Liver Droplet- Raw data Notebook

tissue_of_interest = "Liver"
library(here)
source("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/boilerplate.R")
#tiss = load_tissue_droplet(tissue_of_interest)
#library(scater)
library(dplyr)
library(Seurat)
library(cowplot)
#library(MAST)
########## function load_tissue_droplet############
metadata_chow <- read.csv("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/Chow_metadata.csv", sep=",", header = TRUE)
colnames(metadata_chow)[1] <- "channel"
tissue_metadata_chow = filter(metadata_chow, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex', 'mouse.id')]

raw.data <- Read10X("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/Liver_Chow1/")
colnames(raw.data) <- lapply(colnames(raw.data), function(x) paste0(tissue_metadata_chow$channel[1],'_',x))
  meta.data = data.frame(row.names = colnames(raw.data))
  meta.data['channel'] = tissue_metadata_chow$channel[1]

    if (length(tissue_metadata_chow$channel) > 1){
    # Some tissues, like Thymus and Heart had only one channel
    for(i in 2:nrow(tissue_metadata_chow)){
subfolder = paste0("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/",tissue_of_interest, '_', tissue_metadata_chow$channel[i])
      new.data <- Read10X(data.dir = subfolder)
      colnames(new.data) <- lapply(colnames(new.data), function(x) paste0(tissue_metadata_chow$channel[i],'_', x))
      new.metadata = data.frame(row.names = colnames(new.data))
      new.metadata['channel'] = tissue_metadata_chow$channel[i]

      raw.data = cbind(raw.data, new.data)
      meta.data = rbind(meta.data, new.metadata) }}
  
  
  rnames = row.names(meta.data)
  meta.data <- merge(meta.data, tissue_metadata_chow, sort = F)
  row.names(meta.data) <- rnames
   
  # Order the cells alphabetically to ensure consistency.
  ordered_cell_names = order(colnames(raw.data))
  raw.data = raw.data[,ordered_cell_names]
  meta.data = meta.data[ordered_cell_names,]
  # Find ERCC's, compute the percent ERCC, and drop them from the raw data.
  erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE)
  percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data)
  ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE)
  raw.data <- raw.data[-ercc.index,]
  
  ncRNA.genes <- grep(pattern = "^ncRNA", x = rownames(x = raw.data), value = TRUE)
  percent.ncRNA <- Matrix::colSums(raw.data[ncRNA.genes, ])/Matrix::colSums(raw.data)
 
  # Create the Seurat object with all the data
  droplet <- CreateSeuratObject(raw.data)   # dropseq
  droplet <- AddMetaData(object = droplet, meta.data) 
  droplet@meta.data$tech <- "Chow"
#droplet <- SubsetData(droplet,subset.names = c("nGene", "nUMI"), low.thresholds = c(500, 1000))  # old version of seurat
droplet <-  subset(droplet, subset = nFeature_RNA > 500 & nCount_RNA > 1000)
droplet <- NormalizeData(droplet, verbose = FALSE)
droplet <- FindVariableFeatures(droplet, selection.method = "vst", nfeatures = 2000)
droplet$stim <- "Chow"

### run chow umap separatly
droplet <- ScaleData(droplet, verbose = FALSE)
droplet <- RunPCA(droplet, npcs = 30, verbose = FALSE)
droplet <- RunUMAP(droplet, reduction = "pca", dims = 1:25)
droplet <- FindNeighbors(droplet, reduction = "pca", dims = 1:25)
droplet <- FindClusters(droplet, resolution = 0.07 )   
chowp1<- UMAPPlot(droplet, reduction = "umap", group.by = "channel", label=TRUE, label.size=5)
chowp4 <- UMAPPlot(droplet, label=TRUE, label.size=6)
plot_grid(chowp1, chowp4) 

chow_samples <- droplet
chow_samples$label_channel <- paste(Idents(chow_samples), chow_samples$channel, sep = "_")
chow_samples$seurat_clusters <- Idents(chow_samples)
Idents(chow_samples) <- "label_channel_cluster"


### this is tranformation option for develpment SCtransform program #######
droplet <- SCTransform(droplet,verbose =TRUE)


####################### NASH #######################################
metadata_nash <- read.csv("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/Nash_metadata.csv", sep=",", header = TRUE)
colnames(metadata_nash)[1] <- "channel"
tissue_metadata_nash = filter(metadata_nash, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex', 'mouse.id')]

raw.data1 <- Read10X("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/Liver-10X_Nash1/")
colnames(raw.data1) <- lapply(colnames(raw.data1), function(x) paste0(tissue_metadata_nash$channel[1],'_',x))
  meta.data1 = data.frame(row.names = colnames(raw.data1))
  meta.data1['channel'] = tissue_metadata_nash$channel[1]

    if (length(tissue_metadata_nash$channel) > 1){
    # Some tissues, like Thymus and Heart had only one channel
    for(i in 2:nrow(tissue_metadata_nash)){
subfolder = paste0("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/NASH/data/",tissue_of_interest, '-', tissue_metadata_nash$channel[i])
      new.data1 <- Read10X(data.dir = subfolder)
      colnames(new.data1) <- lapply(colnames(new.data1), function(x) paste0(tissue_metadata_nash$channel[i],'_', x))
      new.metadata1 = data.frame(row.names = colnames(new.data1))
      new.metadata1['channel'] = tissue_metadata_nash$channel[i]
      raw.data1 = cbind(raw.data1, new.data1)
      meta.data1 = rbind(meta.data1, new.metadata1) }}
  
  rnames = row.names(meta.data1)
  meta.data1 <- merge(meta.data1, tissue_metadata_nash, sort = F)
  row.names(meta.data1) <- rnames
   
  # Order the cells alphabetically to ensure consistency.
  ordered_cell_names = order(colnames(raw.data1))
  raw.data1 = raw.data1[,ordered_cell_names]
  meta.data1 = meta.data1[ordered_cell_names,]
  # Find ERCC's, compute the percent ERCC, and drop them from the raw data.
  erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data1), value = TRUE)
  percent.ercc <- Matrix::colSums(raw.data1[erccs, ])/Matrix::colSums(raw.data1)
  ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data1), value = FALSE)
  raw.data1 <- raw.data1[-ercc.index,]
  ncRNA.genes <- grep(pattern = "^ncRNA", x = rownames(x = raw.data1), value = TRUE)
  percent.ncRNA <- Matrix::colSums(raw.data1[ncRNA.genes, ])/Matrix::colSums(raw.data1)
 
  # Create the Seurat object with all the data
  droplet1 <- CreateSeuratObject(raw.data1)   # dropseq
  droplet1 <- AddMetaData(object = droplet1, meta.data1) 
  droplet1 <- subset(droplet1, subset = nFeature_RNA > 500 & nCount_RNA > 1000)
  droplet1@meta.data$tech <- "Nash"
  droplet1 <- NormalizeData(droplet1, verbose = FALSE)
  droplet1 <- FindVariableFeatures(droplet1, selection.method = "vst", nfeatures = 2000)
  droplet1$stim <- "Nash"

  
droplet1 <- ScaleData(droplet1, verbose = FALSE)
droplet1 <- RunPCA(droplet1, npcs = 30, verbose = FALSE)
droplet1 <- RunUMAP(droplet1, reduction = "pca", dims = 1:25)
droplet1 <- FindNeighbors(droplet1, reduction = "pca", dims = 1:25)
droplet1 <- FindClusters(droplet1, resolution = 0.07 )   
nashp1<- UMAPPlot(droplet1, reduction = "umap", group.by = "stim", label=TRUE, label.size=5)
nashp4 <- UMAPPlot(droplet1, label=TRUE, label.size=6)
plot_grid(nashp1, nashp4)
  
  droplet <- SCTransform(droplet,verbose =TRUE)
  droplet1 <- SCTransform(droplet1,verbose =TRUE)


