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singlecell_dmel_hemocytes.R
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singlecell_dmel_hemocytes.R
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library(grid)
library(gridExtra)
library(ggplot2)
library(Seurat)
library(pals)
library(pryr)
library(GO.db)
library(org.Dm.eg.db)
library(reshape)
library(cowplot)
library(scales)
library(slingshot)
library(dplyr)
library(tidymodels)
library(ranger)
library(viridis)
library(plyr)
library(rgl)
library(LSD)
library(patchwork)
library(RColorBrewer)
library(limma)
####SECTION 1 - READING IN DATA FROM CELL RANGER ########
##Sample data
mito.genes <- read.table(file = "mito_genes.dat")
mito.genes <- as.vector(mito.genes[,1])
##NSRef-1_Inf_res
data <- Read10X(data.dir = "NSRef-1_Inf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "NSRef-1_Inf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
NSRef_1_Inf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##NSRef-2_Inf_res
data <- Read10X(data.dir = "NSRef-2_Inf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "NSRef-2_Inf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
NSRef_2_Inf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##NSRef-3_Inf_res
data <- Read10X(data.dir = "NSRef-3_Inf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "NSRef-3_Inf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
NSRef_3_Inf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##NSRef-1_Uninf_res
data <- Read10X(data.dir = "NSRef-1_Uninf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "NSRef-1_Uninf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
NSRef_1_Uninf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##NSRef-2_Uninf_res
data <- Read10X(data.dir = "NSRef-2_Uninf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "NSRef-2_Uninf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
NSRef_2_Uninf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##NSRef-3_Uninf_res
data <- Read10X(data.dir = "NSRef-3_Uninf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "NSRef-3_Uninf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
NSRef_3_Uninf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##C1_Inf_res
data <- Read10X(data.dir = "C1_Inf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "C1_Inf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
C1_Inf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##C2_Inf_res
data <- Read10X(data.dir = "C2_Inf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "C2_Inf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
C2_Inf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##C3_Inf_res
data <- Read10X(data.dir = "C3_Inf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "C3_Inf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
C3_Inf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##C1_Uninf_res
data <- Read10X(data.dir = "C1_Uninf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "C1_Uninf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
C1_Uninf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##C2_Uninf_res
data <- Read10X(data.dir = "C2_Uninf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "C2_Uninf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
C2_Uninf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
##C3_Uninf_res
data <- Read10X(data.dir = "C3_Uninf_res/outs/filtered_gene_bc_matrices/D.mel/")
sample <- CreateSeuratObject(counts = data, min.cells = 3, min.features = 200, project = "C3_Uninf")
mito.genes <- mito.genes[mito.genes %in% rownames(sample)]
sample[["percent.mt"]] <- PercentageFeatureSet(sample, features = mito.genes)
C3_Uninf_obj <- subset(sample, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 )
####SECTION 2 - KEEP only He+ or Srp+ CELLS ########
integrate.list <- list(C1_Inf_obj, C1_Uninf_obj,C3_Inf_obj, C3_Uninf_obj,NSRef_1_Inf_obj, NSRef_1_Uninf_obj,NSRef_3_Inf_obj, NSRef_3_Uninf_obj)
names(integrate.list)=c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")
for (i in 1:length(x = integrate.list)) {
integrate.list[[i]] <- NormalizeData(object = integrate.list[[i]], verbose = FALSE)
integrate.list[[i]] <- FindVariableFeatures(object = integrate.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}
reference.list <- integrate.list[c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")]
integrate.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:50)
integrate.combined <- IntegrateData(anchorset = integrate.anchors, dims = 1:50)
integrate.combined <- subset(integrate.combined, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
DefaultAssay(integrate.combined) <- "integrated"
new <- GetAssayData(object = integrate.combined, assay = "RNA", slot = "data")[c("FBgn0003507"),]>0
new2 <- GetAssayData(object = integrate.combined, assay = "RNA", slot = "data")[c("FBgn0028430"),]>0
new <- new+new2
new <- new[new >0]
length(new)
cells <- gsub("_.","",names(new))
cells <- cells[!(duplicated(cells) | duplicated(cells, fromLast = TRUE)) ]
####SECTION 3 - ROUND 1 CLUSTERING, REMOVING SPERM CELLS ########
C1_Inf_obj <- subset(C1_Inf_obj,cells = cells)
C1_Uninf_obj <- subset(C1_Uninf_obj,cells = cells)
C3_Inf_obj <- subset(C3_Inf_obj,cells = cells)
C3_Uninf_obj <- subset(C3_Uninf_obj,cells = cells)
NSRef_1_Inf_obj <- subset(NSRef_1_Inf_obj,cells = cells)
NSRef_1_Uninf_obj <- subset(NSRef_1_Uninf_obj,cells = cells)
NSRef_3_Inf_obj <- subset(NSRef_3_Inf_obj,cells = cells)
NSRef_3_Uninf_obj <- subset(NSRef_3_Uninf_obj,cells = cells)
integrate.list <- list(C1_Inf_obj, C1_Uninf_obj,C3_Inf_obj, C3_Uninf_obj,NSRef_1_Inf_obj, NSRef_1_Uninf_obj,NSRef_3_Inf_obj, NSRef_3_Uninf_obj)
names(integrate.list)=c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")
for (i in 1:length(x = integrate.list)) {
integrate.list[[i]] <- NormalizeData(object = integrate.list[[i]], verbose = FALSE)
integrate.list[[i]] <- FindVariableFeatures(object = integrate.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}
reference.list <- integrate.list[c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")]
integrate.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:50)
integrate.combined <- IntegrateData(anchorset = integrate.anchors, dims = 1:50)
integrate.combined <- subset(integrate.combined, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
DefaultAssay(integrate.combined) <- "integrated"
integrate.combined <- ScaleData(integrate.combined, vars.to.regress = c("nCount_RNA","nFeature_RNA","percent.mt"), features = rownames(integrate.combined))
integrate.combined <- RunPCA(integrate.combined, npcs = 50, verbose = FALSE)
integrate.combined <- RunUMAP(integrate.combined, reduction = "pca", dims = 1:50)
integrate.combined <- FindNeighbors(integrate.combined, reduction = "pca", dims = 1:50)
s.genes <- read.csv(file = "s.genes.fly.csv")
g2m.genes <- read.csv(file = "g2m.genes.fly.csv")
s.genes <- as.character(s.genes$FlyBaseID)
g2m.genes <- as.character(g2m.genes$FlyBaseID)
s.genes <- s.genes[s.genes %in% rownames(integrate.combined)]
g2m.genes <- g2m.genes[g2m.genes %in% rownames(integrate.combined)]
integrate.combined <- CellCycleScoring(integrate.combined, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
integrate.combined$CC.Difference <- integrate.combined$S.Score - integrate.combined$G2M.Score
integrate.combined$treatment <- integrate.combined$orig.ident
integrate.combined$treatment <- gsub("NSRef-._","",integrate.combined$treatment)
integrate.combined$treatment <- gsub("C._","",integrate.combined$treatment)
integrate.combined$treatment <- gsub("Inf","Infected",integrate.combined$treatment)
integrate.combined$population <- integrate.combined$orig.ident
integrate.combined$population <- gsub("_Uninf","",integrate.combined$population)
integrate.combined$population <- gsub("_Inf","",integrate.combined$population)
integrate.combined$population <- gsub("-","",integrate.combined$population)
integrate.combined$population <- gsub(".$","",integrate.combined$population)
integrate.combined$population <- gsub("C","control",integrate.combined$population)
integrate.combined$poptreat <- paste(integrate.combined$population,integrate.combined$treatment)
