exp7ImmuneCellDeconv.Rmd

---
title: "Immune Deconvolution Analysis"
author: "Sara Gosline"
date: "2023-04-03"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Load immune deconv files

We have run the immune cell deconvolution on both the spatial data and the sorted data as a baseline.

```{r loadData}

source("spleenDataFormatting.R")

###add in the sorted cell signature?

spatial<-read.table('deconv/spatialProtMat-mcpcounter-LM7c.tsv',header=T,sep='\t',row.names=1,check.names=F)
sorted<-read.table("deconv/globalProtMat-mcpcounter-LM7c.tsv",header=T,sep='\t',row.names=1,check.names=F)

library(pheatmap)

pheatmap(spatial)#,filename = 'spatialProtImmune.pdf',cellheight = 10)

pheatmap(sorted)#,filename='sortedProtImmune.pdf',cellheight=10)

```

Now we want to map the scores to the spatialProt object. first we need to go through our voxel annotation strategy that we established -- using sorted cells to label
spatial. Now we can use that to label the heatmaps.

```{r voxel annotation}


```




## Plotting cell scores by voxel


```{r gridplot, echo=FALSE}
#library(spammer)
#library(SingleCellExperiment) ##remove this once we update package
source('spatialProtUtils.R')
colData(spat.prot)<-cbind(colData(spat.prot),t(spatial))
p1<-plotSigGrid(spat.prot,"B.cells")
p2<-plotSigGrid(spat.prot,"T8.cells")
p3<-plotSigGrid(spat.prot,"Granulocytes")
p4<-plotSigGrid(spat.prot,"NK.cells")
p5<-plotSigGrid(spat.prot,"MO")
p6<-plotSigGrid(spat.prot,"DC")


pl<-cowplot::plot_grid(p1,p2,p3,p4,p5,p6,nrow=3)
pl

ggsave("ImmCellPlots.pdf",pl,height=10,width=12)

```