#######################3 option 2 standard workflow merging ################################
chow.list <- SplitObject(droplet, split.by = "channel")
#for (i in names(pbmc.list)) {
    #pbmc.list[[i]] <- SCTransform(pbmc.list[[i]], verbose = FALSE)
#}
nash.list <- SplitObject(droplet1, split.by = "channel")

chow_nash <- c(chow.list, nash.list)
features <- SelectIntegrationFeatures(object.list = chow_nash, nfeatures = 3000)
chow_nash <- PrepSCTIntegration(object.list = c(droplet,droplet1), anchor.features = features)
# This command returns dataset 5.  We can also specify multiple refs. (i.e. c(5,6))
reference_dataset <- which(names(chow_nash) == "Chow")
anchors <- FindIntegrationAnchors(object.list = chow_nash, normalization.method = "SCT", 
    anchor.features = features, reference = reference_dataset)
integrated <- IntegrateData(anchorset = anchors, normalization.method = "SCT")
DefaultAssay(integrated) <- "integrated"
integrated <- ScaleData(integrated, verbose = FALSE)
integrated <- RunPCA(object = integrated, npcs=30,verbose = FALSE)
integrated <- RunUMAP(object = integrated, dims = 1:25)
integrated <- FindNeighbors(integrated, reduction = "pca", dims = 1:25)
integrated <- FindClusters(integrated, resolution = 0.5 )   


##### label transfer appraoches #####33
for (i in 1:length(chow.list)) {
    chow.list[[i]] <- NormalizeData(chow.list[[i]], verbose = FALSE)
    chow.list[[i]] <- FindVariableFeatures(chow.list[[i]], selection.method = "vst", 
        nfeatures = 2000, verbose = FALSE)
}

ref.features <- SelectIntegrationFeatures(object.list = chow.list, nfeatures = 3000)

ref.anchors <- FindIntegrationAnchors(object.list = chow.list,  
    anchor.features =ref.features, verbose = TRUE)
ref.integrated <- IntegrateData(anchorset = ref.anchors, normalization.method = "LogNormalize", 
    verbose = FALSE)


DefaultAssay(ref.integrated) <- "integrated"
# Run the standard workflow for visualization and clustering
ref.integrated <- ScaleData(ref.integrated, verbose = FALSE)
ref.integrated <- RunPCA(ref.integrated, npcs = 30, verbose = FALSE)
ref.integrated <- RunUMAP(ref.integrated, reduction = "pca", dims = 1:30)
ref.integrated <- FindNeighbors(ref.integrated, reduction = "pca", dims = 1:30)
ref.integrated <- FindClusters(ref.integrated, resolution = 0.07 )   


p1 <- DimPlot(ref.integrated, reduction = "umap", group.by = "channel")
p2 <- DimPlot(ref.integrated, reduction = "umap",  label = TRUE, repel = TRUE) 
plot_grid(p1, p2)




transfer.anchors <- FindTransferAnchors(reference = ref.integrated, query =droplet1, dims = 1:30)
predictions <- TransferData(anchorset = transfer.anchors, refdata = ref.integrated$seurat_clusters,  dims = 1:30)
droplet1 <- AddMetaData(droplet1, metadata = predictions)
droplet1$prediction.match <- droplet1$predicted.id == droplet1$
table(pancreas.query$prediction.match)
                                                   
####### old apprach for integration without reference-based  t-SNE and Clustering ########################
anchors <- FindIntegrationAnchors(object.list = list(droplet, droplet1), dims = 1:50) # defeault anchor.feature=2000
#anchors <- FindIntegrationAnchors(object.list = list(lnc5998, droplet1), dims = 1:50, anchor.features = 3000)
combined <- IntegrateData(anchorset = anchors, dims = 1:50)    
DefaultAssay(combined) <- "integrated"
# Run the standard workflow for visualization and clustering
combined <- ScaleData(combined, verbose = FALSE)
combined <- RunPCA(combined, npcs = 30, verbose = FALSE)
                                                   
# t-SNE and Clustering
combined <- RunUMAP(combined, reduction = "pca", dims = 1:25)
combined <- FindNeighbors(combined, reduction = "pca", dims = 1:25)
combined <- FindClusters(combined, resolution = 0.07 )   
#combined <- RunTSNE(combined, reduction = "pca", dims = 1:20)
 # Visualization
p1 <- UMAPPlot(combined, reduction = "umap", group.by = "stim", label=TRUE, label.size=5)
p2 <- UMAPPlot(combined, reduction = "umap", group.by = "mouse.sex")
p3 <- UMAPPlot(combined, reduction = "umap", label = TRUE, label.size=3)
p4 <- UMAPPlot(combined, label=TRUE, label.size=6)
plot_grid(p1, p4) 
DimPlot(combined, reduction = "umap", split.by = "stim")   


## tsne based visualisation 
p5 <- DimPlot(combined, reduction = "tsne", group.by = "stim")
p6 <- DimPlot(combined, reduction = "tsne", group.by = "mouse.sex")
p7 <- DimPlot(combined, reduction = "tsne", label = TRUE)
p8 <- TSNEPlot(combined, label =T)
plot_grid(p6, p7,p8) 
DimPlot(combined, reduction = "tsne", split.by = "stim") 





 # New Visualization
p1 <- UMAPPlot(integrated, reduction = "umap", group.by = "stim", label=TRUE, label.size=5)
p2 <- UMAPPlot(integrated, reduction = "umap", group.by = "mouse.sex")
p3 <- UMAPPlot(integrated, reduction = "umap", label = TRUE, label.size=3)
p4 <- UMAPPlot(integrated, label=TRUE, label.size=6)
plot_grid(p1, p4) 
DimPlot(integrated, reduction = "umap", split.by = "stim")   


  

diffusion plt code

# Before running MDS, we first calculate a distance matrix between all pairs of cells.  Here we
# use a simple euclidean distance metric on all genes, using scale.data as input
d <- dist(t(GetAssayData(combined, slot = "scale.data")))
# Run the MDS procedure, k determines the number of dimensions
mds <- cmdscale(d = d, k = 2)
# cmdscale returns the cell embeddings, we first label the columns to ensure downstream
# consistency
colnames(mds) <- paste0("MDS_", 1:2)
# We will now store this as a custom dimensional reduction called 'mds'
combined[["mds"]] <- CreateDimReducObject(embeddings = mds, key = "MDS_", assay = DefaultAssay(combined))

# We can now use this as you would any other dimensional reduction in all downstream functions
DimPlot(combined, reduction = "mds", pt.size = 0.5)

genes_hep_main =c('Alb', 'Ttr', 'Apoa1', 'Serpina1c')
genes_hep = c('Alb', 'Ttr', 'Apoa1', 'Serpina1c',
                   'Cyp2e1', 'Glul', 'Oat', 'Gulo',
                   'Ass1', 'Hamp', 'Gstp1', 'Ubb',
                   'Cyp2f2', 'Pck1', 'Hal', 'Cdh1')
genes_endo = c('Pecam1', 'Nrp1', 'Kdr','Oit3')
genes_kuppfer = c( 'Clec4f', 'Cd68')
genes_nk = c('Il2rb', 'Nkg7', 'Cxcr6', 'Gzma')
genes_b = c('Cd79a', 'Cd79b')
genes_bec = c('Epcam', 'Krt19', 'Krt7')
genes_immune = 'Ptprc'
HSC = c("Dcn","Lama1","Nes")
Dividing = "Top2a"
Bplasma= "Jchain"
Mac= "Csf1r"
Chol="Sox9"

all_genes = c(genes_hep, genes_endo, genes_kuppfer, Mac,Chol,genes_nk, genes_b,Bplasma, genes_bec, genes_immune, HSC, Dividing)
genes_bec_b_immune  = c(genes_bec,genes_b,genes_immune)
genes_zones = c('Cyp2e1', 'Glul', 'Oat', 'Gulo',
              'Ass1', 'Hamp', 'Gstp1', 'Ubb',
              'Cyp2f2', 'Pck1', 'Hal', 'Cdh1')

receptor_KO <- c("ncRNA-inter-chr7-5998","Cyp2b10","Nr1i2","Nr1i3","Ppara","Pparg","Ppargc1b","Ppard")

cell <- c("Stab2","Csf1r","Cd3g","Ebf1","Irf8","Sox9","Apoc3","Top2a","Dcn")