integrate.combined_0.2 <- FindClusters(integrate.combined, resolution = 0.2)
sample.markers <- FindAllMarkers(integrate.combined_0.2, min.pct = 0.25, logfc.threshold = 0.25)
write.csv(sample.markers, file = "sample.markers.list_round1.csv", quote = F)
write.csv(table(Idents(integrate.combined_0.2)),file = "round1_cellcounts.csv")
jpeg("round1_umap.jpeg", width = 750, height = 350)
DimPlot(integrate.combined_0.2, reduction = "umap", label = TRUE)
dev.off()
integrate.combined_0.2 <- subset(integrate.combined_0.2, subset = seurat_clusters != 9)
cells <- gsub("_.","",colnames(integrate.combined_0.2))
cells <- cells[!(duplicated(cells) | duplicated(cells, fromLast = TRUE)) ]
C1_Inf_obj <- subset(C1_Inf_obj,cells = cells)
C1_Uninf_obj <- subset(C1_Uninf_obj,cells = cells)
C3_Inf_obj <- subset(C3_Inf_obj,cells = cells)
C3_Uninf_obj <- subset(C3_Uninf_obj,cells = cells)
NSRef_1_Inf_obj <- subset(NSRef_1_Inf_obj,cells = cells)
NSRef_1_Uninf_obj <- subset(NSRef_1_Uninf_obj,cells = cells)
NSRef_3_Inf_obj <- subset(NSRef_3_Inf_obj,cells = cells)
NSRef_3_Uninf_obj <- subset(NSRef_3_Uninf_obj,cells = cells)
####SECTION 4 - ROUND 2 CLUSTERING, REMOVE MUSCLE AND FATBODY CELLS ########
integrate.list <- list(C1_Inf_obj, C1_Uninf_obj,C3_Inf_obj, C3_Uninf_obj,NSRef_1_Inf_obj, NSRef_1_Uninf_obj,NSRef_3_Inf_obj, NSRef_3_Uninf_obj)
names(integrate.list)=c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")
for (i in 1:length(x = integrate.list)) {
integrate.list[[i]] <- NormalizeData(object = integrate.list[[i]], verbose = FALSE)
integrate.list[[i]] <- FindVariableFeatures(object = integrate.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}
reference.list <- integrate.list[c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")]
integrate.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:50)
integrate.combined <- IntegrateData(anchorset = integrate.anchors, dims = 1:50)
integrate.combined <- subset(integrate.combined, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
DefaultAssay(integrate.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
integrate.combined <- ScaleData(integrate.combined, vars.to.regress = c("nCount_RNA","nFeature_RNA","percent.mt"), features = rownames(integrate.combined))
integrate.combined <- RunPCA(integrate.combined, npcs = 50, verbose = FALSE)
integrate.combined <- RunUMAP(integrate.combined, reduction = "pca", dims = 1:50)
integrate.combined <- FindNeighbors(integrate.combined, reduction = "pca", dims = 1:50)
s.genes <- read.csv(file = "s.genes.fly.csv")
g2m.genes <- read.csv(file = "g2m.genes.fly.csv")
s.genes <- as.character(s.genes$FlyBaseID)
g2m.genes <- as.character(g2m.genes$FlyBaseID)
s.genes <- s.genes[s.genes %in% rownames(integrate.combined)]
g2m.genes <- g2m.genes[g2m.genes %in% rownames(integrate.combined)]
integrate.combined <- CellCycleScoring(integrate.combined, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
integrate.combined$CC.Difference <- integrate.combined$S.Score - integrate.combined$G2M.Score
integrate.combined$treatment <- integrate.combined$orig.ident
integrate.combined$treatment <- gsub("NSRef-._","",integrate.combined$treatment)
integrate.combined$treatment <- gsub("C._","",integrate.combined$treatment)
integrate.combined$treatment <- gsub("Inf","Infected",integrate.combined$treatment)
integrate.combined$population <- integrate.combined$orig.ident
integrate.combined$population <- gsub("_Uninf","",integrate.combined$population)
integrate.combined$population <- gsub("_Inf","",integrate.combined$population)
integrate.combined$population <- gsub("-","",integrate.combined$population)
integrate.combined$population <- gsub(".$","",integrate.combined$population)
integrate.combined$population <- gsub("C","control",integrate.combined$population)
integrate.combined$poptreat <- paste(integrate.combined$population,integrate.combined$treatment)
integrate.combined_0.3 <- FindClusters(integrate.combined, resolution = 0.3)
sample.markers <- FindAllMarkers(integrate.combined_0.3, min.pct = 0.25, logfc.threshold = 0.25)
write.csv(sample.markers, file = "sample.markers.list_round2.csv", quote = F)
write.csv(table(Idents(integrate.combined_0.3)),file = "round2_cellcounts.csv")
jpeg("round2_umap.jpeg", width = 750, height = 350)
DimPlot(integrate.combined_0.3, reduction = "umap", label = TRUE)
dev.off()
integrate.combined_0.3 <- subset(integrate.combined_0.3, subset = seurat_clusters != 10)
integrate.combined_0.3 <- subset(integrate.combined_0.3, subset = seurat_clusters != 9)
cells <- gsub("_.","",colnames(integrate.combined_0.3))
cells <- cells[!(duplicated(cells) | duplicated(cells, fromLast = TRUE)) ]
C1_Inf_obj <- subset(C1_Inf_obj,cells = cells)
C1_Uninf_obj <- subset(C1_Uninf_obj,cells = cells)
C3_Inf_obj <- subset(C3_Inf_obj,cells = cells)
C3_Uninf_obj <- subset(C3_Uninf_obj,cells = cells)
NSRef_1_Inf_obj <- subset(NSRef_1_Inf_obj,cells = cells)
NSRef_1_Uninf_obj <- subset(NSRef_1_Uninf_obj,cells = cells)
NSRef_3_Inf_obj <- subset(NSRef_3_Inf_obj,cells = cells)
NSRef_3_Uninf_obj <- subset(NSRef_3_Uninf_obj,cells = cells)
####SECTION 5 - ROUND 3 CLUSTERING ########
integrate.list <- list(C1_Inf_obj, C1_Uninf_obj,C3_Inf_obj, C3_Uninf_obj,NSRef_1_Inf_obj, NSRef_1_Uninf_obj,NSRef_3_Inf_obj, NSRef_3_Uninf_obj)
names(integrate.list)=c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")
for (i in 1:length(x = integrate.list)) {
integrate.list[[i]] <- NormalizeData(object = integrate.list[[i]], verbose = FALSE)
integrate.list[[i]] <- FindVariableFeatures(object = integrate.list[[i]], selection.method = "vst", nfeatures = 2000, verbose = TRUE)
}
reference.list <- integrate.list[c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")]
integrate.anchors <- FindIntegrationAnchors(object.list = reference.list, dims = 1:50, anchor.features = 2000)
integrate.combined <- IntegrateData(anchorset = integrate.anchors, dims = 1:50)
integrate.combined <- subset(integrate.combined, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
DefaultAssay(integrate.combined) <- "integrated"
s.genes <- read.csv(file = "s.genes.fly.csv")
g2m.genes <- read.csv(file = "g2m.genes.fly.csv")
s.genes <- as.character(s.genes$FlyBaseID)
g2m.genes <- as.character(g2m.genes$FlyBaseID)
s.genes <- s.genes[s.genes %in% rownames(integrate.combined)]
g2m.genes <- g2m.genes[g2m.genes %in% rownames(integrate.combined)]
integrate.combined <- CellCycleScoring(integrate.combined, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
integrate.combined$CC.Difference <- integrate.combined$S.Score - integrate.combined$G2M.Score
integrate.combined$treatment <- integrate.combined$orig.ident
integrate.combined$treatment <- gsub("NSRef-._","",integrate.combined$treatment)
integrate.combined$treatment <- gsub("C._","",integrate.combined$treatment)
integrate.combined$treatment <- gsub("Inf","Infected",integrate.combined$treatment)
integrate.combined$population <- integrate.combined$orig.ident
integrate.combined$population <- gsub("_Uninf","",integrate.combined$population)
integrate.combined$population <- gsub("_Inf","",integrate.combined$population)
integrate.combined$population <- gsub("-","",integrate.combined$population)
integrate.combined$population <- gsub(".$","",integrate.combined$population)
integrate.combined$population <- gsub("C","control",integrate.combined$population)
integrate.combined$poptreat <- paste(integrate.combined$population,integrate.combined$treatment)
integrate.combined_cellcycle <- integrate.combined
integrate.combined <- ScaleData(integrate.combined, vars.to.regress = c("nCount_RNA","nFeature_RNA","percent.mt"), features = rownames(integrate.combined))
integrate.combined <- RunPCA(integrate.combined, npcs = 50, verbose = FALSE)
integrate.combined <- RunUMAP(integrate.combined, reduction = "pca", dims = 1:50)
integrate.combined <- FindNeighbors(integrate.combined, reduction = "pca", dims = 1:50)
integrate.combined_0.3 <- FindClusters(integrate.combined, resolution = 0.3)
integrate.combined_0.3$poptreat <- gsub("control Infected", "No Parasitism, Infection", integrate.combined_0.3$poptreat)
integrate.combined_0.3$poptreat <- gsub("control Uninf", "No Parasitism, No Infection", integrate.combined_0.3$poptreat)
