inferCNV analysis

1. load libraries

2. Load Seurat Object

#Load Seurat Object merged from cell lines and a control(PBMC) after filtration
load("../../../To_Transfer_between_computers/23-Harmony_Integration/0-robj/5-Harmony_Integrated_All_samples_Merged_CD4Tcells_final_Resolution_Selected_0.8_ADT_Normalized_cleaned_mt.robj")




infercnv_object <- readRDS("inferCNV_Analysis/Silvia_Script_on_cellLines/SilviaScript_L1_cluster_by_group/run.final.infercnv_obj")


infercnv_object_all <- readRDS("inferCNV_Analysis/L1-L7_PBMC_subcluster/run.final.infercnv_obj")

3.Transferring the scores to a FeaturePlot

# sample <- All_samples_Merged
# 
# rm(All_samples_Merged)
# gc()

#----------------------------------------------------
# 1. Load chromosome locations
#----------------------------------------------------
utils::data("human_chr_locations", package = "SCpubr")

#----------------------------------------------------
# 2. Align Seurat and inferCNV objects on common cells
#----------------------------------------------------

# Step 1: Get intersecting cell barcodes
common_cells <- intersect(
  colnames(sample),
  colnames(infercnv_object_all@expr.data)
)

# Step 2: Subset Seurat object
seurat_subset <- subset(sample, cells = common_cells)

# Step 3: Subset inferCNV object
infercnv_object_all@expr.data <- infercnv_object_all@expr.data[, common_cells, drop = FALSE]

#----------------------------------------------------
# 3. Run the CNV plot with SCpubr
#----------------------------------------------------
out <- SCpubr::do_CopyNumberVariantPlot(
  sample = seurat_subset,
  infercnv_object = infercnv_object_all,
  using_metacells = FALSE,
  chromosome_locations = human_chr_locations,

)

#----------------------------------------------------
# 4. Combine and display UMAP panels
#----------------------------------------------------
library(patchwork)  # required for "|" operator
p
p2 <- out$`1_umap` | out$`1p_umap` | out$`1q_umap`
p2


out

4.Visualization

out