DefaultAssay(droplet) <- "RNA"
#droplet <- NormalizeData(combined, verbose = TRUE, normalization.method = "RC", scale.factor = 1e6)
combined <- NormalizeData(combined, verbose = TRUE)


DotPlot(combined, features = all_genes)
FeaturePlot(combined, features = genes_hep_main, min.cutoff = "q9")
#hepatocytes 
subtissplot <- DotPlot(combined, features = c(genes_hep_main, genes_endo, genes_bec_b_immune, genes_kuppfer, genes_nk))
PC <- DotPlot(combined, features = c(genes_hep_main,genes_zones))
NPC <- DotPlot(combined, features = c(genes_endo,genes_kuppfer, genes_nk))
all <- DotPlot(combined, features=c(all_genes))

### coexpression plots####

f1 <- FeaturePlot(KO.cells, features = c('Cyp2b10','ncRNA-inter-chr7-5998'), reduction = "mds", order = TRUE,split.by = "stim", blend = TRUE,sort.cell = TRUE, max.cutoff = 0.5)


########## this is exact averaging formula ###############33
  x <- (AverageExpression(KO.cells, verbose = TRUE, assays = "RNA" ,slot="counts")$RNA)
   x["ncRNA-inter-chr7-5998",]
#                         G171B    G171C
#ncRNA-inter-chr7-5998 1.871795 1.091463
########
#Idents(combined) <- factor(Idents(combined), levels = c(0,1,12))
markers.to.plot <- c("Alb","ncRNA-inter-chr7-5998")
DotPlot(combined, features = rev(markers.to.plot), cols = c("blue", "red"), dot.scale = 8, 
    split.by = "stim") + RotatedAxis()

FeaturePlot(combined, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","dSaCas9","KRAB","AAV8-mCherry"), split.by = "stim", max.cutoff = 3, cols = c("grey", "red"))


######################### vlnplot ##########################

plots <- VlnPlot(combined, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","dSaCas9"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)
CombinePlots(plots = plots, ncol = 1)

plots <- VlnPlot(combined, features = c("Lhx4","Dtna","Fam189a1","Galnt16","Kalrn"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)
CombinePlots(plots = plots, ncol = 1)


#endothelial
DotPlot(combined, features = genes_endo)

#zones
zones <- DotPlot(combined, features = genes_zones)

f1 <- FeaturePlot(combined, features = c('Cyp2e1','Cyp2f2','Ass1'), min.cutoff = "q9", reduction = "tsne")

DimPlot(combined, label = TRUE)

save(combined, file="Seurat_smart-drop_integrated.Robj")


################# save raw counts from cluster #####################

Idents(combined) <- "stim"

### to avergae out the matrix from KO cells 

combined.raw.data.0.1 <- as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 0,idents = "stim")])
combined.raw.data.1 <- as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 0)])
combined.raw.data.2 <- as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 0)])

#combined.raw.data.[i] <- as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 1)])
#combined.raw.data.12 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 12)])
#combined.raw.data.1 <- as.matrix(GetAssayData(combined, slot = "counts"))
x <- AverageExpression(test.combined,assays = "RNA",add.ident = "stim", slot = "data",use.scale = FALSE, use.counts = FALSE)$RNA

#}######## CAR data 
avg.combined.cells <- (AverageExpression(combined, verbose = FALSE)$RNA) 
avg.combined.cells$gene <- rownames(avg.combined.cells)

CAR_FP <- FeaturePlot(combined, features = c('Cyp2b10','Nr1i3'), reduction = "umap", order = TRUE,split.by = "stim", blend = TRUE,sort.cell = TRUE, max.cutoff = 1, min.cutoff = 0, pt.size = 0.5, repel = TRUE)
CAR_DOT_NR <- DotPlot(combined, features = 'Nr1i3', col.min = 0)

Cyp2b10_FP <- FeaturePlot(combined, features = 'Cyp2b10', reduction = "umap", min.cutoff = 0)
########### tSNE #################################
combined <- NormalizeData(object = combined)
combined <- FindVariableFeatures(combined, selection.method = "vst", nfeatures = 2000)

KO.cells <- subset(combined, idents = c("0","1","12"))
Idents(KO.cells) <- "stim"



### to avergae out the matrix from KO cells 
raw.data.0 <- as.matrix(GetAssayData(combined, slot = c("counts","data"))[, WhichCells(combined, ident = 0)])
raw.data.1 <- as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 1)])
raw.data.2 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 2)])
raw.data.3 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 3)])
raw.data.4 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 4)])
raw.data.5 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 5)])
raw.data.6 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 6)])
raw.data.7 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 7)])
raw.data.8 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 8)])
raw.data.9 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 9)])
raw.data.10 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 10)])
raw.data.11 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 11)])
raw.data.12 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 12)])


TPMcount0<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 0)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 0)]))

TPMcount1<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 1)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 1)]))

TPMcount2<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 2)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 2)]))

TPMcount3<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 3)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 3)]))

TPMcount4<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 4)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 4)]))

TPMcount5<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 5)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 5)]))

TPMcount6<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 6)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 6)]))

TPMcount7<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 7)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 7)]))

TPMcount8<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 8)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 8)]))

TPMcount9<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 9)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 9)]))

TPMcount10<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 10)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 10)]))

TPMcount11<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 11)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 11)]))

TPMcount12<- cbind(as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 12)]), as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 12)]))




write.csv(TPMcount0, "CountResult/counts.TPMcount0.csv")
write.csv(TPMcount1, "CountResult/counts.TPMcount1.csv")
write.csv(TPMcount2, "CountResult/counts.TPMcount2.csv")
write.csv(TPMcount3, "CountResult/counts.TPMcount3.csv")
write.csv(TPMcount4, "CountResult/counts.TPMcount4.csv")
write.csv(TPMcount5, "CountResult/counts.TPMcount5.csv")
write.csv(TPMcount6, "CountResult/counts.TPMcount6.csv")
write.csv(TPMcount7, "CountResult/counts.TPMcount7.csv")
write.csv(TPMcount8, "CountResult/counts.TPMcount8.csv")
write.csv(TPMcount9, "CountResult/counts.TPMcount9.csv")
write.csv(TPMcount10, "CountResult/counts.TPMcount10.csv")
write.csv(TPMcount11, "CountResult/counts.TPMcount11.csv")
write.csv(TPMcount12, "CountResult/counts.TPMcount12.csv")



#avg.KO.cells <- log1p(AverageExpression(KO.cells, verbose = FALSE)$RNA)  #original code log transformed
avg.KO.cells <- (AverageExpression(KO.cells, verbose = FALSE)$RNA) 
avg.KO.cells$gene <- rownames(avg.KO.cells)

genes.to.label= ("ncRNA-inter-chr7-5998")
#genes.to.label = c("ISG15", "LY6E", "IFI6", "ISG20", "MX1", "IFIT2", "IFIT1", "CXCL10", "CCL8")
p1 <- ggplot(avg.KO.cells, aes(CTRL, STIM)) + geom_point() + ggtitle("CD4 Naive T Cells")
p1 <- LabelPoints(plot = p1, points = genes.to.label, repel = TRUE)
p2 <- ggplot(avg.cd14.mono, aes(CTRL, STIM)) + geom_point() + ggtitle("CD14 Monocytes")
p2 <- LabelPoints(plot = p2, points = genes.to.label, repel = TRUE)
plot_grid(p1, p2)