integrate.combined_0.3$poptreat <- gsub("NSRef Infected", "High Parasitism, Infection", integrate.combined_0.3$poptreat)
integrate.combined_0.3$poptreat <- gsub("NSRef Uninf", "High Parasitism, No Infection", integrate.combined_0.3$poptreat)
integrate.combined_0.3$poptreat <- factor(integrate.combined_0.3$poptreat, levels = c("No Parasitism, No Infection", "No Parasitism, Infection", "High Parasitism, No Infection", "High Parasitism, Infection"))
integrate.combined_0.3$population <- gsub("control", "No Parasitism", integrate.combined_0.3$population)
integrate.combined_0.3$population <- gsub("NSRef", "High Parasitism", integrate.combined_0.3$population)
integrate.combined_0.3$population <- factor(integrate.combined_0.3$population, levels = c("No Parasitism","High Parasitism"))
integrate.combined_0.3$treatment <- gsub("Infected", "Infection", integrate.combined_0.3$treatment)
integrate.combined_0.3$treatment <- gsub("Uninf", "No Infection", integrate.combined_0.3$treatment)
integrate.combined_0.3$treatment <- factor(integrate.combined_0.3$treatment, levels = c("No Infection","Infection"))
##cluster marker dotplot
sample.markers_res0.3 <- FindAllMarkers(integrate.combined_0.3, min.pct = 0.25, logfc.threshold = 0.25, test.use = "MAST")
sample.markers_res0.3_pos <- sample.markers_res0.3[sample.markers_res0.3$avg_logFC >0,]
sample.markers_list_res0.3_pos_top <- sample.markers_res0.3_pos %>% group_by(cluster) %>% top_n(n = 3, wt = avg_logFC)
write.csv(sample.markers_res0.3, file = "sample.markers_res0.3.csv")
write.csv(sample.markers_list_res0.3_pos, file = "sample.markers_list_res0.3_pos.csv")
write.csv(sample.markers_list_res0.3_pos_top, file = "sample.markers_list_res0.3_pos_top.csv")
DotPlot(integrate.combined_0.3, features = sample.markers_list_res0.3_pos_top$gene)
##cell cycle and plasmatocyte markers
DotPlot(integrate.combined_0.3, features = c("FBgn0003124","FBgn0003525","FBgn0261385", "FBgn0259896", "FBgn0029167", "FBgn0243514", "FBgn0011828", "FBgn0000299")) +
scale_x_discrete(labels=c("Col4a1","Pxn","eater","Hml","NimC1", "scra","stg","polo")) +
theme(axis.text.x = element_text(face = "italic"))+
ylab("Cluster")+
xlab("")
###basic umap marker plots
jpeg("integrate.combined_0.3_umap.jpeg", width = 750, height = 350)
DimPlot(integrate.combined_0.3, reduction = "umap",split.by = "poptreat", label = TRUE)
dev.off()
##lam markers from evans 2014 suggest cluster 1,2,4,5 have variable lam expression
lam_markers <- c("FBgn0034005","FBgn0032422","FBgn0004657","FBgn0261363")
lam_markers_name <- c("ItgaPS4", "atilla", "mys", "PPO3")
lam_markers_plot_unmod <- list()
for (l in 1:length(lam_markers)){
lam_markers_plot_unmod[[l]] <- VlnPlot(object = integrate.combined_0.3, features = lam_markers[l], pt.size = 0) +
xlab("") +
ggtitle(lam_markers_name[l])+
NoLegend()+
theme(plot.title = element_text(size=14, face="bold.italic"))
}
do.call("grid.arrange", c(lam_markers_plot_unmod, ncol=2))
####SECTION 6 - ROUND 3 CLUSTERING WITH CELL CYCLE CORRECTION ########
integrate.combined_cellcycle <- ScaleData(integrate.combined, vars.to.regress = c("nCount_RNA","nFeature_RNA","percent.mt", "CC.Difference"), features = rownames(integrate.combined))
integrate.combined_cellcycle <- RunPCA(integrate.combined_cellcycle, npcs = 50, verbose = FALSE)
integrate.combined_cellcycle <- RunUMAP(integrate.combined_cellcycle, reduction = "pca", dims = 1:50)
integrate.combined_cellcycle <- FindNeighbors(integrate.combined_cellcycle, reduction = "pca", dims = 1:50)
integrate.combined_cellcycle_0.3 <- FindClusters(integrate.combined_cellcycle, resolution = 0.3)
integrate.combined_cellcycle_0.3$poptreat <- gsub("control Infected", "No Parasitism, Infection", integrate.combined_cellcycle_0.3$poptreat)
integrate.combined_cellcycle_0.3$poptreat <- gsub("control Uninf", "No Parasitism, No Infection", integrate.combined_cellcycle_0.3$poptreat)
integrate.combined_cellcycle_0.3$poptreat <- gsub("NSRef Infected", "High Parasitism, Infection", integrate.combined_cellcycle_0.3$poptreat)
integrate.combined_cellcycle_0.3$poptreat <- gsub("NSRef Uninf", "High Parasitism, No Infection", integrate.combined_cellcycle_0.3$poptreat)
integrate.combined_cellcycle_0.3$poptreat <- factor(integrate.combined_cellcycle_0.3$poptreat, levels = c("No Parasitism, No Infection", "No Parasitism, Infection", "High Parasitism, No Infection", "High Parasitism, Infection"))
integrate.combined_cellcycle_0.3$population <- gsub("control", "No Parasitism", integrate.combined_cellcycle_0.3$population)
integrate.combined_cellcycle_0.3$population <- gsub("NSRef", "High Parasitism", integrate.combined_cellcycle_0.3$population)
integrate.combined_cellcycle_0.3$population <- factor(integrate.combined_cellcycle_0.3$population, levels = c("No Parasitism","High Parasitism"))
integrate.combined_cellcycle_0.3$treatment <- gsub("Infected", "Infection", integrate.combined_cellcycle_0.3$treatment)
integrate.combined_cellcycle_0.3$treatment <- gsub("Uninf", "No Infection", integrate.combined_cellcycle_0.3$treatment)
integrate.combined_cellcycle_0.3$treatment <- factor(integrate.combined_cellcycle_0.3$treatment, levels = c("No Infection","Infection"))
###basic umap marker plots
jpeg("integrate.combined_cellcycle_0.3_umap.jpeg", width = 750, height = 350)
DimPlot(integrate.combined_cellcycle_0.3, reduction = "umap",split.by = "poptreat", label = TRUE)
dev.off()
##compare to integrate.combined
cellscompare <- ""
for (i in 0:(length(table(Idents(integrate.combined_cellcycle_0.3)))-1)){
tempcells <- WhichCells(integrate.combined_cellcycle_0.3, idents = i)
tempcells_data <- subset(integrate.combined_0.3, cells = tempcells)
celllist <- Idents(tempcells_data)
celllist <- factor(celllist, levels = 0:(length(table(Idents(integrate.combined_0.3)))-1))
cellscompare <- rbind(cellscompare,table(celllist))
}
cellscompare <- cellscompare[-1,]
rownames(cellscompare) <- 0:(length(table(Idents(integrate.combined_cellcycle_0.3)))-1)
write.csv(cellscompare, file = "row_cc_0.3_col_all_0.3.csv", quote = F)
####SECTION 7 - DATA INTEGRATION FOR ESTIMATING MEAN FOLD CHANGE ACROSS DETECTED GENES########
integrat.list_15000f <- list(C1_Inf_obj, C1_Uninf_obj,C3_Inf_obj, C3_Uninf_obj,NSRef_1_Inf_obj, NSRef_1_Uninf_obj,NSRef_3_Inf_obj, NSRef_3_Uninf_obj)
names(integrat.list_15000f)=c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")
for (i in 1:length(x = integrat.list_15000f)) {
integrat.list_15000f[[i]] <- NormalizeData(object = integrat.list_15000f[[i]], verbose = FALSE)
integrat.list_15000f[[i]] <- FindVariableFeatures(object = integrat.list_15000f[[i]], selection.method = "vst", nfeatures = 15000, verbose = TRUE)
}
reference.list_15000f <- integrat.list_15000f[c("C1_Inf_obj", "C1_Uninf_obj","C3_Inf_obj", "C3_Uninf_obj","NSRef_1_Inf_obj", "NSRef_1_Uninf_obj","NSRef_3_Inf_obj", "NSRef_3_Uninf_obj")]
integrate.anchors_15000f <- FindIntegrationAnchors(object.list = reference.list_15000f, dims = 1:50, anchor.features = 15000)
integrate.combined_15000f <- IntegrateData(anchorset = integrate.anchors_15000f, dims = 1:50)
integrate.combined_15000f <- subset(integrate.combined_15000f, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
DefaultAssay(integrate.combined_15000f) <- "integrated"
s.genes <- read.csv(file = "s.genes.fly.csv")
g2m.genes <- read.csv(file = "g2m.genes.fly.csv")
s.genes <- as.character(s.genes$FlyBaseID)
g2m.genes <- as.character(g2m.genes$FlyBaseID)
s.genes <- s.genes[s.genes %in% rownames(integrate.combined_15000f)]
g2m.genes <- g2m.genes[g2m.genes %in% rownames(integrate.combined_15000f)]
integrate.combined_15000f <- CellCycleScoring(integrate.combined_15000f, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
integrate.combined_15000f$CC.Difference <- integrate.combined_15000f$S.Score - integrate.combined_15000f$G2M.Score
integrate.combined_15000f$treatment <- integrate.combined_15000f$orig.ident
integrate.combined_15000f$treatment <- gsub("NSRef-._","",integrate.combined_15000f$treatment)
integrate.combined_15000f$treatment <- gsub("C._","",integrate.combined_15000f$treatment)
integrate.combined_15000f$treatment <- gsub("Inf","Infected",integrate.combined_15000f$treatment)
integrate.combined_15000f$population <- integrate.combined_15000f$orig.ident
