Normalize: The Processing Data.

In bioinfoDZ/RISC: Robust Integration of Single-Cell RNA-Seq Datasets

The Processing Data.

Description

After data filtration, RISC will normalized the raw counts/UMIs by using size factors which are calculated by the raw counts/UMIs of each cell and will remove sequencing depth batch. The output will be transformed in log1p. The gene expression values of RISC object for the subsequent analyses is based on the normalized data. Here two kinds of normalization can be employed: one is based on the least absolute deviations, while the other is from the least square.

Usage

scNormalize(
  object,
  method = "robust",
  libsize = 1e+06,
  remove.mito = FALSE,
  norm.dis = TRUE,
  large = TRUE,
  ncore = 1
)

Arguments

object	RISC object: a framework dataset.
method	A method for scdataset normalization, two options: "cosine" and "robust".
libsize	The standard sum of the UMI in each cell.
remove.mito	Remove mitochondrial genes from library size.
norm.dis	Normalize the distribution of count data.
large	Whether a large size data (ncell > 50,000)
ncore	The multiple cores for parallel calculating.

Value

RISC single cell dataset, the assay and rowdata slots.

References

Boscovich, R.J. (1757)

Thompson, W.J., Computers in Physics (1992)

Examples

# RISC object
obj0 = raw.mat[[5]]
obj0 = scFilter(obj0, min.UMI = 0, max.UMI = Inf, min.gene = 10, min.cell = 3)
obj0 = scNormalize(obj0)

bioinfoDZ/RISC documentation built on March 30, 2024, 9:19 p.m.