KO.cells <- RunUMAP(KO.cells, reduction = "pca", dims = 1:20 )
KO.cells <- FindNeighbors(KO.cells, reduction = "pca", dims = 1:20)
KO.cells <- FindClusters(KO.cells, resolution = 0.5 )   
KO.cells <- RunTSNE(KO.cells, reduction = "pca", dims = 1:20)
 

Hep.cells <- RunUMAP(Hep.cells, reduction = "pca", dims = 1:20 )
Hep.cells <- FindNeighbors(Hep.cells, reduction = "pca", dims = 1:20)
Hep.cells <- FindClusters(Hep.cells, resolution = 0.5 )   
Hep.cells <- RunTSNE(Hep.cells, reduction = "pca", dims = 1:20)
 

   
 # Visualization
p1 <- UMAPPlot(KO.cells, reduction = "umap", group.by = "stim")
p2 <- UMAPPlot(KO.cells, reduction = "umap", group.by = "mouse.sex")
p3 <- UMAPPlot(KO.cells, reduction = "umap", label = TRUE)
p4 <- UMAPPlot(KO.cells, label=TRUE)
plot_grid(p1,p4) 
DimPlot(KO.cells, reduction = "umap", split.by = "stim")   


#hepatocyte cells

p5 <- UMAPPlot(Hep.cells, reduction = "umap", group.by = "stim")
p6 <- UMAPPlot(Hep.cells, reduction = "umap", group.by = "mouse.sex")
p7 <- UMAPPlot(Hep.cells, reduction = "umap", label = TRUE)
p8 <- UMAPPlot(Hep.cells, label=TRUE)
plot_grid(p5,p8) 
DimPlot(KO.cells, reduction = "umap", split.by = "stim")   


raw.data.KO.0 <- as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 0)])
raw.data.KO.1 <- as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 1)])
raw.data.KO.2 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 2)])
raw.data.KO.3 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 3)])
raw.data.KO.4 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 4)])
raw.data.KO.5 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 5)])


write.csv(raw.data.KO.0, "CountResult/Markers/raw.data.KO.0")
write.csv(raw.data.KO.1, "CountResult/Markers/raw.data.KO.1")
write.csv(raw.data.KO.2, "CountResult/Markers/raw.data.KO.2")
write.csv(raw.data.KO.3, "CountResult/Markers/raw.data.KO.3")
write.csv(raw.data.KO.4, "CountResult/Markers/raw.data.KO.4")
write.csv(raw.data.KO.5, "CountResult/Markers/raw.data.KO.5")



f1 <- FeaturePlot(KO.cells, features = c('ncRNA-inter-chr7-5998'),  reduction = "umap", split.by = "stim")
plot_grid(f1,p1,p4) 

DefaultAssay(KO.cells) <- "RNA"
KO.cells <- NormalizeData(KO.cells, verbose = FALSE)

plots <- VlnPlot(KO.cells, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","Cyp2e1","Cyp2f2"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)
CombinePlots(plots = plots, ncol = 1)

Three_five_six <- subset(KO.cells, idents = c("5","6"))

Three_five_six <- RunUMAP(Three_five_six, reduction = "pca", dims = 1:20 )
Three_five_six <- FindNeighbors(Three_five_six, reduction = "pca", dims = 1:20)
Three_five_six <- FindClusters(Three_five_six, resolution = 1 )   
Three_five_six <- RunTSNE(Three_five_six, reduction = "pca", dims = 1:20)

p1 <- UMAPPlot(Three_five_six, reduction = "umap", group.by = "stim")
p2 <- UMAPPlot(Three_five_six, reduction = "umap", group.by = "mouse.sex")
p3 <- UMAPPlot(Three_five_six, reduction = "umap", label = TRUE)
p4 <- UMAPPlot(Three_five_six, label=TRUE)
plot_grid(p1,p4) 
DimPlot(Three_five_six, reduction = "umap", split.by = "stim")   

f1 <- FeaturePlot(Three_five_six, features = c('ncRNA-inter-chr7-5998'),  reduction = "umap", split.by = "stim")


lnc5998 <- subset(combined, cells = lnc5998.cells, idents = "1")

lnc5998 <- RunUMAP(lnc5998, reduction = "pca", dims = 1:20 )
lnc5998 <- FindNeighbors(lnc5998, reduction = "pca", dims = 1:20)
lnc5998 <- FindClusters(lnc5998, resolution = 1 )   
lnc5998 <- RunTSNE(lnc5998, reduction = "pca", dims = 1:20)
    
 # Visualization
p1 <- UMAPPlot(lnc5998, reduction = "umap", group.by = "stim")
p2 <- UMAPPlot(lnc5998, reduction = "umap", group.by = "mouse.sex")
p3 <- UMAPPlot(lnc5998, reduction = "umap", label = TRUE)
p4 <- UMAPPlot(lnc5998, label=TRUE)
plot_grid(p1,p4) 
DimPlot(lnc5998, reduction = "umap", split.by = "stim")   

lnc5998.cells <- WhichCells(object = combined, expression = "ncRNA-inter-chr7-5998" > 1)
FeaturePlot(lnc5998, features = c("ncRNA-inter-chr7-5998"), split.by = "stim",  
+             cols = c("grey", "red"), cells = lnc5998.cells,min.cutoff = 0.5)


DefaultAssay(lnc5998) <- "RNA"
lnc5998 <- NormalizeData(lnc5998, verbose = FALSE)

plots <- VlnPlot(lnc5998, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","Cyp2e1","Cyp2f2"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)
CombinePlots(plots = plots, ncol = 1)

 

d <- dist(t(GetAssayData(KO.cells, slot = "scale.data")))
# Run the MDS procedure, k determines the number of dimensions
mds <- cmdscale(d = d, k = 2)
# cmdscale returns the cell embeddings, we first label the columns to ensure downstream
# consistency
colnames(mds) <- paste0("MDS_", 1:2)
# We will now store this as a custom dimensional reduction called 'mds'
KO.cells[["mds"]] <- CreateDimReducObject(embeddings = mds, key = "MDS_", assay = DefaultAssay(KO.cells))

# We can now use this as you would any other dimensional reduction in all downstream functions
DimPlot(KO.cells, reduction = "mds", pt.size = 0.5)

Find differential markers

KO.cells$celltype.stim <- paste(Idents(KO.cells), KO.cells$stim, sep = "_")
KO.cells$celltype <- Idents(KO.cells)
Idents(KO.cells) <- "celltype.stim"
response3 <- FindMarkers(KO.cells, ident.1 = c("1_G171B","0_G171B","2_G171B"), ident.2 = c("1_G171C", "0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE)
head(response3, n = 15)


#DE1: Compare cluster 0+1+12 (that expressed lnc5998) with Other hepatocyte clusters (2+5+8)
#DE2: compare cluster 1 (showed major effects in the KD) vs Cluster 0 (that showed little KD)
#DE3: For KO.cells that formed five subcluster, compare clusters 3+4+5+1 vs 2+0
#DE4: for KO.cells that formed five clusters. Comapre cluster 4 vs. 2