integrate.combined_15000f$population <- gsub("_Uninf","",integrate.combined_15000f$population)
integrate.combined_15000f$population <- gsub("_Inf","",integrate.combined_15000f$population)
integrate.combined_15000f$population <- gsub("-","",integrate.combined_15000f$population)
integrate.combined_15000f$population <- gsub(".$","",integrate.combined_15000f$population)
integrate.combined_15000f$population <- gsub("C","control",integrate.combined_15000f$population)
integrate.combined_15000f$poptreat <- paste(integrate.combined_15000f$population,integrate.combined_15000f$treatment)
integrate.combined_15000f <- ScaleData(integrate.combined_15000f, vars.to.regress = c("nCount_RNA","nFeature_RNA","percent.mt"), features = rownames(integrate.combined_15000f))
integrate.combined_15000f <- RunPCA(integrate.combined_15000f, npcs = 50, verbose = FALSE)
integrate.combined_15000f <- RunUMAP(integrate.combined_15000f, reduction = "pca", dims = 1:50)
integrate.combined_15000f <- FindNeighbors(integrate.combined_15000f, reduction = "pca", dims = 1:50)
integrate.combined_15000f_identschange <- integrate.combined_15000f
integrate.combined_15000f_identschange <- subset(integrate.combined_15000f_identschange, treatment == "Uninf")
Idents(integrate.combined_15000f_identschange) <- integrate.combined_15000f_identschange$population
Nsref_v_control_15000f_markers <- FindMarkers(integrate.combined_15000f_identschange, ident.1 = "control", ident.2 = "NSRef",min.pct = 0, logfc.threshold = 0, test.use = "MAST")
Nsref_v_control_15000f_markers$gene <- rownames(Nsref_v_control_15000f_markers)
integrate.combined_15000f_identschange2 <- integrate.combined_15000f
integrate.combined_15000f_identschange2 <- subset(integrate.combined_15000f_identschange2, population == "control")
Idents(integrate.combined_15000f_identschange2) <- integrate.combined_15000f_identschange2$treatment
control_Inf_v_uninf_15000f_markers <- FindMarkers(integrate.combined_15000f_identschange2, ident.1 = "Uninf", ident.2 = "Infected", min.pct = 0, logfc.threshold = 0, test.use = "MAST")
control_Inf_v_uninf_15000f_markers$gene <- rownames(control_Inf_v_uninf_15000f_markers)
integrate.combined_15000f_identschange3 <- integrate.combined_15000f
integrate.combined_15000f_identschange3 <- subset(integrate.combined_15000f_identschange3, population == "NSRef")
Idents(integrate.combined_15000f_identschange3) <- integrate.combined_15000f_identschange3$treatment
wasp_Inf_v_uninf_15000f_markers <- FindMarkers(integrate.combined_15000f_identschange3, ident.1 = "Uninf", ident.2 = "Infected", min.pct = 0, logfc.threshold = 0, test.use = "MAST")
wasp_Inf_v_uninf_15000f_markers$gene <- rownames(wasp_Inf_v_uninf_15000f_markers)
selvinfection_15000f <- inner_join(control_Inf_v_uninf_15000f_markers,Nsref_v_control_15000f_markers, by =c("gene"="gene"))
infvuninfec_15000f <- inner_join(control_Inf_v_uninf_15000f_markers,wasp_Inf_v_uninf_15000f_markers, by =c("gene"="gene"))
infvuninfec_15000f$avg_logFC.x <- infvuninfec_15000f$avg_logFC.x*-1.44269407046
infvuninfec_15000f$avg_logFC.y <- infvuninfec_15000f$avg_logFC.y*-1.44269407046
selvinfection_15000f$avg_logFC.x <- selvinfection_15000f$avg_logFC.x*-1.44269407046
selvinfection_15000f$avg_logFC.y <- selvinfection_15000f$avg_logFC.y*-1.44269407046
##sel, inf vs no sel, no inf
integrate.combined_15000f_identschange4 <- integrate.combined_15000f
integrate.combined_15000f_identschange4 <- subset(integrate.combined_15000f_identschange4, subset = poptreat == c("NSRef Infected","control Uninf"))
Idents(integrate.combined_15000f_identschange4) <- integrate.combined_15000f_identschange4$treatment
Sel_Inf_v_NoSel_Uninf_15000f_markers <- FindMarkers(integrate.combined_15000f_identschange4, ident.1 = "Uninf", ident.2 = "Infected", min.pct = 0, logfc.threshold = 0, test.use = "MAST")
Sel_Inf_v_NoSel_Uninf_15000f_markers$gene <- rownames(Sel_Inf_v_NoSel_Uninf_15000f_markers)
selinfvnoseluninfec_15000f <- inner_join(control_Inf_v_uninf_15000f_markers,Sel_Inf_v_NoSel_Uninf_15000f_markers, by =c("gene"="gene"))
selinfvnoseluninfec_15000f$avg_logFC.x <- selinfvnoseluninfec_15000f$avg_logFC.x*-1.44269407046
selinfvnoseluninfec_15000f$avg_logFC.y <- selinfvnoseluninfec_15000f$avg_logFC.y*-1.44269407046
#write table of logFC
logfc_allgenes <- cbind(infvuninfec_15000f$gene,selvinfection_15000f$avg_logFC.x,selvinfection_15000f$avg_logFC.y,infvuninfec_15000f$avg_logFC.y,selinfvnoseluninfec_15000f$avg_logFC.y)
colnames(logfc_allgenes) <- c("Gene","Infection only FC","High Parasitism only FC","Infection FC in selected populations", "High Parasitism and Infection FC")
write.csv(logfc_allgenes, file = "logfc_allgenes.csv")
##Figure 2 plots
logfc_allgenes <- read.csv(file = "logfc_allgenes2.csv", row.names = 1)
d=data.frame(x1=c(1,1,2,2), x2=c(2,2,3,3), y1=c(1,2,1,2), y2=c(2,3,2,3), c=c('a','a','b','b'), t=c('a','b','a','b'))
rec <- ggplot() +
geom_rect(data=d, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2), color = NA, fill = "white", size=0.5, linetype = 3) +
geom_text(data=d, aes(label = "No Infection", x = 1.5, y = 3.2), size = 4) +
geom_text(data=d, aes(label = "Infection", x = 2.5, y = 3.2), size = 4) +
geom_text(data=d, aes(label = "No Parasitism", x = 0.8, y = 2.5, angle = 90,), size = 4) +
geom_text(data=d, aes(label = "High Parasitism", x = 0.8, y = 1.5, angle = 90), size = 4) +
geom_text(data=d, aes(label = "Selection regime", x = 0.6, y = 2, angle = 90, fontface = "bold"), size = 4) +
geom_text(data=d, aes(label = "Treatment", x = 2, y = 3.4, fontface = "bold"), size = 4) +
geom_segment(aes(x=1.4,y=2.4,xend=1.4,yend=1.4),arrow = arrow(length = unit(0.03, "npc"), ends = "both"),size=1.3)+
geom_segment(aes(x=1.5,y=2.5,xend=2.5,yend=1.5),arrow = arrow(length = unit(0.03, "npc"), ends = "both"),size=1.3)+
geom_segment(aes(x=1.5,y=2.6,xend=2.5,yend=2.6),arrow = arrow(length = unit(0.03, "npc"), ends = "both"),size=1.3)+
geom_segment(aes(x=1,y=1,xend=1,yend=2.8))+
geom_segment(aes(x=1.2,y=3,xend=3,yend=3))+
geom_text(aes(x = 1.2, y = 1.9,angle = 90,label = "Panel B, Y axis\n2: Evolved Constitutive response"), size = 4)+
geom_text(aes(x = 2, y = 2.8,label = "X axes\n1: Induced by Infection"), size = 4)+
geom_text(aes(x = 2.15, y = 2.15,angle = 312,label = "Panel C, Y axis\n3: Induced + Constitutive"), size = 4)+
theme(axis.title=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank())+
theme(
panel.background = element_rect(fill = "transparent"), # bg of the panel
plot.background = element_rect(fill = "transparent", color = NA), # bg of the plot
panel.grid.major = element_blank(), # get rid of major grid
panel.grid.minor = element_blank(), # get rid of minor grid
legend.background = element_rect(fill = "transparent"), # get rid of legend bg
legend.box.background = element_rect(fill = "transparent") # get rid of legend panel bg
)+
theme(legend.position = "none")
##atilla and ppo3
pdf("bulkgeneexp_mod_5.pdf",width = 12, height = 4.4)
par(mfrow=c(1,3))
par(mar=c(5,6,4,1)+0.8)
#par(mgp=c(4,1,0))
plot.new()
title("A", adj = 0, cex.main=2)
heatscatter(logfc_allgenes$Infection.only.FC,logfc_allgenes$Selection.only.FC, ylab =expression(' 2: Constitutive Expression (log2 FC)\n (Evolved with High Parasitism)'),xlab ="", main = "", bty="L", cex.lab=1.6, cex.axis=1.5, xlim= c(-0.9,1.5), ylim=c(-1.1,1.3))
abline(coef = c(0,1))
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Selection.only.FC,
pch = 16, col = "black")
text("atilla", font = 3, x = 0.18+logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Selection.only.FC, cex = 1.3)
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Selection.only.FC,
pch = 16, col = "black")
text("PPO3", font = 3, x = -0.04+logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Infection.only.FC,
y = 0.12+logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Selection.only.FC, cex = 1.3)
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Selection.only.FC,
pch = 16, col = "black")
text("Treh", font = 3, x = -0.18+logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Selection.only.FC, cex = 1.3)
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Selection.only.FC,
pch = 16, col = "black")
