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seurat_harmony_expt.R
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seurat_harmony_expt.R
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##------------------------------------
## START
##------------------------------------
##------------------------------------
## Libraries
rm(list = ls())
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(ggrepel)
library(reticulate)
library(clustree)
library(harmony)
library(scCustomize)
options(future.globals.maxSize= 50000 * 1024^2)
##------------------------------------
## Load seurat object for
## Individual genotype
#### WT-CTL
load("CTL_CB_DF_CLUST.RData")
wt.ctl.seurat <- seuObj
rm(seuObj)
## CKO-CTL
load("CKO_CB_DF_CLUST.RData")
cko.ctl.seurat.temp <- seuObj
rm(seuObj)
## CKO-RES
load("RES_CB_DF_CLUST.RData")
cko.res.seurat.temp <- seuObj
rm(seuObj)
##------------------------------------
## remove outlier samples
cko.ctl.seurat <- subset(cko.ctl.seurat.temp, subset = GenoAgeSample %in% c("CTL_P18_3CF1"), invert = TRUE)
table(cko.ctl.seurat$GenoAgeSample)
cko.res.seurat <- subset(cko.res.seurat.temp, subset = GenoAgeSample %in% c("RES_P18_3BF5"), invert = TRUE)
table(cko.res.seurat$GenoAgeSample)
##------------------------------------
## re-organize seurat objects
wt.ctl.seurat$Virus <- wt.ctl.seurat$Genotype
wt.ctl.seurat$Genotype <- rep("WT", nrow([email protected]))
wt.ctl.seurat$GenoVirAgeSample <- paste(wt.ctl.seurat$Genotype, wt.ctl.seurat$Virus, wt.ctl.seurat$Age, wt.ctl.seurat$Sample, sep = "_")
table([email protected]$GenoVirAgeSample)
cko.ctl.seurat$Virus <- cko.ctl.seurat$Genotype
cko.ctl.seurat$Genotype <- rep("CKO", nrow([email protected]))
cko.ctl.seurat$GenoVirAgeSample <- paste(cko.ctl.seurat$Genotype, cko.ctl.seurat$Virus, cko.ctl.seurat$Age, cko.ctl.seurat$Sample, sep = "_")
table([email protected]$GenoVirAgeSample)
cko.res.seurat$Virus <- cko.res.seurat$Genotype
cko.res.seurat$Genotype <- rep("CKO", nrow([email protected]))
cko.res.seurat$GenoVirAgeSample <- paste(cko.res.seurat$Genotype, cko.res.seurat$Virus, cko.res.seurat$Age, cko.res.seurat$Sample, sep = "_")
table([email protected]$GenoVirAgeSample)
##------------------------------------
## add additional meta information
wt.ctl.seurat$Sex <- gsub("^WT_CTL_P18_C1$", "F", wt.ctl.seurat$GenoVirAgeSample)
wt.ctl.seurat$Sex <- gsub("^WT_CTL_P18_C2$|^WT_CTL_P18_C3$", "M", wt.ctl.seurat$Sex)
cko.ctl.seurat$Sex <- gsub("^CKO_CTL_P18_3BF1$", "F", cko.ctl.seurat$GenoVirAgeSample)
cko.ctl.seurat$Sex <- gsub("^CKO_CTL_P18_3BM3$|^CKO_CTL_P18_3BM4$", "M", cko.ctl.seurat$Sex)
cko.res.seurat$Sex <- gsub("^CKO_RES_P18_3CF5$", "F", cko.res.seurat$GenoVirAgeSample)
cko.res.seurat$Sex <- gsub("^CKO_RES_P18_3BM8$|^CKO_RES_P18_3CF8$", "M", cko.res.seurat$Sex)
wt.ctl.seurat$Batch <- rep(2, nrow([email protected]))
cko.ctl.seurat$Batch <- rep(1, nrow([email protected]))