DE0112.258 <- FindMarkers(combined, ident.1 = c("0","1","12" ), ident.2 = c("2","5","8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DE0112.All <- FindMarkers(combined, ident.1 = c("0","1","12" ), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DE1.2 <- FindMarkers(combined, ident.1 = c("1" ), ident.2 = c("2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DE1.5 <- FindMarkers(combined, ident.1 = c("1" ), ident.2 = c("5"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DE1.8 <- FindMarkers(combined, ident.1 = c("1" ), ident.2 = c("8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)


DE2.5 <- FindMarkers(combined, ident.1 = c("2" ), ident.2 = c("5"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DE2.8 <- FindMarkers(combined, ident.1 = c("2" ), ident.2 = c("8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DE5.8 <- FindMarkers(combined, ident.1 = c("5" ), ident.2 = c("8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)


DEK4351.20 <- FindMarkers(KO.cells, ident.1 = c("3","4","5","1" ), ident.2 = c("2","0"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)

DEK4.3 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "3",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK4.2 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "2",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK4.0 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "0",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK4.1 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "1",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK4.5 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "5",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)



##### comaprison between G171B vs G171C for KO.cell clusters of 0+1+12 ######33

DEK4_C.B <- FindMarkers(KO.cells, ident.1 = "4_G171C", ident.2 = "4_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK3_C.B <- FindMarkers(KO.cells, ident.1 = "3_G171C", ident.2 = "3_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK1_C.B <- FindMarkers(KO.cells, ident.1 = "1_G171C", ident.2 = "1_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK2_C.B <- FindMarkers(KO.cells, ident.1 = "2_G171C", ident.2 = "2_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK0_C.B <- FindMarkers(KO.cells, ident.1 = "0_G171C", ident.2 = "0_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
DEK5_C.B <- FindMarkers(KO.cells, ident.1 = "5_G171C", ident.2 = "5_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)


################################### Write the results #########################
write.csv(DE0112.258, "CountResult/Markers/DE0112.258")
write.csv(DE0112.All, "CountResult/Markers/DE0112.All")
write.csv(DE1.2, "CountResult/Markers/DE1.2")
write.csv(DE1.5, "CountResult/Markers/DE1.5")
write.csv(DE1.8, "CountResult/Markers/DE1.8")
write.csv(DE2.5, "CountResult/Markers/DE2.5")
write.csv(DE2.8, "CountResult/Markers/DE2.8")
write.csv(DE5.8, "CountResult/Markers/DE5.8")

write.csv(DEK4351.20, "CountResult/Markers/DEK4351.20")
write.csv(DEK4.3, "CountResult/Markers/DEK4.3")
write.csv(DEK4.2, "CountResult/Markers/DEK4.2")
write.csv(DEK4.0, "CountResult/Markers/DEK4.0")
write.csv(DEK4.1, "CountResult/Markers/DEK4.1")
write.csv(DEK4.5, "CountResult/Markers/DEK4.5")


write.csv(DEK4_C.B, "CountResult/Markers/DEK4_C.B")
write.csv(DEK3_C.B, "CountResult/Markers/DEK3_C.B")
write.csv(DEK1_C.B, "CountResult/Markers/DEK1_C.B")
write.csv(DEK2_C.B, "CountResult/Markers/DEK2_C.B")
write.csv(DEK0_C.B, "CountResult/Markers/DEK0_C.B")
write.csv(DEK5_C.B, "CountResult/Markers/DEK5_C.B")




combined$celltype.stim <- paste(Idents(combined), combined$stim, sep = "_")
combined$celltype <- Idents(combined)
Idents(combined) <- "celltype.stim"


test.combined$celltype.stim <- paste(Idents(test.combined), test.combined$stim, sep = "_")
test.combined$celltype <- Idents(test.combined)
Idents(test.combined) <- "celltype.stim"




Combined_G171B_vs_G171C <- FindMarkers(combined, ident.1 = c("0_G171B","1_G171B","2_G171B","3_G171B","4_G171B","5_G171B","6_G171B","7_G171B","8_G171B","9_G171B","10_G171B","11_G171B","12_G171B" ), ident.2 = c("0_G171C","1_G171C","2_G171C","3_G171C","4_G171C","5_G171C","6_G171C","7_G171C","8_G171C","9_G171C","10_G171C","11_G171C","12_G171C" ), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)


Combined_G171C_vs_G171B_Clust1 <- FindMarkers(combined, ident.1 = c("1_G171C"), ident.2 = c("1_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust0 <- FindMarkers(combined, ident.1 = c("0_G171C"), ident.2 = c("0_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust12 <- FindMarkers(combined, ident.1 = c("12_G171C"), ident.2 = c("12_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust2 <- FindMarkers(combined, ident.1 = c("2_G171C"), ident.2 = c("2_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust5 <- FindMarkers(combined, ident.1 = c("5_G171C"), ident.2 = c("5_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust8 <- FindMarkers(combined, ident.1 = c("8_G171C"), ident.2 = c("8_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)


Combined_G171C_vs_G171B_Clust3 <- FindMarkers(combined, ident.1 = c("3_G171C"), ident.2 = c("3_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust4 <- FindMarkers(combined, ident.1 = c("4_G171C"), ident.2 = c("4_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust6 <- FindMarkers(combined, ident.1 = c("6_G171C"), ident.2 = c("6_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust7 <- FindMarkers(combined, ident.1 = c("7_G171C"), ident.2 = c("7_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust9 <- FindMarkers(combined, ident.1 = c("9_G171C"), ident.2 = c("9_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust10 <- FindMarkers(combined, ident.1 = c("10_G171C"), ident.2 = c("10_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)
Combined_G171C_vs_G171B_Clust11 <- FindMarkers(combined, ident.1 = c("11_G171C"), ident.2 = c("11_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE)





write.csv(Combined_G171C_vs_G171B_Clust1, "CountResult/Markers/Combined_G171C_vs_G171B_Clust1")
write.csv(Combined_G171C_vs_G171B_Clust0, "CountResult/Markers/Combined_G171C_vs_G171B_Clust0")
write.csv(Combined_G171C_vs_G171B_Clust12, "CountResult/Markers/Combined_G171C_vs_G171B_Clust12")
write.csv(Combined_G171C_vs_G171B_Clust2, "CountResult/Markers/Combined_G171C_vs_G171B_Clust2")
write.csv(Combined_G171C_vs_G171B_Clust5, "CountResult/Markers/Combined_G171C_vs_G171B_Clust5")
write.csv(Combined_G171C_vs_G171B_Clust8, "CountResult/Markers/Combined_G171C_vs_G171B_Clust8")

write.csv(Combined_G171C_vs_G171B_Clust3, "CountResult/Markers/Combined_G171C_vs_G171B_Clust3")
write.csv(Combined_G171C_vs_G171B_Clust4, "CountResult/Markers/Combined_G171C_vs_G171B_Clust4")
write.csv(Combined_G171C_vs_G171B_Clust6, "CountResult/Markers/Combined_G171C_vs_G171B_Clust6")
write.csv(Combined_G171C_vs_G171B_Clust7, "CountResult/Markers/Combined_G171C_vs_G171B_Clust7")
write.csv(Combined_G171C_vs_G171B_Clust9, "CountResult/Markers/Combined_G171C_vs_G171B_Clust9")
write.csv(Combined_G171C_vs_G171B_Clust10, "CountResult/Markers/Combined_G171C_vs_G171B_Clust10")
write.csv(Combined_G171C_vs_G171B_Clust11, "CountResult/Markers/Combined_G171C_vs_G171B_Clust11")





PC_vs_PP_G171B <- FindMarkers(KO.cells, ident.1 = c("4_G171B","3_G171B"), ident.2 = c("0_G171B","2_G171B"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE)
head(PC_vs_PP_G171B, n = 15)