text("Tret1-1", font = 3, x = -0.07+logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Infection.only.FC,
y = 0.08+logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Selection.only.FC, cex = 1.3)
title("B", adj = 0, cex.main=2)
heatscatter(logfc_allgenes$Infection.only.FC,logfc_allgenes$Selection.and.Infection.FC, ylab =expression(' 3: Constitutive + Induced Expression (log2 FC)\n (Evolved with High Parasitism)'),xlab ="", main = "", bty="L", cex.lab=1.6, cex.axis=1.5, xlim= c(-0.9,1.58), ylim=c(-1.1,1.3))
abline(coef = c(0,1))
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Selection.and.Infection.FC,
pch = 16, col = "black")
text("atilla", font = 3, x = -0.18+logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0032422",]$Selection.and.Infection.FC, cex = 1.3)
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Selection.and.Infection.FC,
pch = 16, col = "black")
text("PPO3", font = 3, x = -0.1+logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Infection.only.FC,
y = 0.1+logfc_allgenes[logfc_allgenes$Gene == "FBgn0261363",]$Selection.and.Infection.FC, cex = 1.3)
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Selection.and.Infection.FC,
pch = 16, col = "black")
text("Treh", font = 3, x = 0.13+logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Infection.only.FC,
y = -0.07+logfc_allgenes[logfc_allgenes$Gene == "FBgn0003748",]$Selection.and.Infection.FC, cex = 1.3)
points(x = logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Selection.and.Infection.FC,
pch = 16, col = "black")
text("Tret1-1", font = 3, x = 0.23+logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Infection.only.FC,
y = logfc_allgenes[logfc_allgenes$Gene == "FBgn0050035",]$Selection.and.Infection.FC, cex = 1.3)
title("C", adj = 0, cex.main=2)
mtext("1: Induced expression in populations evolved without parasitism (log2 fold change)",
side=1,outer=T,line=-2.2,cex=1.1, at = 0.68)
vp.Left <- viewport(height=grid::unit(0.9, "npc"), width=grid::unit(0.3, "npc"),
just=c("left","top"),
y=0.95, x=0.01)
print(rec, vp=vp.Left)
dev.off()
####SECTION 8 - CLUSTERING WITH ALL DETECTED ANCHORS########
integrate.combined_15000f_0.3 <- FindClusters(integrate.combined_15000f, resolution = 0.3)
integrate.combined_15000f_0.3$poptreat <- gsub("control Infected", "No Parasitism, Infection", integrate.combined_15000f_0.3$poptreat)
integrate.combined_15000f_0.3$poptreat <- gsub("control Uninf", "No Parasitism, No Infection", integrate.combined_15000f_0.3$poptreat)
integrate.combined_15000f_0.3$poptreat <- gsub("NSRef Infected", "High Parasitism, Infection", integrate.combined_15000f_0.3$poptreat)
integrate.combined_15000f_0.3$poptreat <- gsub("NSRef Uninf", "High Parasitism, No Infection", integrate.combined_15000f_0.3$poptreat)
integrate.combined_15000f_0.3$poptreat <- factor(integrate.combined_15000f_0.3$poptreat, levels = c("No Parasitism, No Infection", "No Parasitism, Infection", "High Parasitism, No Infection", "High Parasitism, Infection"))
integrate.combined_15000f_0.3$population <- gsub("control", "No Parasitism", integrate.combined_15000f_0.3$population)
integrate.combined_15000f_0.3$population <- gsub("NSRef", "High Parasitism", integrate.combined_15000f_0.3$population)
integrate.combined_15000f_0.3$population <- factor(integrate.combined_15000f_0.3$population, levels = c("No Parasitism","High Parasitism"))
integrate.combined_15000f_0.3$treatment <- gsub("Infected", "Infection", integrate.combined_15000f_0.3$treatment)
integrate.combined_15000f_0.3$treatment <- gsub("Uninf", "No Infection", integrate.combined_15000f_0.3$treatment)
integrate.combined_15000f_0.3$treatment <- factor(integrate.combined_15000f_0.3$treatment, levels = c("No Infection","Infection"))
integrate.combined_cellcount <- as.data.frame(table(paste(integrate.combined_15000f_0.3$poptreat,integrate.combined_15000f_0.3$seurat_clusters,sep="_")))
integrate.combined_cellcount <- integrate.combined_cellcount %>% separate(Var1, into= c("Treatment","Cluster"),sep="_")
propall <- vector()
for (t in levels(as.factor(integrate.combined_cellcount$Treatment))){
hold <- integrate.combined_cellcount[integrate.combined_cellcount$Treatment == t,]
prop <- hold$Freq/sum(hold$Freq)
propall <- c(propall,prop)
}
integrate.combined_cellcount$Prop <- propall
integrate.combined_cellcount$Treatment <- factor(integrate.combined_cellcount$Treatment, levels = c("No Parasitism, No Infection", "No Parasitism, Infection", "High Parasitism, No Infection", "High Parasitism, Infection"))
integrate.combined_cellcount <- separate(integrate.combined_cellcount, Treatment, into = c("Parasitism", "Infection"), sep=", ", remove = F)
integrate.combined_cellcount$Parasitism <- as.factor(integrate.combined_cellcount$Parasitism)
integrate.combined_cellcount$Infection <- factor(integrate.combined_cellcount$Infection, levels = c("No Infection", "Infection"))
clusterplot15000 <- ggplot(data=integrate.combined_cellcount, aes(x=Treatment, y=Prop, fill = Parasitism, alpha = Infection)) +
geom_bar(stat="identity")+
facet_wrap(~ as.numeric(Cluster), ncol=4,scales = "free")+
xlab("")+
ylab("Proportion of cells") +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()) +
scale_alpha_manual(name = "", values = c(0.5,1), guide = guide_legend(override.aes = list(fill = c("#0072b2", "#d55e00"), alpha = 0.5)))+
scale_fill_manual(name = "", values = c("#0072b2", "#d55e00")) +
theme(legend.position="none")
d=data.frame(x1=c(2.3,3.2,2.3,3.2), x2=c(2.5,3.4,2.5,3.4), y1=c(2,2,3,3), y2=c(2.8,2.8,3.8,3.8), t=c('a','b','a','b'), r=c('c','c','d','d'),s=c('','','No \nParasitism','High \nParasitism'),u=c('Infection','','No Infection',''))
exptlegend <- ggplot() +
geom_rect(data=d, mapping=aes(xmin=x1, xmax=x2, ymin=y1, ymax=y2, fill=t,alpha=r))+
scale_alpha_manual(name = "", values = c(1,0.5))+
scale_fill_manual(name = "", values = c("#0072b2","#d55e00"))+
theme_void()+
theme(legend.position="none")+
geom_text(data=d, aes(x=x1+(x2-x1)/2, y=(y1+(y2-y1)/2)+1.5, label=s), size=4)+
geom_text(data=d, aes(x=(x1+(x2-x1)/2)-0.8, y=y1+(y2-y1)/2, label=u), size=4)+
xlim(0.8,4)+
ylim(0.8,6)+
ggtitle("Selection Regime")+
theme(plot.title = element_text(hjust = 0.72, vjust = 0, face="bold"))
gnew15000 <- ggdraw(clusterplot15000) + draw_plot(exptlegend,x = 0.72, y = 0, width = 0.3, height = 0.25)
####SECTION 9 - ROUND 1 LAMELLOCYTE SUBCLUSTERING, MOVING MISCLASSIFIED CRYSTAL CELLS ########
mod <- subset(integrate.combined_0.3, idents = c(0,1,2,4,5))
cells_lm <- gsub("_.","",colnames(mod))
cells_lm <- cells_lm[!(duplicated(cells_lm) | duplicated(cells_lm, fromLast = TRUE)) ]
C1_Inf_obj_lm <- subset(C1_Inf_obj,cells = cells_lm)
C1_Uninf_obj_lm <- subset(C1_Uninf_obj,cells = cells_lm)
C3_Inf_obj_lm <- subset(C3_Inf_obj,cells = cells_lm)
C3_Uninf_obj_lm <- subset(C3_Uninf_obj,cells = cells_lm)
NSRef_1_Inf_obj_lm <- subset(NSRef_1_Inf_obj,cells = cells_lm)
NSRef_1_Uninf_obj_lm <- subset(NSRef_1_Uninf_obj,cells = cells_lm)
NSRef_3_Inf_obj_lm <- subset(NSRef_3_Inf_obj,cells = cells_lm)
NSRef_3_Uninf_obj_lm <- subset(NSRef_3_Uninf_obj,cells = cells_lm)
integrate.list_lm <- list(C1_Inf_obj_lm, C1_Uninf_obj_lm, C3_Inf_obj_lm, C3_Uninf_obj_lm, NSRef_1_Inf_obj_lm, NSRef_1_Uninf_obj_lm,NSRef_3_Inf_obj_lm, NSRef_3_Uninf_obj_lm)
names(integrate.list_lm)=c("C1_Inf_obj_lm","C1_Uninf_obj_lm", "C3_Inf_obj_lm","C3_Uninf_obj_lm", "NSRef_1_Inf_obj_lm", "NSRef_1_Uninf_obj_lm","NSRef_3_Inf_obj_lm", "NSRef_3_Uninf_obj_lm")
k.filter <- min(200, min(sapply(integrate.list_lm, ncol)))
for (i in 1:length(x = integrate.list_lm)) {
integrate.list_lm[[i]] <- NormalizeData(object = integrate.list_lm[[i]], verbose = TRUE)
integrate.list_lm[[i]] <- FindVariableFeatures(object = integrate.list_lm[[i]], selection.method = "vst", nfeatures = 2000, verbose = TRUE)
}
reference.list_lm <- integrate.list_lm[c("C1_Inf_obj_lm","C1_Uninf_obj_lm","C3_Inf_obj_lm","C3_Uninf_obj_lm","NSRef_1_Inf_obj_lm", "NSRef_1_Uninf_obj_lm","NSRef_3_Inf_obj_lm", "NSRef_3_Uninf_obj_lm")]
integrate.anchors_lm <- FindIntegrationAnchors(object.list = reference.list_lm, dims = 1:50, anchor.features = 2000, k.filter = k.filter)
integrate.combined_lm <- IntegrateData(anchorset = integrate.anchors_lm, dims = 1:50)
