runSeurat: The default pipline for processing scRNA-seq by Seurat

In Zechuan-Chen/scSensitiveGeneDefine: Define sensitive Gene in single cell data

Description

Analysis the single cell data according to the default parameters of Seurat. Users can adjust the parameters according to their needs.

This step can be skip, if user can provided the processed data.

The processed data should finish several steps by using Seurat: Normalized data, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, FindClusters and RunUMAP.

Usage

runSeurat(
  data.dir,
  PC = 40,
  resolution = 0.6,
  mt.cut_off = 20,
  min_nFeature.cut_off = 200,
  sample_name = "scRNA-seq",
  data_type = "Expression_matrix",
  filter_doublet = TRUE,
  algorithm = 1
)

Arguments

data.dir:	Path to the matrix from CellRanger reults, such as "~/sample_CellRanger_result/outs/filtered_feature_bc_matrix"
PC:	Number of PC used for demension reduction and clustering. default:40
resolution:	Choose appropriate resolution value to adjust the number of clusters. default:0.6
mt.cut_off:	Filter out the cells with a high percentage of MT-genes. default:20(%) (Only apply to samples for human)
min_nFeature.cut_off:	Filter low quality cell by number of Features in each cells. default:200
sample_name:	Samples name of scRNA-seq data. default:"scRNA-seq"
data_type:	Type of scRNA-seq data. Temporary, only "Expression_matrix" and "TotalSeq" data are supported
filter_doublet:	Whether use DoubletFinder to remove Doublet in scRNA-seq; Attention, DoubletFinder performed not pretty well in detect the doublet of same type of cells.
algorithm:	Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python.

Zechuan-Chen/scSensitiveGeneDefine documentation built on March 12, 2021, 8:02 p.m.