cko.res.seurat$Batch <- rep(1, nrow([email protected]))
##------------------------------------
## merge seurat object for genotypes
seuObj <- merge(x = wt.ctl.seurat, y = c(cko.ctl.seurat, cko.res.seurat))
table(seuObj$GenoVirAgeSample)
##------------------------------------
## preprocess
seuObj <- NormalizeData(seuObj) %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA(verbose = FALSE, npcs = 100)
##-------------------------------------------------------
## jackstraw analysis
## Compute and Score Jackstraw
seuObj <- JackStraw(seuObj, num.replicate = 100, dims = 100)
seuObj <- ScoreJackStraw(seuObj, dims = 1:100)
## Plot PCA loadings and Jackstraw scores
p6 <- ElbowPlot(seuObj, ndims = 100)
p7 <- JackStrawPlot(seuObj, dims = 1:100)
ggsave(filename = "SEURAT_PCA_1.pdf", plot = p6, width = 6, height = 4, units = "in", dpi = 150)
ggsave(filename = "SEURAT_PCA_2.pdf", plot = p7, width = 12, height = 6, units = "in", dpi = 150)
## IDENTIFY PCS
pcScores <- seuObj@reductions$pca@jackstraw$overall.p.values
pcScoresNoSign <- pcScores[pcScores[,2] > 0.05,]
selpcs <- min(pcScoresNoSign[,1]) - 1
print(selpcs)
##------------------------------------
## harmony
seuObj <- RunHarmony(seuObj, group.by.vars = c("Batch", "Sex"), reduction = "pca", assay.use = "RNA", max.iter.harmony = 100)
##------------------------------------
## clustering
seuObj <- FindNeighbors(seuObj, dims = 1:selpcs, reduction = "harmony")
seuObj <- FindClusters(seuObj, resolution = c(0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0), reduction = "harmony")
seuObj <- RunUMAP(seuObj, dims = 1:selpcs, reduction = "harmony")
##-------------------------------------------------------
## SAVE RDATA
save(seuObj, file = "SEURAT_CTL_CKO_RES_HARMONY.RData")
##------------------------------------
## Plots
## CLUSTREE
seutree <- clustree(seuObj, prefix = "RNA_snn_res.", node_colour = "sc3_stability") # + scale_color_brewer(palette = "Set1") + scale_edge_color_continuous(low = "blue", high = "red")
ggsave(filename = "SEURAT_HARMONY_CLUSTREE.pdf", plot = seutree, width = 24, height = 24, units = "in", dpi = 150)
## UMAP FOR ALL RESOLUTIONS
ResolutionList <- grep("RNA_snn_res", colnames([email protected]), value = TRUE)
for (Resolution in ResolutionList)
{
print(paste("====> RESOLUTION ", Resolution, sep = ""))
pdf(paste0("SEURAT_HARMONY_UMAP_RES_", Resolution, ".pdf"), width=7, height=6)
g <- DimPlot(object = seuObj, label = TRUE, reduction = "umap", group.by = Resolution)
print(g)
dev.off()
pdf(paste0("SEURAT_HARMONY_VIOLIN_nUMI_RES_", Resolution, ".pdf"), width=7, height=3)
v <- VlnPlot(object = seuObj, features = "nCount_RNA", ncol = 1, pt.size = 0, group.by = Resolution)
print(v)
dev.off()
}
## UMAP FOR MARKER GENES
mygenes <- c("Mki67", "Sox4", "Sox11", "Foxp1", "Foxp2", "Drd", "Drd2", "Tac1", "Penk", "Casz1", "Aqp4", "Olig1", "Cspg4", "Mag", "Cx3cr1", "Flt1", "Slc17a7", "Chat", "Dlx2", "Npy", "Ascl1", "Sp9", "Ppp1r1b", "Oprm1", "Isl1", "Pdyn", "Lypd1", "Nnat", "Ebf1", "Epha4", "Mef2c")