integrate.combined_lm <- subset(integrate.combined_lm, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
DefaultAssay(integrate.combined_lm) <- "integrated"
s.genes <- read.csv(file = "s.genes.fly.csv")
g2m.genes <- read.csv(file = "g2m.genes.fly.csv")
s.genes <- as.character(s.genes$FlyBaseID)
g2m.genes <- as.character(g2m.genes$FlyBaseID)
s.genes <- s.genes[s.genes %in% rownames(integrate.combined_lm)]
g2m.genes <- g2m.genes[g2m.genes %in% rownames(integrate.combined_lm)]
integrate.combined_lm <- CellCycleScoring(integrate.combined_lm, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
integrate.combined_lm$CC.Difference <- integrate.combined_lm$S.Score - integrate.combined_lm$G2M.Score
integrate.combined_lm <- ScaleData(integrate.combined_lm, vars.to.regress = c("nCount_RNA","nFeature_RNA","percent.mt"), features = rownames(integrate.combined_lm))
integrate.combined_lm <- RunPCA(integrate.combined_lm, npcs = 50, verbose = FALSE)
integrate.combined_lm <- RunUMAP(integrate.combined_lm, reduction = "pca", dims = 1:50)
integrate.combined_lm <- FindNeighbors(integrate.combined_lm, reduction = "pca", dims = 1:50)
integrate.combined_lm$treatment <- integrate.combined_lm$orig.ident
integrate.combined_lm$treatment <- gsub("NSRef-._","",integrate.combined_lm$treatment)
integrate.combined_lm$treatment <- gsub("C._","",integrate.combined_lm$treatment)
integrate.combined_lm$treatment <- gsub("Inf","Infected",integrate.combined_lm$treatment)
integrate.combined_lm$population <- integrate.combined_lm$orig.ident
integrate.combined_lm$population <- gsub("_Uninf","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub("_Inf","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub("-","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub(".$","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub("C","control",integrate.combined_lm$population)
integrate.combined_lm$poptreat <- paste(integrate.combined_lm$population,integrate.combined_lm$treatment)
integrate.combined_lm_0.3 <- FindClusters(integrate.combined_lm, resolution = 0.3)
DotPlot(integrate.combined_lm_0.3_round1, features = sample.markers_list_res0.3_pos_top[sample.markers_list_res0.3_pos_top$cluster %in% c(3,6,7,8),]$gene)
DotPlot(integrate.combined_lm_0.3_round1, features = c("FBgn0033367", "FBgn0283437")) +
scale_x_discrete(labels=rev(c("PPO2", "PPO1")))+
theme(axis.text.x = element_text(face = "italic"))+
ylab("Cluster")+
xlab("")
sample.markers_lm_round1_0.3 <- FindAllMarkers(integrate.combined_lm_0.3_round1, min.pct = 0.25, logfc.threshold = 0.25, test.use = "MAST", only.pos = T)
write.csv(sample.markers_lm_round1_0.3, file = "sample.markers_lm_round1_0.3.csv")
##get crystal cells from round 1
integrate.combined_0.3_cc <- subset(integrate.combined_lm_0.3, idents = 5)
integrate.combined_lm_0.3_round1 <- integrate.combined_lm_0.3
cc_misclass <- factor(rep("CC",length(colnames(integrate.combined_0.3_cc))), levels = "CC")
names(cc_misclass) <- colnames(integrate.combined_0.3_cc)
####SECTION 10 - ROUND 2 LAMELLOCYTE SUBCLUSTERING ########
mod2 <- subset(integrate.combined_lm_0.3, idents = c(0:4))
cells_lm <- gsub("_.","",colnames(mod2))
cells_lm <- cells_lm[!(duplicated(cells_lm) | duplicated(cells_lm, fromLast = TRUE)) ]
C1_Inf_obj_lm <- subset(C1_Inf_obj,cells = cells_lm)
C1_Uninf_obj_lm <- subset(C1_Uninf_obj,cells = cells_lm)
C3_Inf_obj_lm <- subset(C3_Inf_obj,cells = cells_lm)
C3_Uninf_obj_lm <- subset(C3_Uninf_obj,cells = cells_lm)
NSRef_1_Inf_obj_lm <- subset(NSRef_1_Inf_obj,cells = cells_lm)
NSRef_1_Uninf_obj_lm <- subset(NSRef_1_Uninf_obj,cells = cells_lm)
NSRef_3_Inf_obj_lm <- subset(NSRef_3_Inf_obj,cells = cells_lm)
NSRef_3_Uninf_obj_lm <- subset(NSRef_3_Uninf_obj,cells = cells_lm)
integrate.list_lm <- list(C1_Inf_obj_lm, C1_Uninf_obj_lm, C3_Inf_obj_lm, C3_Uninf_obj_lm, NSRef_1_Inf_obj_lm, NSRef_1_Uninf_obj_lm,NSRef_3_Inf_obj_lm, NSRef_3_Uninf_obj_lm)
names(integrate.list_lm)=c("C1_Inf_obj_lm","C1_Uninf_obj_lm", "C3_Inf_obj_lm","C3_Uninf_obj_lm", "NSRef_1_Inf_obj_lm", "NSRef_1_Uninf_obj_lm","NSRef_3_Inf_obj_lm", "NSRef_3_Uninf_obj_lm")
k.filter <- min(200, min(sapply(integrate.list_lm, ncol)))
for (i in 1:length(x = integrate.list_lm)) {
integrate.list_lm[[i]] <- NormalizeData(object = integrate.list_lm[[i]], verbose = TRUE)
integrate.list_lm[[i]] <- FindVariableFeatures(object = integrate.list_lm[[i]], selection.method = "vst", nfeatures = 2000, verbose = TRUE)
}
reference.list_lm <- integrate.list_lm[c("C1_Inf_obj_lm","C1_Uninf_obj_lm","C3_Inf_obj_lm","C3_Uninf_obj_lm","NSRef_1_Inf_obj_lm", "NSRef_1_Uninf_obj_lm","NSRef_3_Inf_obj_lm", "NSRef_3_Uninf_obj_lm")]
integrate.anchors_lm <- FindIntegrationAnchors(object.list = reference.list_lm, dims = 1:50, anchor.features = 2000, k.filter = k.filter)
integrate.combined_lm <- IntegrateData(anchorset = integrate.anchors_lm, dims = 1:50)
integrate.combined_lm <- subset(integrate.combined_lm, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
DefaultAssay(integrate.combined_lm) <- "integrated"
s.genes <- read.csv(file = "s.genes.fly.csv")
g2m.genes <- read.csv(file = "g2m.genes.fly.csv")
s.genes <- as.character(s.genes$FlyBaseID)
g2m.genes <- as.character(g2m.genes$FlyBaseID)
s.genes <- s.genes[s.genes %in% rownames(integrate.combined_lm)]
g2m.genes <- g2m.genes[g2m.genes %in% rownames(integrate.combined_lm)]
integrate.combined_lm <- CellCycleScoring(integrate.combined_lm, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE)
integrate.combined_lm$CC.Difference <- integrate.combined_lm$S.Score - integrate.combined_lm$G2M.Score
integrate.combined_lm <- ScaleData(integrate.combined_lm, vars.to.regress = c("nCount_RNA","nFeature_RNA","percent.mt"), features = rownames(integrate.combined_lm))
integrate.combined_lm <- RunPCA(integrate.combined_lm, npcs = 50, verbose = FALSE)
integrate.combined_lm <- RunUMAP(integrate.combined_lm, reduction = "pca", dims = 1:50)
integrate.combined_lm <- FindNeighbors(integrate.combined_lm, reduction = "pca", dims = 1:50)
integrate.combined_lm$treatment <- integrate.combined_lm$orig.ident
integrate.combined_lm$treatment <- gsub("NSRef-._","",integrate.combined_lm$treatment)
integrate.combined_lm$treatment <- gsub("C._","",integrate.combined_lm$treatment)
integrate.combined_lm$treatment <- gsub("Inf","Infected",integrate.combined_lm$treatment)
integrate.combined_lm$population <- integrate.combined_lm$orig.ident
integrate.combined_lm$population <- gsub("_Uninf","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub("_Inf","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub("-","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub(".$","",integrate.combined_lm$population)
integrate.combined_lm$population <- gsub("C","control",integrate.combined_lm$population)
integrate.combined_lm$poptreat <- paste(integrate.combined_lm$population,integrate.combined_lm$treatment)
integrate.combined_lm_0.2 <- FindClusters(integrate.combined_lm, resolution = 0.2)
integrate.combined_lm_0.2$poptreat <- gsub("control Infected", "No Parasitism, Infection", integrate.combined_lm_0.2$poptreat)
integrate.combined_lm_0.2$poptreat <- gsub("control Uninf", "No Parasitism, No Infection", integrate.combined_lm_0.2$poptreat)
integrate.combined_lm_0.2$poptreat <- gsub("NSRef Infected", "High Parasitism, Infection", integrate.combined_lm_0.2$poptreat)
integrate.combined_lm_0.2$poptreat <- gsub("NSRef Uninf", "High Parasitism, No Infection", integrate.combined_lm_0.2$poptreat)
integrate.combined_lm_0.2$poptreat <- factor(integrate.combined_lm_0.2$poptreat, levels = c("No Parasitism, No Infection", "No Parasitism, Infection", "High Parasitism, No Infection", "High Parasitism, Infection"))
integrate.combined_lm_0.2$population <- gsub("control", "No Parasitism", integrate.combined_lm_0.2$population)
integrate.combined_lm_0.2$population <- gsub("NSRef", "High Parasitism", integrate.combined_lm_0.2$population)
integrate.combined_lm_0.2$population <- factor(integrate.combined_lm_0.2$population, levels = c("No Parasitism","High Parasitism"))
integrate.combined_lm_0.2$treatment <- gsub("Infected", "Infection", integrate.combined_lm_0.2$treatment)
integrate.combined_lm_0.2$treatment <- gsub("Uninf", "No Infection", integrate.combined_lm_0.2$treatment)
