Load packages

suppressPackageStartupMessages({ 
  library(BiocFileCache)
  library(scran)
  library(scater)
  library(tidyverse)
  library(SingleCellExperiment)
  library(DropletUtils)
  library(ggplot2)
  library(cowplot)
  library(Seurat)
  library(monocle3)
  library(SeuratObject)
})

Read in previously processed SOs.

SNL_CMV<-readRDS("/scratch/Aireland/SNL_CMV all cells captured.rds")
SNL_CMV_tumor<-readRDS("/scratch/Aireland/SNL for R01 Analysis/SNL_CMV_Tumor seurat 011021.rds")

First, for all cells captured before filtering

x<-DimPlot(SNL_CMV, reduction = "umap",label=TRUE,pt.size=.1)+ggtitle("All CMV captured cells by cluster")
y<-DimPlot(SNL_CMV, reduction = "umap", group.by = "tx",label=FALSE,pt.size=0.1)+ggtitle("All CMV captured cells by treatment")
multiplot(x,y,cols=2)

FeaturePlot(SNL_CMV, features = c("Cxcl1","Cxcl2","Cxcl5","Csf3","Il6","Ccl3","Csf2","Tnf"), pt.size=0.2, cols=c(rgb(0,.9,.7,.03),"cyan4","darkslategray"))

Most chemokine signal is coming from the tumor cells and that cluster 11 (Macs)…

Checking chemokine signal within tumor cells only.

Idents(SNL_CMV_tumor)<-'seurat_clusters'
x<-DimPlot(SNL_CMV_tumor, reduction = "umap",label=TRUE,pt.size=.1)+ggtitle("CMV tumor cells by cluster")
y<-DimPlot(SNL_CMV_tumor, reduction = "umap", group.by = "tx",label=FALSE,pt.size=0.1)+ggtitle("CMV tumor cells by treatment")
multiplot(x,y,cols=2)

FeaturePlot(SNL_CMV_tumor, features = c("Cxcl1","Cxcl2","Cxcl5","Csf3","Il6","Ccl3","Csf2","Tnf"), pt.size=0.2, cols=c(rgb(0,.9,.7,.03),"cyan4","darkslategray"))

Define list of chemokines and look at each by heatmap

chemokines<-c("Cxcl1","Cxcl2","Cxcl5","Csf3","Il6","Ccl3","Csf2","Tnf")

DoHeatmap(subset(SNL_CMV_tumor, downsample = 100), features = chemokines, size = 3)

DoHeatmap(subset(SNL_CMV_tumor, downsample = 100), features = chemokines, size = 3, group.by="tx")

Too hard to interpret with all individual cells…

Take average expression of genes across clusters to visualize in heatmap

cluster.so<-AverageExpression(
  SNL_CMV_tumor,
  return.seurat = TRUE,
  group.by = "ident",
  slot = "data",
  verbose = TRUE)
## Centering and scaling data matrix
cluster.so
## An object of class Seurat 
## 32454 features across 10 samples within 1 assay 
## Active assay: RNA (32454 features, 0 variable features)
z<-DoHeatmap(cluster.so, features = chemokines, draw.lines=FALSE)
multiplot(x,y,z,cols=3)

Cluster 1 highly expresses multiple chemokines. It is also a mainly treated cluster…