PC_vs_PP_G171C <- FindMarkers(KO.cells, ident.1 = c("4_G171C","3_G171C"), ident.2 = c("0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE)
head(PC_vs_PP_G171C, n = 15)


PC_vs_PP_G171BC <- FindMarkers(KO.cells, ident.1 = c("4_G171B","3_G171B","4_G171C","3_G171C"), ident.2 = c("0_G171B","2_G171B","0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE)
head(PC_vs_PP_G171C, n = 15)


lnc5998_KO_DE_1 <- FindMarkers(KO.cells, ident.1 = c("4_G171B","3_G171B","5_G171B"), ident.2 = c("4_G171C","3_G171C","5_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE)
head(lnc5998_KO_DE, n = 15)


lnc5998_KO_DE_2 <- FindMarkers(KO.cells, ident.1 = c("4_G171C","3_G171C","5_G171C"), ident.2 =c("4_G171B","3_G171B","5_G171B") , verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE)
head(lnc5998_KO_DE, n = 15)

cell.type.genes <- (PC_vs_PP_G171BC[1]) # Takes all the unique cell type specific genes
GOterms = topGOterms(fg.genes = cell.type.genes, bg.genes = rownames(KO.cells@assays$RNA@dataKO.cells@assays$RNA@data), organism = "Mouse")

cell.type.genes <- (PC_vs_PP_G171BC[1]) # Takes all the unique cell type specific genes
GOterms = topGOterms(fg.genes = rownames(cell.type.genes), bg.genes = rownames(KO.cells@assays$RNA@data), organism = "Mouse")
 

AvergeExpression2 <- function (object, assays = NULL, features = NULL, return.seurat = FALSE, 
          add.ident = NULL, slot = "data", use.scale = FALSE, use.counts = FALSE, 
          verbose = TRUE, ...) 
{
    
    fxn.average <- switch(EXPR = slot, data = function(x) {
        return(mean(x = x))
    }, mean)
    object.assays <- FilterObjects(object = object, classes.keep = "Assay")
    assays <- assays %||% object.assays
    ident.orig <- Idents(object = object)
    orig.levels <- levels(x = Idents(object = object))
    ident.new <- c()
    if (!all(assays %in% object.assays)) {
        assays <- assays[assays %in% object.assays]
        if (length(assays) == 0) {
            stop("None of the requested assays are present in the object")
        }
        else {
            warning("Requested assays that do not exist in object. Proceeding with existing assays only.")
        }
    }
    if (!is.null(x = add.ident)) {
        new.data <- FetchData(object = object, vars = add.ident)
        new.ident <- paste(Idents(object)[rownames(x = new.data)], 
                           new.data[, 1], sep = "_")
        Idents(object, cells = rownames(new.data)) <- new.ident
    }
    data.return <- list()
    for (i in 1:length(x = assays)) {
        data.use <- GetAssayData(object = object, assay = assays[i], 
                                 slot = slot)
        features.assay <- features
        if (length(x = intersect(x = features, y = rownames(x = data.use))) < 
            1) {
            features.assay <- rownames(x = data.use)
        }
        data.all <- data.frame(row.names = features.assay)
        for (j in levels(x = Idents(object))) {
            temp.cells <- WhichCells(object = object, idents = j)
            features.assay <- unique(x = intersect(x = features.assay, 
                                                   y = rownames(x = data.use)))
            if (length(x = temp.cells) == 1) {
                data.temp <- (data.use[features.assay, temp.cells])
                if (slot == "data") {
                    data.temp <-  data.temp
                }
            }
            if (length(x = temp.cells) > 1) {
                data.temp <- apply(X = data.use[features.assay, 
                                                temp.cells, drop = FALSE], MARGIN = 1, FUN = fxn.average)
            }
            data.all <- cbind(data.all, data.temp)
            colnames(x = data.all)[ncol(x = data.all)] <- j
            if (verbose) {
                message(paste("Finished averaging", assays[i], 
                              "for cluster", j))
            }
            if (i == 1) {
                ident.new <- c(ident.new, as.character(x = ident.orig[temp.cells[1]]))
            }
        }
        names(x = ident.new) <- levels(x = Idents(object))
        data.return[[i]] <- data.all
        names(x = data.return)[i] <- assays[[i]]
    }
    if (return.seurat) {
        toRet <- CreateSeuratObject(counts = data.return[[1]], 
                                    project = "Average", assay = names(x = data.return)[1], 
                                    ...)
        if (length(x = data.return) > 1) {
            for (i in 2:length(x = data.return)) {
                toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = data.return[[i]])
            }
        }
        if (DefaultAssay(object = object) %in% names(x = data.return)) {
            DefaultAssay(object = toRet) <- DefaultAssay(object = object)
        }
        Idents(toRet, cells = colnames(x = toRet)) <- ident.new[colnames(x = toRet)]
        Idents(object = toRet) <- factor(x = Idents(object = toRet), 
                                         levels = as.character(x = orig.levels), ordered = TRUE)
        toRet <- NormalizeData(object = toRet, verbose = verbose)
        toRet <- ScaleData(object = toRet, verbose = verbose)
        return(toRet)
    }
    else {
        return(data.return)
    }
}

Find differential expression markers

combined.markers <- FindAllMarkers(object = combined, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
combined.markers %>% group_by(cluster) %>% top_n(2, avg_logFC)

KO.markers <- FindAllMarkers(object = KO.cells, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

response3 <- FindMarkers(combined, ident.1 = c("1_G171B","0_G171B","2_G171B"), ident.2 = c("1_G171C", "0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE)
head(response3, n = 15)

Visualize top genes in principal components

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = tiss1)

Choose the number of principal components to use.

# Set number of principal components. 
n.pcs = 10

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale. Higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

# Set resolution 
res.used <- 4
tiss1 <- FindClusters(object = tiss1, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)

We use TSNE solely to visualize the data.

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss1 <- RunTSNE(object = tiss1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)

TSNEPlot(object = tiss1, do.label = T, pt.size = 1.2, label.size = 4)

Compare to previous annotations

previous_annotation = read.csv("/Users/kkarri/Documents/Lab/Single_cell_project/dropseq/Liver_droplet_annotation.csv", stringsAsFactors = FALSE)
cols = c('free_annotation', 'cell_ontology_class')
    for (col in cols){
      previous_col = paste0('previous_', col)
      tiss1@meta.data[, previous_col] <- "NA"
      tiss1@meta.data[as.character(previous_annotation$X), previous_col] <- previous_annotation[, col]
      print(table(tiss1@meta.data[, previous_col]))
      print(table(tiss1@meta.data[, previous_col], tiss@ident))
      
    }
    
tiss1 = compare_previous_annotation(tiss1, tissue_of_interest, "droplet")
TSNEPlot(object = tiss1, do.return = TRUE, group.by = "previous_cell_ontology_class")
table(tiss1@meta.data[, "previous_cell_ontology_class"], tiss@ident)

tiss1 = compare_previous_annotation(tiss1, tissue_of_interest, "droplet")
TSNEPlot(object = tiss1, do.return = TRUE, group.by = "previous_cell_ontology_class")
table(tiss1@meta.data[, "previous_cell_ontology_class"], tiss1@ident)

TSNEPlot(tiss1, group.by="mouse.sex")
TSNEPlot(tiss1, group.by="mouse.id")

Significant genes:

hepatocyte: Alb, Ttr, Apoa1, and Serpina1c pericentral: Cyp2e1, Glul, Oat, Gulo midlobular: Ass1, Hamp, Gstp1, Ubb periportal: Cyp2f2, Pck1, Hal, Cdh1

endothelial cells: Pecam1, Nrp1, Kdr+ and Oit3+ Kuppfer cells: Emr1, Clec4f, Cd68, Irf7 NK/NKT cells: Zap70, Il2rb, Nkg7, Cxcr6, Klr1c, Gzma B cells: Cd79a, Cd79b, Cd74 and Cd19 Immune cells: Ptprc

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest. The radius shows you the percent of cells in that cluster with at least one read sequenced from that gene. The color level indicates the average Z-score of gene expression for cells in that cluster, where the scaling is done over taken over all cells in the sample.