integrate.combined_lm_0.2$treatment <- factor(integrate.combined_lm_0.2$treatment, levels = c("No Infection","Infection"))
lmcellsids <- integrate.combined_lm_0.2$seurat_clusters
lmcellsids <- mapvalues(lmcellsids, from = c("0","1","2","3"), to = c("PLASM1","LAM2","LAM3","LAM1"))
lmcellsids <- factor(lmcellsids, levels = c("PLASM1","LAM1","LAM2","LAM3"))
integrate.combined_lm_0.2$seurat_clusters <- lmcellsids
Idents(integrate.combined_lm_0.2) <- lmcellsids
lm_with_cc <- factor(c(as.character(unlist(lmcellsids)), as.character(unlist(cc_misclass))), levels = c("PLASM1","LAM1","LAM2","LAM3","LAM4","CC"))
names(lm_with_cc) <- c(names(lmcellsids),names(cc_misclass))
##check lm subcluster against top markers from all cells
DotPlot(integrate.combined_lm_0.2, features = sample.markers_list_res0.3_pos_top[sample.markers_list_res0.3_pos_top$cluster %in% c(3,6,7,8),]$gene)
DotPlot(integrate.combined_lm_0.2, features = sample.markers_list_res0.3_pos_top[sample.markers_list_res0.3_pos_top$cluster %in% c(0,1,2,4,5),]$gene)
DotPlot(integrate.combined_lm_0.2, features = sample.markers_list_res0.3_pos_top[sample.markers_list_res0.3_pos_top$cluster %in% c(0,1,2,4,5),]$gene)
###lamellocyte markers dotplot
DotPlot(object = integrate.combined_lm_0.2, features = c("FBgn0034005","FBgn0004657","FBgn0032422","FBgn0261363")) +
scale_x_discrete(labels=rev(c("PPO3", "atilla", "mys", "ItgaPS4")))
##compare lm to integrate.combined
cellscompare <- ""
clustersnames <- names(table(Idents(integrate.combined_lm_0.2)))
for (i in 1:(length(table(Idents(integrate.combined_lm_0.2))))){
tempcells <- WhichCells(integrate.combined_lm_0.2, idents = clustersnames[i])
tempcells_data <- subset(integrate.combined_0.3, cells = tempcells)
celllist <- Idents(tempcells_data)
celllist <- factor(celllist, levels = 0:(length(table(Idents(integrate.combined_0.3)))-1))
cellscompare <- rbind(cellscompare,table(celllist))
}
cellscompare <- cellscompare[-1,]
rownames(cellscompare) <- clustersnames
cellscompare
write.csv(cellscompare, file = "row_lm_0.2_col_all_0.3.csv")
##cluster phase
phase_table <- table(integrate.combined_lm_0.2$seurat_clusters,integrate.combined_lm_0.2$Phase)
phase_table <- phase_table/rowSums(phase_table)
phase_table <- melt(phase_table)
colnames(phase_table) <- c("Cluster","Phase","Proportion")
lamphase <- ggplot(data=phase_table, aes(x=factor(Cluster,levels = c( "PLASM1","LAM1","LAM2","LAM3")), y=Proportion, fill = Phase)) +
geom_bar(stat="identity")+
xlab("Cluster")+
ylab("Proportion")
####SECTION 11 - TRAJECTORY INFERENCE FOR LAMELLOCYTE DIFFERENTIATION ########
sds_pca <- slingshot(Embeddings(integrate.combined_lm_0.2, "pca"), clusterLabels = integrate.combined_lm_0.2$seurat_clusters, start.clus = "PLASM1", stretch = 0)
integrate.combined_lm_0.2 <- FindVariableFeatures(object = integrate.combined_lm_0.2)
top_hvg <- HVFInfo(integrate.combined_lm_0.2) %>%
mutate(., bc = rownames(.)) %>%
arrange(desc(variance.standardized)) %>%
top_n(2000, variance.standardized) %>%
pull(bc)
# Prepare data for random forest
dat_use <- t(GetAssayData(integrate.combined_lm_0.2, slot = "scale.data")[top_hvg,])
dat_use_df <- cbind(slingPseudotime(sds_pca)[,1], dat_use)
colnames(dat_use_df)[1] <- "pseudotime"
dat_use_df <- as.data.frame(dat_use_df[!is.na(dat_use_df[,1]),])
model <- rand_forest(trees = 2000, min_n = 15, mode = "regression") %>%
set_engine("ranger", importance = "impurity", num.threads = 32) %>%
fit(pseudotime ~ ., data = dat_use_df)
impt_pval <- importance_pvalues(model$fit, method = "altmann",formula = pseudotime ~ ., data = dat_use_df)
impt_pval_df <- as.data.frame(impt_pval)
impt_pval_df$symbol <- mapIds(org.Dm.eg.db, rownames(impt_pval_df), column="SYMBOL", keytype="FLYBASE", multiVals="first")
impt_pval_df <- read.csv(file = "impt_pval_df.csv", row.names = 1)
impt_pval_df <- impt_pval_df[order(rownames(impt_pval_df)),]
aveexp <- read.csv(file = "aveexp.csv", row.names = 1)
aveexp <- aveexp[order(rownames(aveexp)),]
impt_pval_df <- cbind(impt_pval_df[1:2],impt_pval_df[4],aveexp[1:4])
impt_pval_df <- impt_pval_df[order(impt_pval_df$pvalue,-impt_pval_df$importance),]
write.csv(impt_pval_df, file = "var_imp_df_lm_lineage_2000hvg.csv")
##dimplot lm subcluster
lm_dimplot <- DimPlot(object = integrate.combined_lm_0.2, reduction = "umap",label = F, order = rev(c( "PLASM1","LAM1","LAM2","LAM3")), cols = c('#9970ab',"#F28848","#FCBC2A", "#F0F921"))
lm_dimplot_data <- lm_dimplot$data
lm_dimplot_data_mean <- aggregate(lm_dimplot_data[, 1:2], list(lm_dimplot_data$ident), mean)
lm_dimplot_lineage <- lm_dimplot +
geom_line(data = lm_dimplot_data_mean, aes(UMAP_1,UMAP_2), size = 1, arrow = arrow())+
#geom_point(data = lm_dimplot_data_mean, aes(UMAP_1,UMAP_2), size = 2)+
geom_line(data = lm_dimplot_data_mean[1:3,], aes(UMAP_1,UMAP_2), size = 1, arrow = arrow())+
geom_line(data = lm_dimplot_data_mean[1:2,], aes(UMAP_1,UMAP_2), size = 1, arrow = arrow())+
#geom_line(data = lm_dimplot_data_mean[1,], aes(UMAP_1,UMAP_2), size = 1, arrow = arrow())+
theme(legend.position="right")
#top lineage marker plot
integrate.combined_lm_0.2_forplot = SetAssayData(object = integrate.combined_lm_0.2, slot = "data", assay = "integrated", new.data = log2(exp(as.matrix(GetAssayData(object = integrate.combined_lm_0.2, slot = "data", assay = "integrated")))))
integrate.combined_lm_0.2_ave <- integrate.combined_lm_0.2_forplot
integrate.combined_lm_0.2_ave$seurat_clusters <- gsub("LAM3","LAM2",integrate.combined_lm_0.2_ave$seurat_clusters)
Idents(integrate.combined_lm_0.2_ave) <- integrate.combined_lm_0.2_ave$seurat_clusters
aveexp <- AverageExpression(object = integrate.combined_lm_0.2_ave, features = head(as.character(rownames(impt_pval_df)), n = 100), assays = "integrated", slot = "scale.data")
aveexp <- aveexp$integrated
aveexp <- aveexp[order(-aveexp$LAM2),]
scaleddata_forplot <- integrate.combined_lm_0.2_forplot@assays[["integrated"]]@scale.data
scaleddata_forplot <- scaleddata_forplot[,order(scaleddata_forplot[which(rownames(scaleddata_forplot) %in% rownames(impt_pval_df)[101]),])]
top100_part1<- DoHeatmap2(integrate.combined_lm_0.2_forplot, features = head(as.character(rownames(aveexp)), n = 50), angle = 0, size = 6.5, label = T, group.bar = T, draw.lines = T, group.by = "ident", cells = colnames(scaleddata_forplot), lines.width = 100, group.bar.height = 0, hjust = 0.5, slot = "scale.data") +
#ggtitle("Top 100 lineage markers") +
theme(axis.text.y = element_text(face="italic")) +
guides(color=FALSE) +
guides(fill=FALSE) +
theme(plot.title = element_text(size = 16, face = "bold"),
axis.text=element_text(size=14))+
scale_fill_gradientn(colours = rev(colorRampPalette(RColorBrewer::brewer.pal(11,"RdYlBu"))(256)),na.value = "white")
top100_part1$data$Feature<- mapvalues(top100_part1$data$Feature, from = head(as.character(rownames(aveexp)), n = 50), to = mapIds(org.Dm.eg.db, head(as.character(rownames(aveexp)), n = 50), column="SYMBOL", keytype="FLYBASE", multiVals="first"))
top100_part2<- DoHeatmap2(integrate.combined_lm_0.2_forplot, features = tail(as.character(rownames(aveexp)), n = 50), angle = 0, size = 6.5, label = T, group.bar = T, draw.lines = T, group.by = "ident", cells = colnames(scaleddata_forplot), lines.width = 100, group.bar.height = 0, hjust = 0.5, slot = "scale.data") +
#ggtitle("Top 50 lineage markers") +
theme(axis.text.y = element_text(face="italic")) +
guides(color=FALSE) +
guides(fill=guide_legend(title="Scaled \nLog (2) expression"))+
theme(plot.title = element_text(size = 16, face = "bold"),
axis.text=element_text(size=14))+
scale_fill_gradientn(colours = rev(colorRampPalette(RColorBrewer::brewer.pal(11,"RdYlBu"))(256)),na.value = "white")
top100_part2$data$Feature<- mapvalues(top100_part2$data$Feature, from = tail(as.character(rownames(aveexp)), n = 50), to = mapIds(org.Dm.eg.db, tail(as.character(rownames(aveexp)), n = 50), column="SYMBOL", keytype="FLYBASE", multiVals="first"))
title.grob <- textGrob("Top 100 lineage markers", gp=gpar(fontface="bold", col="black", fontsize=18))
top100markersplot <- grid.arrange(grobs = list(top100_part1,top100_part2), layout_matrix = t(matrix(c(1,1,1,1,1,2,2,2,2,2,2,2))), top = title.grob)
