# Code below was used to generate the object :
# sobj = readRDS("/shared/projects/2325_ebaii/SingleCell/TD_DATA/DATA_START/CLUSTERING/TD3A.TDCT_Scal2K.IntH20.RDS")
# sobj = Seurat::RunPCA(sobj)
# sobj = Seurat::RunTSNE(sobj)
# sobj = sobj[, 1:200]
# saveRDS(sobj, file = "/shared/projects/2325_ebaii/SingleCell/TD_RMDs/Introduction_Rmd/sobj.rds")


# Load data
sobj = readRDS("/shared/projects/2325_ebaii/SingleCell/TD_RMDs/Introduction_Rmd/sobj.rds")
sobj

# Libraries of interest
library(Seurat)
library(ggplot2)

# Visualize our favorite gene
FeaturePlot(sobj, reduction = "tsne", features = "Ptcra") +
  theme(aspect.ratio = 1)

# Ptcra is more expressed on the right, but how many cells are concerned ?

# Which cells are positive for Ptcra ?
sobj$is_Ptcra_pos = (FetchData(sobj, "Ptcra")[, 1] > 0)
DimPlot(sobj, reduction = "tsne", group.by = "is_Ptcra_pos") +
  theme(aspect.ratio = 1)

# How many cells are positive ?
table(sobj$is_Ptcra_pos)

# half of cells are Ptcra+