We have various immune cell types in the last cluster

Using the markers above, we can confidentaly label many of the clusters:

19: endothelial cells 20: bile duct epithelial cells 21: immune cells rest are hepatocytes

We will add those cell_ontology_classes to the dataset.

tiss1 <- StashIdent(object = tiss1, save.name = "cluster.ids")
cluster.ids <- c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20)
free_annotation <- c(
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  NA,
  "bile duct epithelial cells",
  "endothelial cell of hepatic sinusoid",
  NA
  )
cell_ontology_class <- c(
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "duct epithelial cell",
  "endothelial cell of hepatic sinusoid",
  "hepatocyte")
tiss1 = stash_annotations(tiss1, cluster.ids, free_annotation, cell_ontology_class)

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = tiss1, do.return = TRUE, group.by = "channel")
TSNEPlot(object = tiss1, do.return = TRUE, group.by = "free_annotation")

Subcluster

Let’s drill down on the hepatocytes.

subtiss1 = SubsetData(tiss1, ident.use = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20))

subtiss1 <- subtiss1 %>% ScaleData() %>%
  FindVariableGenes(do.plot = FALSE, x.high.cutoff = Inf, y.cutoff = 0.5) %>%
  RunPCA(do.print = FALSE)

PCHeatmap(object = subtiss1, pc.use = 1:3, cells.use = 20, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)
PCElbowPlot(subtiss1)

sub.n.pcs = 8
sub.res.use = 0.5
subtiss1 <- subtiss1 %>% FindClusters(reduction.type = "pca", dims.use = 1:sub.n.pcs,
    resolution = sub.res.use, print.output = 0, save.SNN = TRUE, force.recalc = TRUE) %>%
    RunTSNE(dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=8)
TSNEPlot(object = subtiss1, do.label = T, pt.size = 1, label.size = 4)

BuildClusterTree(subtiss1)

From these genes, it appears that the clusters represent:

0: midlobular male 1: pericentral female 2: periportal female 3: periportal male 4: midlobular male 5: pericentral male 6: midlobular female 7: midlobular female

The multitude of clusters of each type correspond mostly to individual animals/sexes.

table(FetchData(subtiss1, c('mouse.sex','ident')) %>% droplevels())

sub.cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
sub.free_annotation <- c("periportal female", "midlobular male", "pericentral female", "periportal male", "midlobular male", "pericentral male", "midlobular female", "midlobular female")
sub.cell_ontology_class <- c("hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte")
subtiss1 = stash_annotations(subtiss1, sub.cluster.ids, sub.free_annotation, sub.cell_ontology_class)
tiss1 = stash_subtiss_in_tiss(tiss1, subtiss1)

Liver zonation markers

genes_zones = c('Cyp2e1', 'Glul', 'Oat', 'Gulo',
              'Ass1', 'Hamp', 'Gstp1', 'Ubb',
              'Cyp2f2', 'Pck1', 'Hal', 'Cdh1')

FeaturePlot(subtiss1,c(genes_zones),cols.use = c("grey", "red"), pt.size = 1, nCol = 4)

DotPlot(subtiss1,c(genes_zones), plot.legend = T, col.max = 2.5, do.return = T) + coord_flip()


TSNEPlot(object = subtiss1, do.label = T, pt.size = 1, label.size = 4, group.by="free_annotation")

TSNEPlot(object = tiss1, do.label = T, pt.size = 1, label.size = 4, group.by="free_annotation")

Find cluster markers for lncRNAs

MIN_LOGFOLD_CHANGE = 1 # set to minimum required average log fold change in gene expression.
MIN_PCT_CELLS_EXPR_GENE = 0.1

all.markers = FindAllMarkers(tiss1,
                             min.pct = MIN_PCT_CELLS_EXPR_GENE,
                             logfc.threshold = MIN_LOGFOLD_CHANGE,
                             only.pos = TRUE,
                             test.use="bimod") # likelihood ratio test
lnc_all_markers <- grep(pattern = "^ncRNA", x= rownames(all.markers), value = TRUE)
lnc_all_markers

#[1] "ncRNA_inter_chr10_92081" "ncRNA_intra_chr16_13383" "ncRNA_inter_chr17_13605" "ncRNA_inter_chr14_11815"
#[5] "ncRNA_inter_chr18_14344"

FeaturePlot(subtiss1,c(lnc_all_markers),cols.use = c("grey", "red"), pt.size = 1, nCol = 4)

######################### lncRNA markers- CELL TYPE MARKER ############
markers.hep <- FindMarkers(object = tiss1, ident.1 = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20), ident.2 = c(18,19),only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
lnc_markers_hep <- grep(pattern = "^ncRNA", x= rownames(markers.hep), value = TRUE)
lnc_markers_hep
FeaturePlot(tiss1,c(lnc_markers_hep),cols.use = c("grey", "red"), pt.size = 1, nCol = 4)
DotPlot(tiss1,lnc_markers_hep, plot.legend = T, col.max = 2.5, do.return = T) + coord_flip()
#[1] "ncRNA_as_chr11_9423"     "ncRNA_as_chr7_6166"      "ncRNA_inter_chr4_3295"   "ncRNA_inter_chr17_14026"
#[5] "ncRNA_inter_chr3_2915"   "ncRNA_inter_chr5_4547"   "ncRNA_inter_chr15_12684"


markers.hep.MAST <- FindMarkers(object = tiss1, ident.1 = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20), ident.2 = c(18,19),only.pos = TRUE, test.use = "MAST")
lnc_markers_hep_MAST_TABLE <- subset(markers.hep.MAST, grepl("^ncRNA", rownames(markers.hep.MAST)))
lnc_markers_hep_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.hep.MAST), value = TRUE)
lnc_markers_hep_MAST



markers.endo <- FindMarkers(object = tiss1, ident.1 = c(18,19),  only.pos = TRUE, min.pct = 0.25, thresh.use = 0.5)
lnc_markers_endo <- grep(pattern = "^ncRNA", x= rownames(markers.endo), value = TRUE)
lnc_markers_endo
FeaturePlot(tiss1,c(lnc_markers_endo),cols.use = c("grey", "red"), pt.size = 1, nCol = 4)
DotPlot(tiss1,lnc_markers_endo, plot.legend = T, col.max = 2.5, do.return = T) + coord_flip()


#"ncRNA_inter_chr15_12770", "ncRNA_inter_chr12_10817", "ncRNA_as_chr13_11451",


markers.endo.MAST <- FindMarkers(object = tiss1, ident.1 = 19, test.use = "MAST" ,only.pos = TRUE)
lnc_markers_endo_MAST_TABLE <- subset(markers.endo.MAST, grepl("^ncRNA", rownames(markers.endo.MAST)))
lnc_markers_endo_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.endo.MAST), value = TRUE)
lnc_markers_endo_MAST


