迪斯可工具包

DISCO 工具包 1.1.0（2024 年 6 月 11 日）

DISCOtoolkit 是一個 R 包，提供對 DISCO 資料庫中的資料和工具的存取。其功能包括：

• 根據樣本元數據和指定的細胞類型過濾和下載 DISCO 數據
• CELLiD：細胞類型註釋
• scEnrichment：使用 DISCO DEG 進行基因組富集

要求

DISCOtoolkit 依賴以下軟體包：

• R (>= 4.0.0)
• PBMC申請
• 縱樑
• jsonlite
• 進步

安裝

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

基本用法

過濾並下載 DISCO 數據

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 " )

賽利德

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 )

科學富集

 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 )