---
title: "G185_Integration"
author: "KK"
date: "August 19, 2020"
output: html_document
---

```{r setup, include=FALSE}
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)
options(future.globals.maxSize= 891289600)
```

```{r}
raw.data1 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/dcas9_G185M3/outs/filtered_feature_bc_matrix")
G185M3 <- CreateSeuratObject(raw.data1)   # dropseq
G185M3@meta.data$samples  <- "dcas9"
G185M3 <-  subset(G185M3, subset = nFeature_RNA > 200 & nCount_RNA > 500)
# G185M3 <- NormalizeData(G185M3, verbose = FALSE)
# G185M3 <- FindVariableFeatures(G185M3, selection.method = "vst", nfeatures = 2000)
# G185M3 <- SCTransform(G185M3)
# 

raw.data2 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/lnc3779_G185M5/outs/filtered_feature_bc_matrix")
G185M5 <- CreateSeuratObject(raw.data2)   # dropseq
G185M5@meta.data$samples  <- "lnc3779"
G185M5 <-  subset(G185M5, subset = nFeature_RNA > 200 & nCount_RNA > 500)
# G185M5 <- NormalizeData(G185M5, verbose = FALSE)
# G185M5 <- FindVariableFeatures(G185M5, selection.method = "vst", nfeatures = 2000)
# G185M5 <- SCTransform(G185M5)

raw.data3 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/lnc5998_G185M4/outs/filtered_feature_bc_matrix")
G185M4 <- CreateSeuratObject(raw.data3)   # dropseq
G185M4@meta.data$samples  <- "lnc5998"
G185M4 <-  subset(G185M4, subset = nFeature_RNA > 200 & nCount_RNA > 500)
# G185M4 <- NormalizeData(G185M4, verbose = FALSE)
# G185M4 <- FindVariableFeatures(G185M4, selection.method = "vst", nfeatures = 2000)
# G185M4 <- SCTransform(G185M4)

ctrl.merge.object1 <- merge(G185M3,y=c(G185M5),add.cell.ids = c("dcas9","lnc3779"),project="G185M3M5")
ctrl.merge.object2 <- merge(G185M3,y=c(G185M4),add.cell.ids = c("dcas9","lnc5998"),project="G185M3M4")
ctrl.merge.object3 <- merge(G185M4,y=c(G185M5),add.cell.ids = c("lnc5998","lnc3779"),project="G185M4M5")




raw.data4 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/dcas9_G171B_PCG-newlncs_GeneBody_TACOTishaEnsmblNRLincs/outs/filtered_feature_bc_matrix")
G171B <- CreateSeuratObject(raw.data4)   # dropseq
G171B@meta.data$samples  <- "G171Bdcas9"
G171B <-  subset(G171B, subset = nFeature_RNA > 200 & nCount_RNA > 500)


raw.data5 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/lnc5998_G171C_PCG-newlncs_GeneBody_TACOTishaEnsmblNRLincs/outs/filtered_feature_bc_matrix")
G171C <- CreateSeuratObject(raw.data5)   # dropseq
G171C@meta.data$samples  <- "G171Clnc5998"
G171C 

ctrl.merge.object4 <- merge(G171B,y=c(G171C),add.cell.ids = c("G171Bdcas9","G171Clnc5998"),project="G171BC")


```

```{r}

load("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M3M5_SCTIntegration_PC11_res0.15_GB.Rdata")

object.list1.2 <- SplitObject(ctrl.merge.object2, split.by = "samples")
for (i in 1:length(object.list1.2)){
  object.list1.2[[i]] <- SCTransform(object.list1.2[[i]], verbose = TRUE)
  
}

ctrl.features1.2 <- SelectIntegrationFeatures(object.list = object.list1.2,dims=1:30, verbose = TRUE)
object.list1.2  <- PrepSCTIntegration(object.list = object.list1.2, anchor.features = ctrl.features1.2, 
    verbose = TRUE)

ctrl.anchors1.2 <- FindIntegrationAnchors(object.list = object.list1.2, normalization.method = "SCT", 
    anchor.features = ctrl.features1.2, verbose = FALSE)
ctrl_integrate1.2 <- IntegrateData(anchorset = ctrl.anchors1.2, normalization.method = "SCT", 
    verbose = TRUE)

#ctrl_integrate2 <- ScaleData(ctrl_integrate2)
ctrl_integrate1.2 <- RunPCA(ctrl_integrate1.2, npcs = 100)
ctrl_integrate1.2 <- RunUMAP(ctrl_integrate1.2, reduction = "pca", dims = 1:11)
ctrl_integrate1.2 <- FindNeighbors(ctrl_integrate1.2, reduction = "pca", dims = 1:11)
ctrl_integrate1.2 <- FindClusters(ctrl_integrate1.2 , resolution = 0.15 )   


ctrl_integrate1.2_p1.1 <- DimPlot(ctrl_integrate1.2, reduction = "umap",label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate1.2_p1.2 <- DimPlot(ctrl_integrate1.2, reduction = "umap",split.by = "samples", label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate1.2_p1.3 <- DimPlot(ctrl_integrate1.2, reduction = "umap",  group.by = "samples",label=TRUE, label.size=5, pt.size=0.5)
plot_grid(ctrl_integrate1.2_p1.1,ctrl_integrate1.2_p1.2,ctrl_integrate1.2_p1.3,ncol = 3)

DefaultAssay(ctrl_integrate1.2) <- "SCT"
ctrl_integrate1.2_f1.1 <-  FeaturePlot(ctrl_integrate1.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples",  cols = c("grey", "red"),order=TRUE)

ctrl_integrate1.2_v1.1 <- VlnPlot(ctrl_integrate1.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples", group.by ="seurat_clusters", pt.size = 0.1, combine = FALSE)

###### We use RNA aasay to make dotpots 
DefaultAssay(ctrl_integrate1.2) <- "RNA"
ctrl_integrate1.2 <- NormalizeData(ctrl_integrate1.2)
