Perangkat DISCO

DISCOtoolkit 1.1.0 (11 Juni 2024)

DISCOtoolkit adalah paket R yang menyediakan akses ke data dan alat dari database DISCO. Fungsinya antara lain:

• Filter dan unduh data DISCO berdasarkan sampel metadata dan tipe sel tertentu
• CELLiD: anotasi tipe sel
• scEnrichment: pengayaan genset menggunakan DISCO DEGs

Persyaratan

DISCOtoolkit bergantung pada paket berikut:

• R (>= 4.0.0)
• pbmcapply
• stringr
• jsonlite
• kemajuan

Instalasi

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

Penggunaan Dasar

Filter dan unduh data 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 " )

SELID

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 )

scPengayaan

 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 )