MakeSeuObj_FromRawRNAData: MakeSeuObj_FromRawRNAData

In yuanqingyan/singleGEO: Query and Analysis of High-throughput Single Cell Sequencing Data from GEO

MakeSeuObj_FromRawRNAData

Description

Make Seurat object from the raw count data which were downloaded from GEO database. To use this function, a list containing the raw data should be provided. This function is re-written from Seurat package.

Usage

MakeSeuObj_FromRawRNAData(
  RawList = RawList,
  MtPattern = "^MT-",
  GSE.ID = "Test",
  MinFeature = 200,
  MaxFeature = 7500,
  MinCount = 400,
  MaxCount = 40000,
  MaxMT = 10,
  Norm.method = "lognorm",
  Scale.factor = 10000,
  Feature.selection.method = "vst",
  Nfeatures = 2000,
  Npcs = 50,
  Dims = 1:30,
  Resolution = 0.8,
  Algorithm = 1,
  Do.scale = TRUE,
  Do.center = TRUE
)

Arguments

RawList	A list containing the raw data. Each element of the list is for one raw dataset. The name of the element will be used as the ID of the raw dataset. Required.
MtPattern	The pattern to recognize mitochondria gene. Different species can have different labels. For example, for human data, MtPattern='^MT-' is appropriate, while for mouse it should be MtPattern='^mt-'. Optional. Default: MtPattern='^MT-'
GSE.ID	The GSE ID for this project. Default: Test
MinFeature	Minimal feature count to keep the cell. Default: 200
MaxFeature	Maximal feature count to keep the cell. Default: 7500
MinCount	Minimal RNA count to keep the cell. Default: 400
MaxCount	Maximal RNA count to keep the cell. Default: 40000
MaxMT	Percentage of mitochondria gene to filter out the cell. Default: 10
Norm.method	The method to normalize data. Two normalization methods are allowed and supported: lognorm (For LogNormalization) and sct(For sctransform). Default: lognorm
Scale.factor	The factor to scale up the data. Default: 10000
Feature.selection.method	The method for the top variable feature selection. This will feed to FindVariableFeatures function. Default: vst
Nfeatures	The number of top variable features for the FindVariableFeatures function. Default: 2000
Npcs	The number of pc to use for the functions of RunPCA. Default: 50
Dims	The number of top dimensions of reduction to use for the functions of FindNeighbors and RunUMAP. Default: 1:30
Resolution	The resolution value for FindClusters function. Default: 0.8
Algorithm	The algorithm to be used in FindClusters. Default: 1
Do.scale	Whether to scale the data or not. Binary data. This will feed to ScaleData function. Default: TRUE
Do.center	Whether to center the data or not. Binary data. This will feed to ScaleData function. Default: TRUE

Value

A list with each element corresponding to a Seurat object of a raw data.

Examples

library(singleGEO)

#

data(testData_GSE134174)

test_dat<-testData_GSE134174$TwoRawData
test_meta<-testData_GSE134174$TwoMetaData
list_GSE134174<-Splitdata_MakeDataList(InputData=test_dat,Group=test_meta$Donor)

##The following code setting big number of MaxFeature,MaxCount,MaxMT and small number
##of MinFeature,MinCount will keep all the cells

seu_GSE134174<-MakeSeuObj_FromRawRNAData(RawList=list_GSE134174,GSE.ID="GSE134174",MinFeature=1,MaxFeature=750000,
                                       MinCount=1, MaxCount=4000000,MaxMT=100)

yuanqingyan/singleGEO documentation built on Jan. 11, 2025, 6:35 p.m.