ctrl_integrate1.2_d1.1 <-DotPlot(ctrl_integrate1.2, features = MS_list2)+RotatedAxis()
ctrl_integrate1.2_d1.2 <-DotPlot(ctrl_integrate1.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"),split.by = "samples", cols=c("red","blue")) +RotatedAxis()  

ctrl_integrate1.2_f1.2 <-  FeaturePlot(ctrl_integrate1.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples",  
            cols = c("grey", "red"),order=TRUE)

ctrl_integrate1.2_v1.2 <- VlnPlot(ctrl_integrate1.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples", group.by ="seurat_clusters", pt.size = 0.1, combine = TRUE)


MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1','Cyp2f2','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')

table(FetchData(ctrl_integrate_markers2.1, c('samples','ident')))


##Find Conserved Markers 
ctrl_integrate_cons_markers1.2.1 <- FindConservedMarkers(ctrl_integrate1.2, ident.1 = "1", grouping.var = "samples", verbose = FALSE)
ctrl_integrate_cons_markers1.2.0 <- FindConservedMarkers(ctrl_integrate1.2, ident.1 = "0", grouping.var = "samples", verbose = FALSE)

ctrl_integrate_cons_markers1.2.01 <- FindConservedMarkers(ctrl_integrate1.2, ident.1 = c("0","1"), grouping.var = "samples", verbose = FALSE)


write.csv(ctrl_integrate_cons_markers1.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_Integrate_ConservedMark_Cluster1")

write.csv(ctrl_integrate_cons_markers1.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_Integrate_ConservedMark_Cluster0")

write.csv(ctrl_integrate_cons_markers1.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_Integrate_ConservedMark_Cluster01")

### DE analysis#

ctrl_integrate1.2.1 <- ctrl_integrate1.2
ctrl_integrate1.2.1$celltype.stim <- paste(Idents(ctrl_integrate1.2.1), ctrl_integrate1.2.1$samples, sep = "_")
ctrl_integrate1.2.1$celltype <- Idents(ctrl_integrate1.2.1)
Idents(ctrl_integrate1.2.1) <- "celltype.stim"

ctrl_integrate_markers1.2.0 <- FindMarkers(ctrl_integrate1.2.1, ident.1 = "0_dcas9", ident.2 = "0_lnc5998", verbose = FALSE)

ctrl_integrate_markers1.2.1 <- FindMarkers(ctrl_integrate1.2.1, ident.1 = "1_dcas9", ident.2 = "1_lnc5998", verbose = FALSE)

ctrl_integrate_markers1.2.01 <- FindMarkers(ctrl_integrate1.2.1, ident.1 = c("0_dcas9","1_dcas9"), ident.2 =c("0_lnc5998","1_lnc5998"), verbose = FALSE)

write.csv(ctrl_integrate_markers1.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_DE0_Marker")

write.csv(ctrl_integrate_markers1.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_DE1_Marker")

write.csv(ctrl_integrate_markers1.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_DE01_Marker")

head(ctrl_integrate_markers2.1, n = 15)

save(ctrl_integrate1.2,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M3M5_SCTIntegration_PC11_res0.15_GB.Rdata")

```

```{r}
ctrl_integrate1.2.0_dcas9 <- subset(ctrl_integrate1.2.1,idents = "0_dcas9")
ctrl_integrate1.2.0_dcas9 <- NormalizeData(ctrl_integrate1.2.0_dcas9,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate1.2.0_dcas9_avgCPM <- rowMeans(as.data.frame(ctrl_integrate1.2.0_dcas9@assays$RNA@data))
ctrl_integrate1.2.0_dcas9.count <- as.data.frame(GetAssayData(ctrl_integrate1.2.0_dcas9, slot = "counts"))
ctrl_integrate1.2.0_dcas9.nUMI <- as.data.frame((rowSums(ctrl_integrate1.2.0_dcas9.count!=0)))
ctrl_integrate1.2.0_dcas9.UMISum <- as.data.frame((rowSums(ctrl_integrate1.2.0_dcas9.count)))
ctrl_integrate1.2.0_dcas9.pct <- as.data.frame((rowSums(ctrl_integrate1.2.0_dcas9.count!=0)/ncol(ctrl_integrate1.2.0_dcas9.count))*100)
x1 <- cbind(ctrl_integrate1.2.0_dcas9.nUMI,ctrl_integrate1.2.0_dcas9.UMISum,ctrl_integrate1.2.0_dcas9.pct,ctrl_integrate1.2.0_dcas9_avgCPM)

ctrl_integrate1.2.1_dcas9 <- subset(ctrl_integrate1.2.1,idents = "1_dcas9")
ctrl_integrate1.2.1_dcas9 <- NormalizeData(ctrl_integrate1.2.1_dcas9,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate1.2.1_dcas9_avgCPM <- rowMeans(as.data.frame(ctrl_integrate1.2.1_dcas9@assays$RNA@data))
ctrl_integrate1.2.1_dcas9.count <- as.data.frame(GetAssayData(ctrl_integrate1.2.1_dcas9, slot = "counts"))
ctrl_integrate1.2.1_dcas9.nUMI <- as.data.frame((rowSums(ctrl_integrate1.2.1_dcas9.count!=0)))
ctrl_integrate1.2.1_dcas9.UMISum <- as.data.frame((rowSums(ctrl_integrate1.2.1_dcas9.count)))
ctrl_integrate1.2.1_dcas9.pct <- as.data.frame((rowSums(ctrl_integrate1.2.1_dcas9.count!=0)/ncol(ctrl_integrate1.2.1_dcas9.count))*100)
x2 <- cbind(ctrl_integrate1.2.1_dcas9.nUMI,ctrl_integrate1.2.1_dcas9.UMISum,ctrl_integrate1.2.1_dcas9.pct,ctrl_integrate1.2.1_dcas9_avgCPM)

ctrl_integrate1.2.0_lnc3779 <- subset(ctrl_integrate1.2.1,idents = "0_lnc3779")
ctrl_integrate1.2.0_lnc3779 <- NormalizeData(ctrl_integrate1.2.0_lnc3779,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate1.2.0_lnc3779_avgCPM <- rowMeans(as.data.frame(ctrl_integrate1.2.0_lnc3779@assays$RNA@data))