pdf(file = "top100lineagemarkers.pdf", height = 12, width = 14)
grid.arrange(grobs = list(top100_part1,top100_part2), layout_matrix = t(matrix(c(1,1,1,1,1,2,2,2,2,2,2,2))), top = title.grob)
dev.off()
##ppo3 and atilla plot, subclustering
integrate.combined_lm_0.2_forplot = SetAssayData(object = integrate.combined_lm_0.2, slot = "data", assay = "integrated", new.data = log2(exp(as.matrix(GetAssayData(object = integrate.combined_lm_0.2, slot = "data", assay = "integrated")))))
atilla_vlnplot <- VlnPlot(object = integrate.combined_lm_0.2_forplot, cols = c('#9970ab',"#F28848","#FCBC2A", "#F0F921"), features = "FBgn0032422", slot = "data",assay = "integrated", pt.size = 0) +NoLegend() +
ggtitle("atilla") +
theme(plot.title = element_text(size=14, face="bold.italic")) +
ylab("Log (2) expression")+
xlab("")
ppo3_vlnplot <- VlnPlot(object = integrate.combined_lm_0.2_forplot, cols = c('#9970ab',"#F28848","#FCBC2A", "#F0F921"), features = "FBgn0261363", slot = "data",assay = "integrated", pt.size = 0) +NoLegend() +
ggtitle("PPO3") +
theme(plot.title = element_text(size=14, face="bold.italic"))+
ylab("Log (2) expression")+
xlab("")
lineage_plot <- plot_grid(lm_dimplot_lineage,atilla_vlnplot,ppo3_vlnplot, labels = c('A','B','C'), label_size = 14, ncol = 3, rel_widths = c(1.8,1,1))
write.csv(atilla_vlnplot$data, file = "atilla_sourcedata.csv", quote = F)
write.csv(ppo3_vlnplot$data, file = "ppo3_sourcedata.csv", quote = F)
##lineage pca
lm_dimplot_pca_legend <- DimPlot(object = integrate.combined_lm_0.2, reduction = "pca",label = T, order = rev(c( "PLASM1","LAM1","LAM2","LAM3")), cols = c('#9970ab',"#F28848","#FCBC2A", "#F0F921")) + NoLegend()
lm_dimplot_pca <- DimPlot(object = integrate.combined_lm_0.2, reduction = "pca",label = F, order = rev(c( "PLASM1","LAM1","LAM2","LAM3")), cols = c('#9970ab',"#F28848","#FCBC2A", "#F0F921"))
lm_dimplot_pca_data <- lm_dimplot_pca$data
lm_dimplot_pca_data_mean <- aggregate(lm_dimplot_pca_data[, 1:2], list(lm_dimplot_pca_data$ident), mean)
lm_dimplot_pca_lineage <- lm_dimplot_pca +
geom_line(data = lm_dimplot_pca_data_mean, aes(PC_1,PC_2), size = 1)+
geom_point(data = lm_dimplot_pca_data_mean, aes(PC_1,PC_2), size = 2)
##pathway analysis first with last LAM
sample_markers_lm_0.2_LAM1v3 <- FindMarkers(integrate.combined_lm_0.2, ident.1 = "LAM1", ident.2 = "LAM3",min.pct = 0.25, logfc.threshold = 0.25, test.use = "MAST")
sample_markers_lm_0.2_LAM1v3$gene <- rownames(sample_markers_lm_0.2_LAM1v3)
sample_markers_lm_0.2_LAM1v3$symbol <- mapIds(org.Dm.eg.db, as.character(sample_markers_lm_0.2_LAM1v3$gene), column="SYMBOL", keytype="FLYBASE", multiVals="first")
write.csv(sample_markers_lm_0.2_LAM1v3, file = "markers_LAM1v3.csv")
##GO plots for genes enriched in PLASM1 and LAM3
#plots only for Log(e)>1, FDR corrected and REVIGO reduction
#supramolecular fiber organization and muscle attachment removed due to complete gene overlap with others
integrate.combined_lm_0.2_forplot = SetAssayData(object = integrate.combined_lm_0.2, slot = "scale.data", assay = "integrated", new.data = log2(exp(as.matrix(GetAssayData(object = integrate.combined_lm_0.2, slot = "scale.data", assay = "integrated")))))
LAM3vPLASM1 <- FindMarkers(integrate.combined_lm_0.2, ident.1 = "LAM3", ident.2 = "PLASM1",min.pct = 0.25, logfc.threshold = 0.25, test.use = "MAST")
LAM3vPLASM1$gene <- mapIds(org.Dm.eg.db, as.character(row.names(LAM3vPLASM1)), column="SYMBOL", keytype="FLYBASE", multiVals="first")
LAM3vPLASM1$avg_logFC <- log2(exp(LAM3vPLASM1$avg_logFC))
write.csv(LAM3vPLASM1, file = "LAM3vPLASM1.csv")
universelist <- mapIds(org.Dm.eg.db, as.character(VariableFeatures(integrate.combined_lm_0.2)), column="ENTREZID", keytype="FLYBASE", multiVals="first")
genelist <- mapIds(org.Dm.eg.db, rownames(LAM3vPLASM1[LAM3vPLASM1$avg_logFC > 1.4,]), column="ENTREZID", keytype="FLYBASE", multiVals="first")
go.fisher <- goana(genelist, universe = universelist, species = "Dm", prior.prob = NULL, covariate=NULL,plot = TRUE)
go.fisher$ADJ<- p.adjust(go.fisher$P.DE, method = "fdr")
go.fisher.sig <- subset(go.fisher, go.fisher$ADJ < 0.05)
for (r in 1:length(rownames(go.fisher.sig))){
x <- org.Dm.egGO2ALLEGS
Rkeys(x) <- rownames(go.fisher.sig)[r]
EG <- mappedLkeys(x)
genes <- paste(mapIds(org.Dm.eg.db, intersect(EG, genelist), column="FLYBASE", keytype="ENTREZID", multiVals="first"),collapse=" ")
go.fisher.sig$Genes[r] <- genes
}
write.csv(go.fisher.sig,file = "go.fisher.sig_uplam3.csv")
scaleddata_forplot <- integrate.combined_lm_0.2_forplot@assays[["integrated"]]@data
scaleddata_forplot <- scaleddata_forplot[,order(scaleddata_forplot[which(rownames(scaleddata_forplot) %in% "FBgn0032422"),])]
x <- org.Dm.egGO2ALLEGS
Rkeys(x) <- "GO:0097435"
EG <- mappedLkeys(x)
aveexp <- AverageExpression(object = integrate.combined_lm_0.2_forplot, features = mapIds(org.Dm.eg.db, intersect(EG, genelist), column="FLYBASE", keytype="ENTREZID", multiVals="first"), assays = "integrated", slot = "scale.data")
aveexp <- aveexp$integrated
aveexp <- aveexp[order(-aveexp$LAM3),]
go0097435<- DoHeatmap2(integrate.combined_lm_0.2_forplot, features = rownames(aveexp), angle = 0, size = 5.5, label = T, group.bar = T, draw.lines = T, group.by = "ident", cells = colnames(scaleddata_forplot), lines.width = 100, group.bar.height = 0, hjust = 0.5, slot = "scale.data") +
ggtitle("Supramolecular fiber organization") +
theme(axis.text.y = element_text(face="italic")) +
guides(color=FALSE) +
guides(fill=FALSE) +
#guides(fill=guide_legend(title="Log (2) expression"))+
theme(plot.title = element_text(size = 16, face = "bold"),
axis.text=element_text(size=14))+
#scale_fill_gradientn(colours = rev(colorRampPalette(RColorBrewer::brewer.pal(11,"RdYlBu"))(256)),na.value = "white")
scale_fill_gradientn(colors = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),na.value = "white")
go0097435$data$Feature <- mapvalues(go0097435$data$Feature, from = rownames(aveexp), to = mapIds(org.Dm.eg.db, rownames(aveexp), column="SYMBOL", keytype="FLYBASE", multiVals="first"))
x <- org.Dm.egGO2ALLEGS
Rkeys(x) <- "GO:0030029"
EG <- mappedLkeys(x)
aveexp <- AverageExpression(object = integrate.combined_lm_0.2_forplot, features = mapIds(org.Dm.eg.db, intersect(EG, genelist), column="FLYBASE", keytype="ENTREZID", multiVals="first"), assays = "integrated", slot = "scale.data")
aveexp <- aveexp$integrated
aveexp <- aveexp[order(-aveexp$LAM3),]
go0030029<- DoHeatmap2(integrate.combined_lm_0.2_forplot, features = rownames(aveexp), angle = 0, size = 5.5, label = T, group.bar = T, draw.lines = T, group.by = "ident", cells = colnames(scaleddata_forplot), lines.width = 100, group.bar.height = 0, hjust = 0.5, slot = "scale.data") +
ggtitle("Actin filament-based process") +
theme(axis.text.y = element_text(face="italic")) +
guides(color=FALSE) +
guides(fill=FALSE) +
guides(fill=guide_legend(title=" Scaled \nLog (2) expression"))+
theme(legend.position="bottom")+
theme(plot.title = element_text(size = 16, face = "bold"),
axis.text=element_text(size=14))+
#scale_fill_gradientn(colours = rev(colorRampPalette(RColorBrewer::brewer.pal(11,"RdYlBu"))(256)),na.value = "white")+
scale_fill_gradientn(colors = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),na.value = "white")+
theme(plot.margin=unit(c(1,1,1.5,1.2),"cm"))
go0030029$data$Feature <- mapvalues(go0030029$data$Feature, from = rownames(aveexp), to = mapIds(org.Dm.eg.db, rownames(aveexp), column="SYMBOL", keytype="FLYBASE", multiVals="first"))
x <- org.Dm.egGO2ALLEGS
Rkeys(x) <- "GO:0016203"
EG <- mappedLkeys(x)
aveexp <- AverageExpression(object = integrate.combined_lm_0.2_forplot, features = mapIds(org.Dm.eg.db, intersect(EG, genelist), column="FLYBASE", keytype="ENTREZID", multiVals="first"), assays = "integrated", slot = "scale.data")
aveexp <- aveexp$integrated
aveexp <- aveexp[order(-aveexp$LAM3),]
go0016203<- DoHeatmap2(integrate.combined_lm_0.2_forplot, features = rownames(aveexp), angle = 0, size = 5.5, label = T, group.bar = T, draw.lines = T, group.by = "ident", cells = colnames(scaleddata_forplot), lines.width = 100, group.bar.height = 0, hjust = 0.5, slot = "scale.data") +
ggtitle("Muscle attachment") +
theme(axis.text.y = element_text(face="italic")) +