DISCO툴킷

DISCOtoolkit 1.1.0(2024년 6월 11일)

DISCOtoolkit은 DISCO 데이터베이스의 데이터 및 도구에 대한 액세스를 제공하는 R 패키지입니다. 그 기능은 다음과 같습니다:

• 샘플 메타데이터와 지정된 셀 유형을 기반으로 DISCO 데이터를 필터링하고 다운로드합니다.
• CELLiD: 세포 유형 주석
• scEnrichment: DISCO DEG를 사용한 유전자 세트 농축

요구 사항

DISCOtoolkit은 다음 패키지에 의존합니다:

• R (>= 4.0.0)
• pbmcapply
• 스트링거
• jsonlite
• 진전

설치

 devtools :: install_github( " [email protected]:JinmiaoChenLab/DISCOtoolkit.git " )

기본 사용법

DISCO 데이터 필터링 및 다운로드

library( DISCOtoolkit )

# find samples from normal lung tissue and sequenced by 10X Genomics 5' platform
# retain samples containing more than 100 Macrophages(or its children)
metadata = FilterDiscoMetadata(
  sample_id = NULL ,
  project_id = NULL ,
  tissue = " lung " ,
  disease = NULL ,
  platform = c( " 10x5' " ),
  sample_type = c( " control " , " adjacent normal " ),
  cell_type = " Macrophage " , 
  cell_type_confidence = " high " , 
  include_cell_type_children = T , 
  min_cell_per_sample = 100
)
# ## print information ###
# Fetching sample metadata
# Filtering sample
# Fetching cell type information
# Fetching ontology from DISCO database
# 18 samples and 64592 cells were found

# download filtered data into 'disco_data' folder
DownloadDiscoData( metadata , output_dir = " disco_data " )

셀리드

library( DISCOtoolkit )
library( Seurat )

metadata = FilterDiscoMetadata(
  sample_id = " ERX2757110 "
)

DownloadDiscoData( metadata , output_dir = " disco_data " )

rna = readRDS( " disco_data/ERX2757110.rds " )
rna = CreateSeuratObject( rna )
rna = NormalizeData( rna )
rna = FindVariableFeatures( rna )
rna = ScaleData( rna )
rna = RunPCA( rna )
rna = FindNeighbors( rna , dims = 1 : 10 )
rna = FindClusters( rna )

rna_average = AverageExpression( rna )
predict_ct = CELLiDCluster( rna = as.matrix( rna_average $ RNA ))


# It will download reference data and differential expression gene (DEG) data from DISCO and save them in the 'DISCOtmp' folder by default. You can reuse this data for subsequent CELLiD analyses as follow:

ref_data = readRDS( " DISCOtmp/ref_data.rds " )
ref_deg = readRDS( " DISCOtmp/ref_deg.rds " )
predict_ct = CELLiDCluster( rna = as.matrix( rna_average $ RNA ), ref_data = ref_data , ref_deg = ref_deg )


rna $ cell_type = predict_ct $ predict_cell_type_1 [as.numeric( rna $ seurat_clusters )]
rna = RunUMAP( rna , dims = 1 : 10 )
DimPlot( rna , group.by = " cell_type " , label = T )

sc강화

 markers = FindMarkers( rna , ident.1 = 0 , only.pos = T , logfc.threshold = 0.5 )
cellid_input = data.frame ( gene = rownames( markers ), logFC = markers $ avg_log2FC )
cellid_res = CELLiDEnrichment( cellid_input )

# also it will download 'ref_geneset.rds' in 'DISCOtmp' folder by default,
# You can reuse this data for subsequent CELLiDEnrichment analyses as follow:
ref = readRDS( " DISCOtmp/ref_geneset.rds " )
cellid_res = CELLiDEnrichment( cellid_input , reference = ref )