ctrl_integrate1.2.0_lnc3779.count <- as.data.frame(GetAssayData(ctrl_integrate1.2.0_lnc3779, slot = "counts"))
ctrl_integrate1.2.0_lnc3779.nUMI <- as.data.frame((rowSums(ctrl_integrate1.2.0_lnc3779.count!=0)))
ctrl_integrate1.2.0_lnc3779.UMISum <- as.data.frame((rowSums(ctrl_integrate1.2.0_lnc3779.count)))
ctrl_integrate1.2.0_lnc3779.pct <- as.data.frame((rowSums(ctrl_integrate1.2.0_lnc3779.count!=0)/ncol(ctrl_integrate1.2.0_lnc3779.count))*100)
x3 <- cbind(ctrl_integrate1.2.0_lnc3779.nUMI,ctrl_integrate1.2.0_lnc3779.UMISum,ctrl_integrate1.2.0_lnc3779.pct,ctrl_integrate1.2.0_lnc3779_avgCPM)

ctrl_integrate1.2.1_lnc3779 <- subset(ctrl_integrate1.2.1,idents = "1_lnc3779")
ctrl_integrate1.2.1_lnc3779 <- NormalizeData(ctrl_integrate1.2.1_lnc3779,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate1.2.1_lnc3779_avgCPM <- rowMeans(as.data.frame(ctrl_integrate1.2.1_lnc3779@assays$RNA@data))
ctrl_integrate1.2.1_lnc3779.count <- as.data.frame(GetAssayData(ctrl_integrate1.2.1_lnc3779, slot = "counts"))
ctrl_integrate1.2.1_lnc3779.nUMI <- as.data.frame((rowSums(ctrl_integrate1.2.1_lnc3779.count!=0)))
ctrl_integrate1.2.1_lnc3779.UMISum <- as.data.frame((rowSums(ctrl_integrate1.2.1_lnc3779.count)))
ctrl_integrate1.2.1_lnc3779.pct <- as.data.frame((rowSums(ctrl_integrate1.2.1_lnc3779.count!=0)/ncol(ctrl_integrate1.2.1_lnc3779.count))*100)
x4 <- cbind(ctrl_integrate1.2.1_lnc3779.nUMI,ctrl_integrate1.2.1_lnc3779.UMISum,ctrl_integrate1.2.1_lnc3779.pct,ctrl_integrate1.2.1_lnc3779_avgCPM)
y <- cbind(x1,x2,x3,x4)

write.csv(y,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M5/G185M3M5_Data")

```




```{r}

######################### SC transform integration method (USE THIS) ################################
##################3standardworkflow for integration  G185M3M4 ####################
object.list2.2 <- SplitObject(ctrl.merge.object2, split.by = "samples")
for (i in 1:length(object.list2.2)){
  object.list2.2[[i]] <- SCTransform(object.list2.2[[i]], verbose = TRUE)
  
}

ctrl.features2.2 <- SelectIntegrationFeatures(object.list = object.list2.2,dims=1:30, verbose = TRUE)
object.list2.2  <- PrepSCTIntegration(object.list = object.list2.2, anchor.features = ctrl.features2.2, 
    verbose = TRUE)

ctrl.anchors2.2 <- FindIntegrationAnchors(object.list = object.list2.2, normalization.method = "SCT", 
    anchor.features = ctrl.features2.2, verbose = FALSE)
ctrl_integrate2.2 <- IntegrateData(anchorset = ctrl.anchors2.2, normalization.method = "SCT", 
    verbose = TRUE)

#ctrl_integrate2 <- ScaleData(ctrl_integrate2)
ctrl_integrate2.2 <- RunPCA(ctrl_integrate2.2, npcs = 100)
ctrl_integrate2.2 <- RunUMAP(ctrl_integrate2.2, reduction = "pca", dims = 1:11)
ctrl_integrate2.2 <- FindNeighbors(ctrl_integrate2.2, reduction = "pca", dims = 1:11)
ctrl_integrate2.2 <- FindClusters(ctrl_integrate2.2 , resolution = 0.15 )   


ctrl_integrate2.2_p1.1 <- DimPlot(ctrl_integrate2.2, reduction = "umap",label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate2.2_p1.2 <- DimPlot(ctrl_integrate2.2, reduction = "umap",split.by = "samples", label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate2.2_p1.3 <- DimPlot(ctrl_integrate2.2, reduction = "umap",  group.by = "samples",label=TRUE, label.size=5, pt.size=0.5)
plot_grid(ctrl_integrate2.2_p1.1,ctrl_integrate2.2_p1.2,ctrl_integrate2.2_p1.3,ncol = 3)

DefaultAssay(ctrl_integrate2.2) <- "SCT"
ctrl_integrate2.2_f1.1 <-  FeaturePlot(ctrl_integrate2.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples",  cols = c("grey", "red"),order=TRUE)

ctrl_integrate2.2_v1.1 <- VlnPlot(ctrl_integrate2.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples", group.by ="seurat_clusters", pt.size = 0.1, combine = FALSE)

###### We use RNA aasay to make dotpots 
DefaultAssay(ctrl_integrate2.2) <- "RNA"
ctrl_integrate2.2 <- NormalizeData(ctrl_integrate2.2)
ctrl_integrate2.2_d1.1 <-DotPlot(ctrl_integrate2.2, features = MS_list2)+RotatedAxis()
ctrl_integrate2.2_d1.2 <-DotPlot(ctrl_integrate2.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"),split.by = "samples", cols=c("red","blue")) +RotatedAxis()  

ctrl_integrate2.2_f1.2 <-  FeaturePlot(ctrl_integrate2.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples",  
            cols = c("grey", "red"),order=TRUE)

ctrl_integrate2.2_v1.2 <- VlnPlot(ctrl_integrate2.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples", group.by ="seurat_clusters", pt.size = 0.1, combine = TRUE)


MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1','Cyp2f2','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')

table(FetchData(ctrl_integrate_markers2.1, c('samples','ident')))


