DISCOинструментарий

DISCOtoolkit 1.1.0 (11 июня 2024 г.)

DISCOtoolkit — это пакет R, который обеспечивает доступ к данным и инструментам из базы данных DISCO. В его функции входят:

• Фильтруйте и загружайте данные DISCO на основе метаданных образца и указанных типов ячеек.
• CELLiD: аннотация типа ячейки
• scEnrichment: обогащение генетического набора с использованием DISCO DEG.

Требование

DISCOtoolkit зависит от следующих пакетов:

• Р (>= 4.0.0)
• pbmcaply
• стрингр
• jsonlite
• прогресс

Установка

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

Основное использование

Фильтруйте и загружайте данные DISCO

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 " )

CELLiD

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 )

scОбогащение

 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 )