fpl1 <- FeaturePlot_scCustom(seurat_object = seuObj, features = mygenes, reduction = "umap", pt.size = 0.05, min.cutoff = 0, max.cutoff = 3, order = TRUE, raster = TRUE)
ggsave(filename = "SEURAT_PLOT_FeaturePlot_orderT.pdf", plot = fpl1, width = 15, height = 24, units = "in", dpi = 150)
fpl2 <- FeaturePlot_scCustom(seurat_object = seuObj, features = mygenes, reduction = "umap", pt.size = 0.05, min.cutoff = 0, max.cutoff = 3, order = FALSE, raster = TRUE)
ggsave(filename = "SEURAT_PLOT_FeaturePlot_orderF.pdf", plot = fpl2, width = 15, height = 24, units = "in", dpi = 150)
plotCluUMAP1 <- DimPlot_scCustom(seurat_object = seuObj, group.by = "Genotype", pt.size = 0.1, reduction = "umap", label = FALSE, raster = TRUE)
ggsave(filename = "SEURAT_UMAP_GENOTYPE.pdf", plot = plotCluUMAP1, width = 10, height = 8, units = "in", dpi = 300)
plotCluUMAP1b <- DimPlot_scCustom(seurat_object = seuObj, group.by = "Genotype", split.by = "Genotype", num_columns = 4, pt.size = 0.1, reduction = "umap", raster = TRUE)
ggsave(filename = "SEURAT_UMAP_GENOTYPE_FACET.pdf", plot = plotCluUMAP1b, width = 32, height = 8, units = "in", dpi = 300)
plotCluUMAP1c <- DimPlot_scCustom(seurat_object = seuObj, group.by = "GenoVirAgeSample", split.by = "GenoVirAgeSample", num_columns = 3, pt.size = 0.1, reduction = "umap", raster = TRUE)
ggsave(filename = "SEURAT_UMAP_GENOVIRAGESAMPLE_FACET.pdf", plot = plotCluUMAP1c, width = 25, height = 20, units = "in", dpi = 300)
plotCluUMAP1d <- DimPlot_scCustom(seurat_object = seuObj, group.by = "GenoVirAgeSample", pt.size = 0.1, reduction = "umap", raster = TRUE)
ggsave(filename = "SEURAT_UMAP_GENOVIRAGESAMPLE.pdf", plot = plotCluUMAP1d, width = 8, height = 6, units = "in", dpi = 300)
plotCluUMAP1e <- DimPlot(seuObj, group.by = c("Sex", "Batch", "Genotype", "Virus", "GenoVirAgeSample"), pt.size = 0.01)
ggsave("SEURAT_UMAP_COVARIATES.pdf", plot = plotCluUMAP1e, width = 20, height = 16, units = "in", dpi = 300)
# RESOLUTION 1.2
my_levels <- seq(0, max(as.numeric(seuObj$RNA_snn_res.1.2)) - 1)
seuObj$RNA_snn_res.1.2 <- factor(x = seuObj$RNA_snn_res.1.2, levels = my_levels)
vpl2 <- Stacked_VlnPlot(seurat_object = seuObj, features = mygenes, group.by = "RNA_snn_res.1.2", pt.size = 0, x_lab_rotate = TRUE)
ggsave(filename = "SEURAT_PLOT_ViolinPlot_1.2_A.pdf", plot = vpl2 , width = 12, height = 18, units = "in", dpi = 150, useDingbats=FALSE)
vpl3 <- Stacked_VlnPlot(seurat_object = seuObj, features = c("nCount_RNA", "nFeature_RNA"), group.by = "RNA_snn_res.1.2", pt.size = 0, x_lab_rotate = TRUE)
ggsave(filename = "SEURAT_PLOT_ViolinPlot_1.2_B.pdf", plot = vpl3 , width = 12, height = 3, units = "in", dpi = 150, useDingbats=FALSE)