##Find Conserved Markers 
ctrl_integrate_cons_markers2.2.1 <- FindConservedMarkers(ctrl_integrate2.2, ident.1 = "1", grouping.var = "samples", verbose = FALSE)
ctrl_integrate_cons_markers2.2.0 <- FindConservedMarkers(ctrl_integrate2.2, ident.1 = "0", grouping.var = "samples", verbose = FALSE)

ctrl_integrate_cons_markers2.2.01 <- FindConservedMarkers(ctrl_integrate2.2, ident.1 = c("0","1"), grouping.var = "samples", verbose = FALSE)


write.csv(ctrl_integrate_cons_markers2.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_Integrate_ConservedMark_Cluster1")

write.csv(ctrl_integrate_cons_markers2.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_Integrate_ConservedMark_Cluster0")

write.csv(ctrl_integrate_cons_markers2.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_Integrate_ConservedMark_Cluster01")

### DE analysis#

ctrl_integrate2.2.1 <- ctrl_integrate2.2
ctrl_integrate2.2.1$celltype.stim <- paste(Idents(ctrl_integrate2.2.1), ctrl_integrate2.2.1$samples, sep = "_")
ctrl_integrate2.2.1$celltype <- Idents(ctrl_integrate2.2.1)
Idents(ctrl_integrate2.2.1) <- "celltype.stim"

ctrl_integrate_markers2.2.0 <- FindMarkers(ctrl_integrate2.2.1, ident.1 = "0_dcas9", ident.2 = "0_lnc5998", verbose = FALSE)

ctrl_integrate_markers2.2.1 <- FindMarkers(ctrl_integrate2.2.1, ident.1 = "1_dcas9", ident.2 = "1_lnc5998", verbose = FALSE)

ctrl_integrate_markers2.2.01 <- FindMarkers(ctrl_integrate2.2.1, ident.1 = c("0_dcas9","1_dcas9"), ident.2 =c("0_lnc5998","1_lnc5998"), verbose = FALSE)

write.csv(ctrl_integrate_markers2.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_DE0_Marker")

write.csv(ctrl_integrate_markers2.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_DE1_Marker")

write.csv(ctrl_integrate_markers2.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_DE01_Marker")

head(ctrl_integrate_markers2.1, n = 15)

save(ctrl_integrate2.2,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M3M4_SCTIntegration_PC11_res0.15_GB.Rdata")

```


```{r}
ctrl_integrate2.2.0_dcas9 <- subset(ctrl_integrate2.2.1,idents = "0_dcas9")
ctrl_integrate2.2.0_dcas9 <- NormalizeData(ctrl_integrate2.2.0_dcas9,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate2.2.0_dcas9_avgCPM <- rowMeans(as.data.frame(ctrl_integrate2.2.0_dcas9@assays$RNA@data))
ctrl_integrate2.2.0_dcas9.count <- as.data.frame(GetAssayData(ctrl_integrate2.2.0_dcas9, slot = "counts"))
ctrl_integrate2.2.0_dcas9.nUMI <- as.data.frame((rowSums(ctrl_integrate2.2.0_dcas9.count!=0)))
ctrl_integrate2.2.0_dcas9.UMISum <- as.data.frame((rowSums(ctrl_integrate2.2.0_dcas9.count)))
ctrl_integrate2.2.0_dcas9.pct <- as.data.frame((rowSums(ctrl_integrate2.2.0_dcas9.count!=0)/ncol(ctrl_integrate2.2.0_dcas9.count))*100)
x1 <- cbind(ctrl_integrate2.2.0_dcas9.nUMI,ctrl_integrate2.2.0_dcas9.UMISum,ctrl_integrate2.2.0_dcas9.pct,ctrl_integrate2.2.0_dcas9_avgCPM)

ctrl_integrate2.2.1_dcas9 <- subset(ctrl_integrate2.2.1,idents = "1_dcas9")
ctrl_integrate2.2.1_dcas9 <- NormalizeData(ctrl_integrate2.2.1_dcas9,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate2.2.1_dcas9_avgCPM <- rowMeans(as.data.frame(ctrl_integrate2.2.1_dcas9@assays$RNA@data))
ctrl_integrate2.2.1_dcas9.count <- as.data.frame(GetAssayData(ctrl_integrate2.2.1_dcas9, slot = "counts"))
ctrl_integrate2.2.1_dcas9.nUMI <- as.data.frame((rowSums(ctrl_integrate2.2.1_dcas9.count!=0)))
ctrl_integrate2.2.1_dcas9.UMISum <- as.data.frame((rowSums(ctrl_integrate2.2.1_dcas9.count)))
ctrl_integrate2.2.1_dcas9.pct <- as.data.frame((rowSums(ctrl_integrate2.2.1_dcas9.count!=0)/ncol(ctrl_integrate2.2.1_dcas9.count))*100)
x2 <- cbind(ctrl_integrate2.2.1_dcas9.nUMI,ctrl_integrate2.2.1_dcas9.UMISum,ctrl_integrate2.2.1_dcas9.pct,ctrl_integrate2.2.1_dcas9_avgCPM)

ctrl_integrate2.2.0_lnc5998 <- subset(ctrl_integrate2.2.1,idents = "0_lnc5998")
ctrl_integrate2.2.0_lnc5998 <- NormalizeData(ctrl_integrate2.2.0_lnc5998,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate2.2.0_lnc5998_avgCPM <- rowMeans(as.data.frame(ctrl_integrate2.2.0_lnc5998@assays$RNA@data))
ctrl_integrate2.2.0_lnc5998.count <- as.data.frame(GetAssayData(ctrl_integrate2.2.0_lnc5998, slot = "counts"))
ctrl_integrate2.2.0_lnc5998.nUMI <- as.data.frame((rowSums(ctrl_integrate2.2.0_lnc5998.count!=0)))
ctrl_integrate2.2.0_lnc5998.UMISum <- as.data.frame((rowSums(ctrl_integrate2.2.0_lnc5998.count)))
ctrl_integrate2.2.0_lnc5998.pct <- as.data.frame((rowSums(ctrl_integrate2.2.0_lnc5998.count!=0)/ncol(ctrl_integrate2.2.0_lnc5998.count))*100)
x3 <- cbind(ctrl_integrate2.2.0_lnc5998.nUMI,ctrl_integrate2.2.0_lnc5998.UMISum,ctrl_integrate2.2.0_lnc5998.pct,ctrl_integrate2.2.0_lnc5998_avgCPM)

ctrl_integrate2.2.1_lnc5998 <- subset(ctrl_integrate2.2.1,idents = "1_lnc5998")
