tabula_muris_analysis.Rmd

---
title: "Tabula Muris Analysis"
author: "Rohan Verma"
date: "12/11/2019"
output: html_document
---

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

## R Markdown

```{r cars}
#Tabula Muris Data
library(ggplot2);library(Seurat);library(umap);library(dplyr)
toremove <- c('Prss1', 'Pnlip', 'Cpa1', 'Gp2', 'Gm13011', 'Krt13', 'Pgc', 'Krt4', 
              'Prl', 'Lipf', 'Clps', 'Ctrb1', 'Fga', 'Hp', 'Ckm', 'Fgg', 'Mybpc1', 'Myh2','Prss2')

####all endoT####
files <- c('~/Downloads/facs_Brain_Non-Myeloid_seurat_tiss.Robj',
           '~/Downloads/facs_Liver_seurat_tiss.Robj',
           '~/Downloads/facs_Lung_seurat_tiss.Robj',
           '~/Downloads/facs_Fat_seurat_tiss.Robj',
           '~/Downloads/facs_Heart_seurat_tiss.Robj',
           '~/Downloads/facs_Kidney_seurat_tiss.Robj',
           '~/Downloads/facs_Limb_Muscle_seurat_tiss.Robj',
           '~/Downloads/facs_Trachea_seurat_tiss.Robj',
           '~/Downloads/facs_Pancreas_seurat_tiss.Robj')

endoT <- list()
for (i in 1:length(files)){
  load(files[i])
  tiss <- UpdateSeuratObject(tiss)
  endoT[[i]] <- rownames(tiss@meta.data)[(grepl(tiss@meta.data$cell_ontology_class, pattern='endothelial cell'))]
  endoT[[i]] <- subset(tiss,cells = endoT[[i]])
  rm(tiss)
}
names(endoT) <- c('Brain','Liver','Lung','Fat','Heart','Kidney','Limb','Trachea','Pancreas')

x <- endoT[[1]]

for(i in 2:length(endoT)){
  x <<- merge(x,endoT[[i]])
}

rm(endoT);ET <- x;rm(x)
ET <- NormalizeData(ET, normalization.method = "LogNormalize", scale.factor = 10000)
ET <- ScaleData(ET, features = rownames(ET))
ET <- FindVariableFeatures(ET, selection.method = "vst", nfeatures = 2000)
ET <- RunPCA(ET, features = VariableFeatures(object = ET))
print(DimHeatmap(ET, dims = 1:6, cells = 500, balanced = TRUE,fast=FALSE) + theme_void()) 
print(DimHeatmap(ET, dims = 7:12, cells = 500, balanced = TRUE,fast=FALSE) + theme_void())
print(ElbowPlot(ET))# want to use 6 here
tothispc <-10#6
ET <- FindNeighbors(ET, dims = 1:tothispc)
ET <- FindClusters(ET, resolution = 0.5)
ET <- RunTSNE(ET, dims = 1:tothispc)
DimPlot(ET, reduction = "tsne")
TSNEPlot(ET,group.by='tissue')


tothispc <-10#6
ET <- FindNeighbors(ET, dims = 1:tothispc)
ET <- FindClusters(ET, resolution = 0.5)
ET <- RunTSNE(ET, dims = 1:tothispc)
DimPlot(ET, reduction = "tsne")
TSNEPlot(ET,group.by='tissue')
```

```{r}
sessionInfo()
```