################## lncRNA expression ########################3

# "ncRNA_inter_chr17_13605" , "ncRNA_intra_chr16_13383"

########## Periporal markers- zonation markers ############
markers.pc <- FindMarkers(object = subtiss1, ident.1 = c(2,5), 
                              only.pos = FALSE, min.pct = 0.001, thresh.use = 0.001, test.use = "bimod" )

markers.pc.MAST <- FindMarkers(object = subtiss1, ident.1 = c(2,5), ident.2 = c(0,3), test.use = "MAST" ,only.pos = TRUE)
lnc_markers_pc <- subset(markers.pc, grepl("^ncRNA", rownames(markers.pc)))
lnc_markers_pc <- grep(pattern = "^ncRNA", x= rownames(markers.pc), value = TRUE)
lnc_markers_pc 

markers.pc.MAST <- FindMarkers(object = subtiss1, ident.1 = c(2,5), ident.2 = c(0,3), test.use = "MAST" ,only.pos = TRUE)
lnc_markers_pc_MAST <- subset(markers.pc.MAST, grepl("^ncRNA", rownames(markers.pc.MAST)))
lnc_markers_pc_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.pc.MAST), value = TRUE)
lnc_markers_pc_MAST

DotPlot(tiss1, lnc_markers_pc, plot.legend = T, col.max = 2.5, do.return = T, group.by="free_annotation") + coord_flip()
FeaturePlot(subtiss1,c(lnc_markers_pc),cols.use = c("grey", "red"), pt.size = 1, nCol = 4)


############################### midlobular genes #############

markers.mid <- FindMarkers(object = subtiss1, ident.1 = c(1,4,6,7), 
                              only.pos = FALSE, min.pct = 0.001, thresh.use = 0.05)

lnc_markers_mid <- subset(markers.mid, grepl("^ncRNA", rownames(markers.mid)))
lnc_markers_mid <- grep(pattern = "^ncRNA", x= rownames(markers.mid), value = TRUE)
lnc_markers_mid
DotPlot(tiss1, lnc_markers_mid, plot.legend = T, col.max = 2.5, do.return = T, group.by="free_annotation") + coord_flip()


FeaturePlot(subtiss1,c(lnc_markers_mid),cols.use = c("grey", "red"), pt.size = 1, nCol = 4)



markers.mid.MAST <- FindMarkers(object = subtiss1, ident.1 = c(1,4,6,7),test.use = "MAST",only.pos = TRUE )

lnc_markers_mid_MAST_TABLE <- subset(markers.mid.MAST, grepl("^ncRNA", rownames(markers.mid.MAST)))
lnc_markers_mid_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.mid.MAST), value = TRUE)
lnc_markers_mid_MAST


#####3 periportalmarker genes############3

markers.pp <- FindMarkers(object = subtiss1, ident.1 = c(0,3),
                              only.pos = FALSE, min.pct = 0.001, thresh.use = 0.05)


lnc_markers_pp <- subset(markers.pp, grepl("^ncRNA", rownames(markers.pp)))
lnc_markers_pp <- grep(pattern = "^ncRNA", x= rownames(markers.pp), value = TRUE)
lnc_markers_pp

markers.pp.MAST <- FindMarkers(object = subtiss1, ident.1 = c(0,3), ident.2 = c(2,5),test.use = "MAST",only.pos = TRUE )

lnc_markers_pp_MAST_TABLE <- subset(markers.pp.MAST, grepl("^ncRNA", rownames(markers.pp.MAST)))
lnc_markers_pp_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.pp.MAST), value = TRUE)
lnc_markers_pp_MAST

FeaturePlot(subtiss1,c(lnc_markers_pp),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1)
DotPlot(tiss1, c(lnc_markers_pp,"Cyp2e1","Cyp2f2"), plot.legend = T, col.max = 2.5, do.return = T, group.by= "free_annotation") + coord_flip()


################## amle and female specific ############################

markers.female <- FindMarkers(object = subtiss1, ident.1 = c(0,2,6,7),
                              only.pos = TRUE, min.pct = 0.1, logfc.threshold = 1)

lnc_markers_female <- subset(markers.female, grepl("^ncRNA", rownames(markers.female)))
lnc_markers_female <- grep(pattern = "^ncRNA", x= rownames(markers.female), value = TRUE)
lnc_markers_female

FeaturePlot(subtiss1,c(lnc_markers_female),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1)
DotPlot(tiss1, c(lnc_markers_female,"Cyp2e1","Cyp2f2"), plot.legend = T, col.max = 2.5, do.return = T, group.by= "free_annotation") + coord_flip()



markers.male <- FindMarkers(object = subtiss1, ident.1 = c(1,3,4,5),
                              only.pos = TRUE, min.pct = 0.001, thresh.use = 0.05)

lnc_markers_male <- subset(markers.male, grepl("^ncRNA", rownames(markers.male)))
lnc_markers_male <- grep(pattern = "^ncRNA", x= rownames(markers.male), value = TRUE)
lnc_markers_male

FeaturePlot(subtiss1,c(lnc_markers_male),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1)
DotPlot(tiss1, c(lnc_markers_male,"Cyp2e1","Cyp2f2"), plot.legend = T, col.max = 2.5, do.return = T, group.by= "free_annotation") + coord_flip()


############################ Female zonate specific genes ###################################

markers.pericentral.female <- FindMarkers(object = tiss1, ident.1 = c(6,11,14,20), test.use = "MAST",
                            only.pos = TRUE, min.pct = 0.1, ident.2 = c(2,3,15,12,13,8,5,16), logfc.threshold = 1)

markers.periportal.female <- FindMarkers(object = tiss1, ident.1 = c(2,3,15),
                            only.pos = TRUE, min.pct = 0.1, ident.2 = c(6,11,14,20,12,13,8,5,16), logfc.threshold = 1)


markers.pericentral.male <- FindMarkers(object = tiss1, ident.1 = c(13,12), test.use = "MAST",
                            only.pos = TRUE, min.pct = 0.1, ident.2 = c(2,3,15,8,5,16,6,11,14,20), logfc.threshold = 1)


markers.periportal.male <- FindMarkers(object = tiss1, ident.1 = c(8,5,16), test.use = "MAST",
                            only.pos = TRUE, min.pct = 0.1, ident.2 = c(2,3,15,13,12,6,11,14,20), logfc.threshold = 1)




############### xeno-lncs CAR?RXR ##################

FeaturePlot(tiss1,c("ncRNA_inter_chr15_12684","ncRNA_inter_chr8_7430","ncRNA_inter_chr7_6222"),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1)






#######################################################################

markers.endo.2 <- FindMarkers(object = seurat_drop, logfc.threshold = 2,ident.1 = "Endothelial", 
                              only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

lnc.endo.2 <- grep(pattern = "^ncRNA", x= rownames(markers.endo.2), value = TRUE)
lnc.endo.2

Zonated lncRNAs

pp_zontaed <- c('ncRNA_inter_chr14_12016','ncRNA_as_chr19_15090','ncRNA_inter_chr10_9351','ncRNA_inter_chr16_13170',
'ncRNA_inter_chr3_2697','ncRNA_inter_chr1_274','ncRNA_as_chr6_5518','ncRNA_inter_chr14_12066','ncRNA_intra_chr12_10871',
'ncRNA_inter_chr16_13510','ncRNA_inter_chr3_2314','ncRNA_inter_chr10_9264,'ncRNA_inter_chr9_8122')

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = subtiss1, do.return = TRUE, group.by = "mouse.sex")

Final coloring

Color by cell ontology class on the original TSNE.

TSNEPlot(object = tiss1, do.return = TRUE, group.by = "cell_ontology_class")

Save the Robject for later

filename = here('00_data_ingest', '04_tiss1ue_robj_generated', 
                     paste0("droplet_", tiss1ue_of_interest, "refinedcells_seurat_tiss1.Robj"))
print(filename)
save(tiss1, file=filename)

# To reload a saved object
filename = here('00_data_ingest', '04_tiss1ue_robj_generated',
                      paste0("droplet_", tissue_of_interest, "seurat_smartdrop-integrated-8272019.Robj"))
load(file=filename)

Export the final metadata

save_annotation_csv(tiss1, tiss1ue_of_interest, "droplet")