ctrl_integrate2.2.1_lnc5998 <- NormalizeData(ctrl_integrate2.2.1_lnc5998,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate2.2.1_lnc5998_avgCPM <-  rowMeans(as.data.frame(ctrl_integrate2.2.1_lnc5998@assays$RNA@data))
ctrl_integrate2.2.1_lnc5998.count <- as.data.frame(GetAssayData(ctrl_integrate2.2.1_lnc5998, slot = "counts"))
ctrl_integrate2.2.1_lnc5998.nUMI <- as.data.frame((rowSums(ctrl_integrate2.2.1_lnc5998.count!=0)))
ctrl_integrate2.2.1_lnc5998.UMISum <- as.data.frame((rowSums(ctrl_integrate2.2.1_lnc5998.count)))
ctrl_integrate2.2.1_lnc5998.pct <- as.data.frame((rowSums(ctrl_integrate2.2.1_lnc5998.count!=0)/ncol(ctrl_integrate2.2.1_lnc5998.count))*100)
x4 <- cbind(ctrl_integrate2.2.1_lnc5998.nUMI,ctrl_integrate2.2.1_lnc5998.UMISum,ctrl_integrate2.2.1_lnc5998.pct,ctrl_integrate2.2.1_lnc5998_avgCPM)


y <- cbind(x1,x2,x3,x4)

write.csv(y,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M3M4/G185M3M4_Data")

```






```{r}
######################### SC transform integration method (USE THIS) ################################
##################3standardworkflow for integration  G185M3M4 ####################
object.list3.2 <- SplitObject(ctrl.merge.object3, split.by = "samples")

for (i in 1:length(object.list3.2)){
  object.list3.2[[i]] <- SCTransform(object.list3.2[[i]], verbose = TRUE)
}

ctrl.features3.2 <- SelectIntegrationFeatures(object.list = object.list3.2,dims=1:30, verbose = TRUE)
object.list3.2  <- PrepSCTIntegration(object.list = object.list3.2, anchor.features = ctrl.features3.2, 
    verbose = TRUE)

ctrl.anchors3.2 <- FindIntegrationAnchors(object.list = object.list3.2, normalization.method = "SCT", 
    anchor.features = ctrl.features3.2, verbose = FALSE)
ctrl_integrate3.2 <- IntegrateData(anchorset = ctrl.anchors3.2, normalization.method = "SCT", 
    verbose = TRUE)

#ctrl_integrate2 <- ScaleData(ctrl_integrate2)
ctrl_integrate3.2 <- RunPCA(ctrl_integrate3.2, npcs = 100)
ctrl_integrate3.2 <- RunUMAP(ctrl_integrate3.2, reduction = "pca", dims = 1:11)
ctrl_integrate3.2 <- FindNeighbors(ctrl_integrate3.2, reduction = "pca", dims = 1:11)
ctrl_integrate3.2 <- FindClusters(ctrl_integrate3.2 , resolution = 0.15 )   


ctrl_integrate3.2_p1.1 <- DimPlot(ctrl_integrate3.2, reduction = "umap",label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate3.2_p1.2 <- DimPlot(ctrl_integrate3.2, reduction = "umap",split.by = "samples", label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate3.2_p1.3 <- DimPlot(ctrl_integrate3.2, reduction = "umap",  group.by = "samples",label=TRUE, label.size=5, pt.size=0.5)
plot_grid(ctrl_integrate3.2_p1.1,ctrl_integrate3.2_p1.2,ctrl_integrate3.2_p1.3,ncol = 3)


###### We use RNA aasay to make dotpots 
DefaultAssay(ctrl_integrate3.2) <- "RNA"
ctrl_integrate3.2 <- NormalizeData(ctrl_integrate3.2)
ctrl_integrate3.2_d1.1 <-DotPlot(ctrl_integrate3.2, features = MS_list2)+RotatedAxis()
ctrl_integrate3.2_d1.2 <-DotPlot(ctrl_integrate3.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"),split.by = "samples", cols=c("red","blue")) +RotatedAxis()  

ctrl_integrate3.2_f1.2 <-  FeaturePlot(ctrl_integrate3.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples",  
            cols = c("grey", "red"),order=TRUE,label = TRUE)

ctrl_integrate3.2_v1.2 <- VlnPlot(ctrl_integrate3.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples", group.by ="seurat_clusters", pt.size = 0.1, combine = TRUE)


MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1','Cyp2f2','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')

table(FetchData(ctrl_integrate3.2, c('samples','ident')))


##Find Conserved Markers 
ctrl_integrate_cons_markers3.2.1 <- FindConservedMarkers(ctrl_integrate3.2, ident.1 = "1", grouping.var = "samples", verbose = FALSE)
ctrl_integrate_cons_markers3.2.0 <- FindConservedMarkers(ctrl_integrate3.2, ident.1 = "0", grouping.var = "samples", verbose = FALSE)

ctrl_integrate_cons_markers3.2.01 <- FindConservedMarkers(ctrl_integrate3.2, ident.1 = c("0","1"), grouping.var = "samples", verbose = FALSE)


write.csv(ctrl_integrate_cons_markers3.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M4M5/G185M4M5_Integrate_ConservedMark_Cluster1")

write.csv(ctrl_integrate_cons_markers3.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M4M5/G185M4M5_Integrate_ConservedMark_Cluster0")

write.csv(ctrl_integrate_cons_markers3.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M4M5/G185M4M5_Integrate_ConservedMark_Cluster01")

### DE analysis#

ctrl_integrate3.2.1 <- ctrl_integrate3.2
ctrl_integrate3.2.1$celltype.stim <- paste(Idents(ctrl_integrate3.2.1), ctrl_integrate3.2.1$samples, sep = "_")
ctrl_integrate3.2.1$celltype <- Idents(ctrl_integrate3.2.1)
Idents(ctrl_integrate3.2.1) <- "celltype.stim"

