DISCOtoolkit
Discotoolkit_R_1.1.0
DISCOtoolkit عبارة عن حزمة R توفر الوصول إلى البيانات والأدوات من قاعدة بيانات DISCO. وظائفها تشمل:
تعتمد مجموعة أدوات DISCOtoolkit على الحزم التالية:
devtools :: install_github( " [email protected]:JinmiaoChenLab/DISCOtoolkit.git " )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 " )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 ) 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 )
