scVDJplot

This repository was used to store the code, which was used in "Single-cell Profiling Reveals Distinct Adaptive Immune Hallmarks in MDA5+ Dermatomyositis with Therapeutic Implications"

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Attention:

All the code has been integrated into the R packages "scSensitiveGeneDefine";

This repository will be renamed as "scSensitiveGeneDefine"

Description:

scVDJplot is a R package which can be used in single cell VDJ sequencing caculating and visualization.

scVDJplot is build based on Seurat >= 3.0.1; data.table >= 1.14.2; entropy >= 1.2.1; ;dplyr >= 1.0.0; shazam >= 1.1.0; alakazam >= 1.2.0; All of these four dependent packages are R package.

scVDJplot intend to publish on Nature Communications.

Installation(in R/Rstudio)

devtools::install_github("Zechuan-Chen/scVDJplot")

Dependencies

scVDJplot requires the following R packages:

• Seurat (>=3.0.1)
• data.table （>= 1.14.2）
• dplyr (>=1.0.0)
• entropy (>=1.2.1)
• shazam (>= 1.1.0)
• alakazam (>= 1.2.0)
• NOTE:The version of these depend packages are temporary.

Example code for scVDJplot

# The count data and meta.data have saved
# We can get data from /data/count.RData  and /data/meta.data

# Step1: intergrated change-o result with Seurat S4 object
object<-runVDJ.intergrated(object,tsv.file="~/changeo-result/filtered_heavy_germ-pass.tsv",Seurat.object=object,runSHM=T) 

# Or you can get a simple data from our package 
load("count.RData")
load("meta.data.RData")
object<-CreateSeuratobject(count=count,meta.data=meta.data)
object %>% NormalizeData() %>% FindVariableFeatures() %>%  # Common Seurat pipline for visualization
  ScaleData(assay="RNA")  %>% 
  RunPCA(npcs = 40,assay="RNA")%>% 
  FindNeighbors(assay="RNA",dims = 1:40)%>%
  FindClusters(assay="RNA",resolution = 0.6)%>%
  RunUMAP(dims=1:40) -> object

# Step2: Visualization of the IGHV usage in each groups
P1<-runVDJ.Usage(object,v.call="germline_v_call",sample.label="samples",group.label="group",min.cells=10)

# Step3: Visualization of the IGHV usage difference in 3 groups
P2<-runVDJ.diffUsage(object,v.call="germline_v_call",
                 sample.label="samples",group.label="group",
                 GroupA=c("P05","P06","P07"),GroupB=c("P014","P15","P16"),reference=c("P01","P02","P03"),
                 min.cells=10)

# Step4: Visualization of the selection strength
P3<-runVDJ.Selec.Strength(object,group.label='group',
                                isotype.label="c_call",isotype="IGHM",
                                cell.type.label="sub_cell_type2",cell.type=" scB8-ASC",clone.id="clone_id")

# Step5: Caculate and visualizate clonality and diversity
P4<-runVDJ.Diversity(object,cell.type.label="sub_cell_type2",clone.id='clone_id')
P4<-runVDJ.Clonality(object,cell.type.label="sub_cell_type2",clone.id='clone_id')

# Step6: Caculate and visualizate the clonetype sharing between different cell types.
P5<-runVDJ.CloneSharing(object,cell.type.label = "sub_cell_type2",clone.id="clone_id")
P6<-runVDJ.CellType.Connection(object,cell.type.label = "sub_cell_type2",clone.id="clone_id")