ctrl_integrate_markers3.2.0 <- FindMarkers(ctrl_integrate3.2.1, ident.1 = "0_lnc5998", ident.2 = "0_lnc3779", verbose = FALSE)

ctrl_integrate_markers3.2.1 <- FindMarkers(ctrl_integrate3.2.1, ident.1 = "1_lnc5998", ident.2 = "1_lnc3779", verbose = FALSE)

ctrl_integrate_markers3.2.01 <- FindMarkers(ctrl_integrate3.2.1, ident.1 = c("0_lnc5998","1_lnc3779"), ident.2 =c("0_lnc3779","1_lnc3779"), verbose = FALSE)

write.csv(ctrl_integrate_markers3.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M4M5/G185M4M5_DE0_Marker")

write.csv(ctrl_integrate_markers3.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M4M5/G185M4M5_DE1_Marker")

write.csv(ctrl_integrate_markers3.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M4M5/G185M4M5_DE01_Marker")


save(ctrl_integrate3.2,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M4M5_SCTIntegration_PC11_res0.15_GB.Rdata")

```



```{r}

ctrl_integrate3.2.0_lnc5998 <- subset(ctrl_integrate3.2.1,idents = "0_lnc5998")
ctrl_integrate3.2.0_lnc5998 <- NormalizeData(ctrl_integrate3.2.0_lnc5998,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate3.2.0_lnc5998_avgCPM <- rowMeans(as.data.frame(ctrl_integrate3.2.0_lnc5998@assays$RNA@data))
ctrl_integrate3.2.0_lnc5998.count <- as.data.frame(GetAssayData(ctrl_integrate3.2.0_lnc5998, slot = "counts"))
ctrl_integrate3.2.0_lnc5998.nUMI <- as.data.frame((rowSums(ctrl_integrate3.2.0_lnc5998.count!=0)))
ctrl_integrate3.2.0_lnc5998.UMISum <- as.data.frame((rowSums(ctrl_integrate3.2.0_lnc5998.count)))
ctrl_integrate3.2.0_lnc5998.pct <- as.data.frame((rowSums(ctrl_integrate3.2.0_lnc5998.count!=0)/ncol(ctrl_integrate3.2.0_lnc5998.count))*100)
x1 <- cbind(ctrl_integrate3.2.0_lnc5998.nUMI,ctrl_integrate3.2.0_lnc5998.UMISum,ctrl_integrate3.2.0_lnc5998.pct,ctrl_integrate3.2.0_lnc5998_avgCPM)


ctrl_integrate3.2.1_lnc5998 <- subset(ctrl_integrate3.2.1,idents = "1_lnc5998")
ctrl_integrate3.2.1_lnc5998 <- NormalizeData(ctrl_integrate3.2.1_lnc5998,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate3.2.1_lnc5998_avgCPM <- rowMeans(as.data.frame(ctrl_integrate3.2.1_lnc5998@assays$RNA@data))
ctrl_integrate3.2.1_lnc5998.count <- as.data.frame(GetAssayData(ctrl_integrate3.2.1_lnc5998, slot = "counts"))
ctrl_integrate3.2.1_lnc5998.nUMI <- as.data.frame((rowSums(ctrl_integrate3.2.1_lnc5998.count!=0)))
ctrl_integrate3.2.1_lnc5998.UMISum <- as.data.frame((rowSums(ctrl_integrate3.2.1_lnc5998.count)))
ctrl_integrate3.2.1_lnc5998.pct <- as.data.frame((rowSums(ctrl_integrate3.2.1_lnc5998.count!=0)/ncol(ctrl_integrate3.2.1_lnc5998.count))*100)
x2 <- cbind(ctrl_integrate3.2.1_lnc5998.nUMI,ctrl_integrate3.2.1_lnc5998.UMISum,ctrl_integrate3.2.1_lnc5998.pct,ctrl_integrate3.2.1_lnc5998_avgCPM)

ctrl_integrate3.2.0_lnc3779 <- subset(ctrl_integrate3.2.1,idents = "0_lnc3779")
ctrl_integrate3.2.0_lnc3779 <- NormalizeData(ctrl_integrate3.2.0_lnc3779,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate3.2.0_lnc3779_avgCPM <- rowMeans(as.data.frame(ctrl_integrate3.2.0_lnc3779@assays$RNA@data))
ctrl_integrate3.2.0_lnc3779.count <- as.data.frame(GetAssayData(ctrl_integrate3.2.0_lnc3779, slot = "counts"))
ctrl_integrate3.2.0_lnc3779.nUMI <- as.data.frame((rowSums(ctrl_integrate3.2.0_lnc3779.count!=0)))
ctrl_integrate3.2.0_lnc3779.UMISum <- as.data.frame((rowSums(ctrl_integrate3.2.0_lnc3779.count)))
ctrl_integrate3.2.0_lnc3779.pct <- as.data.frame((rowSums(ctrl_integrate3.2.0_lnc3779.count!=0)/ncol(ctrl_integrate3.2.0_lnc3779.count))*100)
x3 <- cbind(ctrl_integrate3.2.0_lnc3779.nUMI,ctrl_integrate3.2.0_lnc3779.UMISum,ctrl_integrate3.2.0_lnc3779.pct,ctrl_integrate3.2.0_lnc3779_avgCPM)


ctrl_integrate3.2.1_lnc3779 <- subset(ctrl_integrate3.2.1,idents = "1_lnc3779")
ctrl_integrate3.2.1_lnc3779 <- NormalizeData(ctrl_integrate3.2.1_lnc3779,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate3.2.1_lnc3779_avgCPM <- rowMeans(as.data.frame(ctrl_integrate3.2.1_lnc3779@assays$RNA@data))
ctrl_integrate3.2.1_lnc3779.count <- as.data.frame(GetAssayData(ctrl_integrate3.2.1_lnc3779, slot = "counts"))
ctrl_integrate3.2.1_lnc3779.nUMI <- as.data.frame((rowSums(ctrl_integrate3.2.1_lnc3779.count!=0)))
ctrl_integrate3.2.1_lnc3779.UMISum <- as.data.frame((rowSums(ctrl_integrate3.2.1_lnc3779.count)))
ctrl_integrate3.2.1_lnc3779.pct <- as.data.frame((rowSums(ctrl_integrate3.2.1_lnc3779.count!=0)/ncol(ctrl_integrate3.2.1_lnc3779.count))*100)
x4 <- cbind(ctrl_integrate3.2.1_lnc3779.nUMI,ctrl_integrate3.2.1_lnc3779.UMISum,ctrl_integrate3.2.1_lnc3779.pct,ctrl_integrate3.2.1_lnc3779_avgCPM)


y <- cbind(x1,x2,x3,x4)

write.csv(y,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/Markers/G185M4M5/G185M4M5_Data")

```






```{r}
######################### SC transform integration method (USE THIS) ################################
