Filter: The Pre-processing Data.

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

The Pre-processing Data.

Description

After input data, RISC preliminarily filter datasets by using three criteria: first, discard cells with too low/high raw counts/UMIs by distribution analysis. Second, remove cells with too low expressed genes. Lastly, filter out the genes only expressed in few cells.

Usage

scFilter(
  object,
  min.UMI = NULL,
  max.UMI = NULL,
  min.gene = NULL,
  min.cell = NULL,
  mitochon = 1,
  gene.ratio = 0.05,
  is.filter = TRUE
)

Arguments

object	RISC object: a framework dataset.
min.UMI	The min UMI for valid cells is usually based on the distribution analysis, with more than 2.5 percentage of UMI distribution, discarding the cells with too few UMIs. This parameter can be adjusted manually.
max.UMI	The max UMI for valid cells is usually based on the distribution analysis, with less than 97.5 percentage of UMI distribution, discarding the cells with too many UMIs.This parameter can be adjusted manually.
min.gene	The min number of expressed genes for valid cells. The default is based on the distribution analysis, with more than 0.5 percentage of gene distribution. This parameter can be adjusted manually.
min.cell	The min number of cells for valid expressed genes. The default is based on the distribution analysis, with more than 0.5 percentage of cell distribution. This parameter can be adjusted manually.
mitochon	The cutoff of the mitochondrial UMI proportion for valid cells.
gene.ratio	The cutoff of the proportions of genes in UMIs.
is.filter	Whether filter the data.

Value

RISC single cell dataset, the coldata and rowdata slots.

References

Liu et al., Nature Biotech. (2021)

Examples

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

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