quality control

In cailab-tamu/PCrTdMa: Construct and Compare scGRN from Single-Cell Transcriptomic Data

Performs single-cell data quality control

Description

This function performs quality control filters over the provided input matrix, the function checks for minimum cell library size, mitochondrial ratio, outlier cells, and the fraction of cells where a gene is expressed.

Usage

scQC(
  X,
  minLibSize = 1000,
  removeOutlierCells = TRUE,
  minPCT = 0.05,
  maxMTratio = 0.1
)

Arguments

X	Raw counts matrix with cells as columns and genes (symbols) as rows.
minLibSize	An integer value. Defines the minimum library size required for a cell to be included in the analysis.
removeOutlierCells	A boolean value (TRUE/FALSE), if TRUE, the identified cells with library size greater than 1.58 IQR/sqrt(n) computed from the sample, are removed. For further details see: ?boxplot.stats
minPCT	A decimal value between 0 and 1. Defines the minimum fraction of cells where the gene needs to be expressed to be included in the analysis.
maxMTratio	A decimal value between 0 and 1. Defines the maximum ratio of mitochondrial reads (mithocondrial reads / library size) present in a cell to be included in the analysis. It's computed using the symbol genes starting with 'MT-' non-case sensitive.

Value

A dgCMatrix object with the cells and the genes that pass the quality control filters.

References

Ilicic, Tomislav, et al. "Classification of low quality cells from single-cell RNA-seq data." Genome biology 17.1 (2016): 29.

Examples

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
)

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

par(
  mfrow = c(2, 2),
  mar = c(3, 3, 1, 1),
  mgp = c(1.5, 0.5, 0)
)
# Library Size
plot(
  Matrix::colSums(X),
  ylim = c(20, 70),
  ylab = 'Library Size',
 xlab = 'Cell',
  main = 'Library Size - Before QC'
)
abline(h = c(30, 58),
       lty = 2,
       col = 'red')
plot(
  Matrix::colSums(qcOutput),
  ylim = c(20, 70),
  ylab = 'Library Size',
  xlab = 'Cell',
  main = 'Library Size - After QC'
)
abline(h = c(30, 58),
       lty = 2,
       col = 'red')
# Mitochondrial ratio
mtGenes <- grepl('^mt-', rownames(X), ignore.case = TRUE)
plot(
  Matrix::colSums(X[mtGenes,]) / Matrix::colSums(X),
  ylim = c(0, 0.3),
  ylab = 'Mitochondrial Ratio',
  xlab = 'Cell',
  main = 'Mitochondrial Ratio - Before QC'
)
abline(h = c(0.1), lty = 2, col = 'red')
plot(
  Matrix::colSums(qcOutput[mtGenes,]) / Matrix::colSums(qcOutput),
  ylim = c(0, 0.3),
  ylab = 'Mitochondrial Ratio',
  xlab = 'Cell',
  main = 'Mitochondrial Ratio - Before QC'
)
abline(h = c(0.1), lty = 2, col = 'red')

par(oldPar)

cailab-tamu/PCrTdMa documentation built on Aug. 6, 2022, 8:11 p.m.