##################3standardworkflow for integration  G185M3M4 ####################
object.list4.2 <- SplitObject(ctrl.merge.object4, split.by = "samples")

for (i in 1:length(object.list4.2)){
  object.list4.2[[i]] <- SCTransform(object.list4.2[[i]], verbose = TRUE)
}

ctrl.features4.2 <- SelectIntegrationFeatures(object.list = object.list4.2,dims=1:30, verbose = TRUE)
object.list4.2  <- PrepSCTIntegration(object.list = object.list4.2, anchor.features = ctrl.features4.2, 
    verbose = TRUE)

ctrl.anchors4.2 <- FindIntegrationAnchors(object.list = object.list4.2, normalization.method = "SCT", 
    anchor.features = ctrl.features4.2, verbose = FALSE)
ctrl_integrate4.2 <- IntegrateData(anchorset = ctrl.anchors4.2, normalization.method = "SCT", 
    verbose = TRUE)

#ctrl_integrate2 <- ScaleData(ctrl_integrate2)
ctrl_integrate4.2 <- RunPCA(ctrl_integrate4.2, npcs = 100)
ctrl_integrate4.2 <- RunUMAP(ctrl_integrate4.2, reduction = "pca", dims = 1:10)
ctrl_integrate4.2 <- FindNeighbors(ctrl_integrate4.2, reduction = "pca", dims = 1:10)
ctrl_integrate4.2 <- FindClusters(ctrl_integrate4.2 , resolution = 0.15 )   


ctrl_integrate4.2_p1.1 <- DimPlot(ctrl_integrate4.2, reduction = "umap",label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate4.2_p1.2 <- DimPlot(ctrl_integrate4.2, reduction = "umap",split.by = "samples", label=TRUE, label.size=5, pt.size=0.5)
ctrl_integrate4.2_p1.3 <- DimPlot(ctrl_integrate4.2, reduction = "umap",  group.by = "samples",label=TRUE, label.size=5, pt.size=0.5)
plot_grid(ctrl_integrate4.2_p1.1,ctrl_integrate4.2_p1.2,ctrl_integrate4.2_p1.3,ncol = 3)


###### We use RNA aasay to make dotpots 
DefaultAssay(ctrl_integrate4.2) <- "RNA"
ctrl_integrate4.2 <- NormalizeData(ctrl_integrate4.2)
ctrl_integrate4.2_d1.1 <-DotPlot(ctrl_integrate4.2, features = MS_list2)+RotatedAxis()
ctrl_integrate4.2_d1.2 <-DotPlot(ctrl_integrate4.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"),split.by = "samples", cols=c("red","blue")) +RotatedAxis()  

ctrl_integrate4.2_f1.2 <-  FeaturePlot(ctrl_integrate4.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples",  
            cols = c("grey", "red"),order=TRUE,label = TRUE)

ctrl_integrate4.2_v1.2 <- VlnPlot(ctrl_integrate4.2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples", group.by ="seurat_clusters", pt.size = 0.1, combine = TRUE)


MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1','Cyp2f2','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')

table(FetchData(ctrl_integrate4.2, c('samples','ident')))


##Find Conserved Markers 
ctrl_integrate_cons_markers4.2.1 <- FindConservedMarkers(ctrl_integrate4.2, ident.1 = "1", grouping.var = "samples", verbose = FALSE)
ctrl_integrate_cons_markers4.2.0 <- FindConservedMarkers(ctrl_integrate4.2, ident.1 = "0", grouping.var = "samples", verbose = FALSE)

ctrl_integrate_cons_markers4.2.01 <- FindConservedMarkers(ctrl_integrate4.2, ident.1 = c("0","1"), grouping.var = "samples", verbose = FALSE)


write.csv(ctrl_integrate_cons_markers4.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_Integrate_ConservedMark_Cluster1")

write.csv(ctrl_integrate_cons_markers4.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_Integrate_ConservedMark_Cluster0")

write.csv(ctrl_integrate_cons_markers4.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_Integrate_ConservedMark_Cluster01")

### DE analysis#

ctrl_integrate4.2.1 <- ctrl_integrate4.2
ctrl_integrate4.2.1$celltype.stim <- paste(Idents(ctrl_integrate4.2.1), ctrl_integrate4.2.1$samples, sep = "_")
ctrl_integrate4.2.1$celltype <- Idents(ctrl_integrate4.2.1)
Idents(ctrl_integrate4.2.1) <- "celltype.stim"

ctrl_integrate_markers4.2.0 <- FindMarkers(ctrl_integrate4.2.1, ident.1 = "0_G171Bdcas9", ident.2 = "0_G171Clnc5998", verbose = FALSE)

ctrl_integrate_markers4.2.0_nologFC <- FindMarkers(ctrl_integrate4.2.1, ident.1 = "0_G171Bdcas9", ident.2 = "0_G171Clnc5998", verbose = FALSE,logfc.threshold = 0)

ctrl_integrate_markers4.2.1 <- FindMarkers(ctrl_integrate4.2.1, ident.1 = "1_G171Bdcas9", ident.2 = "1_G171Clnc5998", verbose = FALSE)

ctrl_integrate_markers4.2.1_nologFC <- FindMarkers(ctrl_integrate4.2.1, ident.1 = "1_G171Bdcas9", ident.2 = "1_G171Clnc5998", verbose = FALSE,logfc.threshold = 0)

ctrl_integrate_markers4.2.01 <- FindMarkers(ctrl_integrate4.2.1, ident.1 = c("0_G171Bdcas9","1_G171Bdcas9"), ident.2 =c("0_G171Clnc5998","1_G171Clnc5998"), verbose = FALSE)

write.csv(ctrl_integrate_markers4.2.0,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_DE0_Marker")

write.csv(ctrl_integrate_markers4.2.0_nologFC,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_DE0_Marker_nologFC")