dpl2 <- DotPlot_scCustom(seurat_object = seuObj, features = rev(mygenes), colors_use = viridis_plasma_dark_high, group.by = "RNA_snn_res.1.2", x_lab_rotate = TRUE)
ggsave(filename = "SEURAT_PLOT_DotPlot_1.2.pdf", plot = dpl2, width = 12, height = 16, units = "in", dpi = 150, useDingbats=FALSE)
## RES 1.2
## BARPLOT SHOWING CELLS PER CLUSTER PER GENOTYPE | RES 1.2
cellsPerCluster <- as.data.frame.matrix(table([email protected]$RNA_snn_res.1.2, [email protected]$Genotype))
cellsPerCluster$Cluster <- paste("Cluster", sprintf("%02d", as.numeric(row.names(cellsPerCluster))), sep = "_")
cellsPerCluster <- cellsPerCluster[order(cellsPerCluster$Cluster),]
write.table(cellsPerCluster, paste("SEURAT_GENOTYPE_PER_CLUSTER_1.2.txt", sep = "_"), row.names = F, col.names = T, quote = F, sep = "\t")
cellsPerCluster2 <- melt(cellsPerCluster)
colnames(cellsPerCluster2) <- c("CLUSTER", "GENOTYPE", "CELLS")
p7 <- ggplot(cellsPerCluster2) +
geom_bar(aes(x = CLUSTER, y = CELLS, fill = GENOTYPE), stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
NULL
ggsave(filename = paste("SEURAT_GENOTYPE_PER_CLUSTER_1.2.pdf", sep = "_"), plot = p7, width = 9, height = 3, units = "in", dpi = 150)
## BARPLOT SHOWING CELLS PER CLUSTER PER GENOTYPE | RES 1.2
cellsPerCluster <- as.data.frame.matrix(table([email protected]$RNA_snn_res.1.2, [email protected]$GenoVirAgeSample))
cellsPerCluster$Cluster <- paste("Cluster", sprintf("%02d", as.numeric(row.names(cellsPerCluster))), sep = "_")
cellsPerCluster <- cellsPerCluster[order(cellsPerCluster$Cluster),]
write.table(cellsPerCluster, paste("SEURAT_GENOVIRAGESAMPLE_PER_CLUSTER_1.2.txt", sep = "_"), row.names = F, col.names = T, quote = F, sep = "\t")
cellsPerCluster2 <- melt(cellsPerCluster)
colnames(cellsPerCluster2) <- c("CLUSTER", "GENOVIRAGESAMPLE", "CELLS")
p7 <- ggplot(cellsPerCluster2) +
geom_bar(aes(x = CLUSTER, y = CELLS, fill = GENOVIRAGESAMPLE), stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent_format()) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
NULL
ggsave(filename = paste("SEURAT_GENOVIRAGESAMPLE_PER_CLUSTER_1.2.pdf", sep = "_"), plot = p7, width = 9, height = 3, units = "in", dpi = 150)
##------------------------------------
## Find Cluster Markers
rm(list = ls())
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(ggrepel)
library(reticulate)
library(clustree)
library(harmony)
library(scCustomize)
options(future.globals.maxSize= 50000 * 1024^2)
load("SEURAT_CTL_CKO_RES_HARMONY.RData")
Idents(seuObj) <- "RNA_snn_res.1.2"
seuObj.markers <- FindAllMarkers(seuObj, only.pos = TRUE) #, min.pct = 0, logfc.threshold = 0)
write.table(seuObj.markers, "SEURAT_CTL_CKO_RES_HARMONY_RES_1.2_CLUSTER_MARKERS.txt", row.names = TRUE, col.names = TRUE, quote = FALSE, sep = "\t")
save(seuObj.markers, file = "SEURAT_CTL_CKO_RES_HARMONY_RES_1.2_CLUSTER_MARKERS.RData")
##------------------------------------
## END
##------------------------------------