scAnalyzer: Analysis pipeline for single cell RNA-seq data

In WubingZhang/scAnalyzer: Integrative analysis pipeline for analyzing single-cell RNA-seq data

Analysis pipeline for single cell RNA-seq data

Description

Analysis pipeline for single cell RNA-seq data

Usage

scAnalyzer(
  obj,
  project = NULL,
  analyses = c("qc", "doublet", "norm", "pca", "clustering"),
  norm.method = c("LogNormalize", "SCTransform"),
  outdir = "",
  resolution = 0.5,
  min.cells = 3,
  min.features = 200,
  max.features = 8000,
  percent.mt = NULL,
  subset.singlet = TRUE,
  scale.factor = 10000,
  nVarfeatures = 2000,
  nPCs = NULL,
  PC.Variance = 0.85
)

Arguments

obj	A matrix or data.frame of count table or a Seurat object.
project	A character, specifying the name of the project (also prefix of outputs).
analyses	A character vector, specifying the type of analysis to perform, available analysis include qc, doublet, norm, pca, clustering.
norm.method	LogNormalize or SCTransform.
outdir	The path to output figures.
resolution	The resolution for clustering. 0.5 by default.
min.cells	Include features detected in at least this many cells.
min.features	Include cells with more than this many features are detected.
max.features	Include cells with less than this many features are detected.
percent.mt	Include cells with less than this fraction of mitochondrial gene expression.
subset.singlet	Remove doublets from the data by subseting the singlets.
scale.factor	Scale factor.
nVarfeatures	The number of variable genes for downstream analysis.
nPCs	The number of PCs.
PC.Variance	At least this fraction of variance (80 explained by the Principal components, required when nPCs is not specified.

Details

This pipeline takes count table or seurat object as input, and performs data quality control, normalization, doublets removal (DoubletFinder), dimention reduction, cell clustering and identification of marker genes.

Value

Seurat object

Author(s)

Wubing Zhang

Examples

library(scAnalyzer);
pbmc.data <- Read10X(data.dir = "../data/pbmc3k/filtered_gene_bc_matrices/hg19/")
res <- scAnalyzer(pbmc.data)

WubingZhang/scAnalyzer documentation built on Jan. 30, 2023, 10:11 p.m.