write.csv(ctrl_integrate_markers4.2.1,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_DE1_Marker")

write.csv(ctrl_integrate_markers4.2.1_nologFC,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_DE1_Marker_nologFC")


write.csv(ctrl_integrate_markers4.2.01,"/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC/G171BC_DE01_Marker")


save(ctrl_integrate4.2,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/G171BC_SCTIntegration_PC10_res0.15_GB.Rdata")

```

```{r}

ctrl_integrate4.2.0_G171Bdcas9 <- subset(ctrl_integrate4.2.1,idents = "0_G171Bdcas9")
ctrl_integrate4.2.0_G171Bdcas9 <- NormalizeData(ctrl_integrate4.2.0_G171Bdcas9,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate4.2.0_G171Bdcas9_avgCPM <- rowMeans(as.data.frame(ctrl_integrate4.2.0_G171Bdcas9@assays$RNA@data))
ctrl_integrate4.2.0_G171Bdcas9.count <- as.data.frame(GetAssayData(ctrl_integrate4.2.0_G171Bdcas9, slot = "counts"))
ctrl_integrate4.2.0_G171Bdcas9.nUMI <- as.data.frame((rowSums(ctrl_integrate4.2.0_G171Bdcas9.count!=0)))
ctrl_integrate4.2.0_G171Bdcas9.UMISum <- as.data.frame((rowSums(ctrl_integrate4.2.0_G171Bdcas9.count)))
ctrl_integrate4.2.0_G171Bdcas9.pct <- as.data.frame((rowSums(ctrl_integrate4.2.0_G171Bdcas9.count!=0)/ncol(ctrl_integrate4.2.0_G171Bdcas9.count))*100)
x1 <- cbind(ctrl_integrate4.2.0_G171Bdcas9.nUMI,ctrl_integrate4.2.0_G171Bdcas9.UMISum,ctrl_integrate4.2.0_G171Bdcas9.pct,ctrl_integrate4.2.0_G171Bdcas9_avgCPM)


#####################################################################################
ctrl_integrate4.2.1_G171Bdcas9 <- subset(ctrl_integrate4.2.1,idents = "1_G171Bdcas9")
ctrl_integrate4.2.1_G171Bdcas9 <- NormalizeData(ctrl_integrate4.2.1_G171Bdcas9,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate4.2.1_G171Bdcas9_avgCPM <- rowMeans(as.data.frame(ctrl_integrate4.2.1_G171Bdcas9@assays$RNA@data))
ctrl_integrate4.2.1_G171Bdcas9.count <- as.data.frame(GetAssayData(ctrl_integrate4.2.1_G171Bdcas9, slot = "counts"))
ctrl_integrate4.2.1_G171Bdcas9.nUMI <- as.data.frame((rowSums(ctrl_integrate4.2.1_G171Bdcas9.count!=0)))
ctrl_integrate4.2.1_G171Bdcas9.UMISum <- as.data.frame((rowSums(ctrl_integrate4.2.1_G171Bdcas9.count)))
ctrl_integrate4.2.1_G171Bdcas9.pct <- as.data.frame((rowSums(ctrl_integrate4.2.1_G171Bdcas9.count!=0)/ncol(ctrl_integrate4.2.1_G171Bdcas9.count))*100)
x2 <- cbind(ctrl_integrate4.2.1_G171Bdcas9.nUMI,ctrl_integrate4.2.1_G171Bdcas9.UMISum,ctrl_integrate4.2.1_G171Bdcas9.pct,ctrl_integrate4.2.1_G171Bdcas9_avgCPM)

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

ctrl_integrate4.2.0_G171Clnc5998 <- subset(ctrl_integrate4.2.1,idents = "0_G171Clnc5998")
ctrl_integrate4.2.0_G171Clnc5998 <- NormalizeData(ctrl_integrate4.2.0_G171Clnc5998,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate4.2.0_G171Bdcas9_avgCPM <- rowMeans(as.data.frame(ctrl_integrate4.2.0_G171Clnc5998@assays$RNA@data))
ctrl_integrate4.2.0_G171Clnc5998.count <- as.data.frame(GetAssayData(ctrl_integrate4.2.0_G171Clnc5998, slot = "counts"))
ctrl_integrate4.2.0_G171Clnc5998.nUMI <- as.data.frame((rowSums(ctrl_integrate4.2.0_G171Clnc5998.count!=0)))
ctrl_integrate4.2.0_G171Clnc5998.UMISum <- as.data.frame((rowSums(ctrl_integrate4.2.0_G171Clnc5998.count)))
ctrl_integrate4.2.0_G171Clnc5998.pct <- as.data.frame((rowSums(ctrl_integrate4.2.0_G171Clnc5998.count!=0)/ncol(ctrl_integrate4.2.0_G171Clnc5998.count))*100)
x3 <- cbind(ctrl_integrate4.2.0_G171Clnc5998.nUMI,ctrl_integrate4.2.0_G171Clnc5998.UMISum,ctrl_integrate4.2.0_G171Clnc5998.pct,ctrl_integrate4.2.0_G171Bdcas9_avgCPM)

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


ctrl_integrate4.2.1_G171Clnc5998 <- subset(ctrl_integrate4.2.1,idents = "1_G171Clnc5998")
ctrl_integrate4.2.1_G171Clnc5998 <- NormalizeData(ctrl_integrate4.2.1_G171Clnc5998,assay = "RNA",normalization.method ="RC",scale.factor = 1e6)
ctrl_integrate4.2.1_G171Clnc5998_avgCPM <- rowMeans(as.data.frame(ctrl_integrate4.2.1_G171Clnc5998@assays$RNA@data))
ctrl_integrate4.2.1_G171Clnc5998.count <- as.data.frame(GetAssayData(ctrl_integrate4.2.1_G171Clnc5998, slot = "counts"))
ctrl_integrate4.2.1_G171Clnc5998.nUMI <- as.data.frame((rowSums(ctrl_integrate4.2.1_G171Clnc5998.count!=0)))
ctrl_integrate4.2.1_G171Clnc5998.UMISum <- as.data.frame((rowSums(ctrl_integrate4.2.1_G171Clnc5998.count)))
ctrl_integrate4.2.1_G171Clnc5998.pct <- as.data.frame((rowSums(ctrl_integrate4.2.1_G171Clnc5998.count!=0)/ncol(ctrl_integrate4.2.1_G171Clnc5998.count))*100)
x4 <- cbind(ctrl_integrate4.2.1_G171Clnc5998.nUMI,ctrl_integrate4.2.1_G171Clnc5998.UMISum,ctrl_integrate4.2.1_G171Clnc5998.pct,ctrl_integrate4.2.1_G171Clnc5998_avgCPM)


y <- cbind(x1,x2,x3,x4)

write.csv(y, "/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171_New/Markers/G171BC_Clust01_Data")

```