FloWuenne

library(tidyverse)
library(Seurat)
# Find specific markers for each cell type
# Find specific markers for each cell type

celltypes <- unique(expression_seurat_subset@ident)  # list of cell types (unique ident labels of Seurat object)
obj <-   expression_seurat_subset      # Seurat object
specific_markers <- NULL

# First we do all pairwise comparisons and retain any markers that 

FloWuenne

# Basic function to convert human to mouse gene names
convertHumanGeneList <- function(x){
  
  require("biomaRt")
  human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
  mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")
  
  genesV2 = getLDS(attributes = c("hgnc_symbol"), filters = "hgnc_symbol", values = x , mart = human, attributesL = c("mgi_symbol"), martL = mouse, uniqueRows=T)
  
  humanx <- unique(genesV2[, 2])

FloWuenne

##Plotting theme from (https://rpubs.com/Koundy/71792)
theme_Publication_custom <- function(base_size=14, base_family="helvetica") {
      library(grid)
      library(ggthemes)
      (theme_foundation(base_size=base_size, base_family=base_family)
       + theme(plot.title = element_text(face = "bold",
                                         size = rel(1.2), hjust = 0.5),
               text = element_text(),
               panel.background =  element_rect(colour = NA),
               plot.background = element_rect(colour = NA),

FloWuenne

## Plot PCA for ethnicity of a given VCF file

## Import publication theme
## A nice R ggplot theme that I found online. Original can be found here:
## https://rpubs.com/Koundy/71792

theme_Publication <- function(base_size=14, base_family="helvetica") {
  library(grid)
  library(ggthemes)
  (theme_foundation(base_size=base_size, base_family=base_family)

FloWuenne

## 3.0 contains all regulons that are considered for AUCell scoring
load("./int/3.0_regulons_forAUCell.RData")

## Define the gene you would like to find
gene_of_interest <- ""


## Iterates over all regulon names in regulons and searches the gene of interest inside the regulon.
for(reg in names(regulons)){
  if(gene_of_interest %in% regulons[[reg]]){

FloWuenne

```{r}
library(tidyr)
library(dplyr)

seurat_object <- seurat_objects.combined
grouping_var <- "Treatment"

cells_for_all_clusters <- data.frame()
#colnames(cells_for_all_clusters) <- c("WT","THC","CBS")

FloWuenne

```{r}
## Read in some test data
colorectal <- readRDS("D:/Studies_PhD/Single_Cell_data/colorectal_cancer_Li_et_al_2017_SCESet.rds")
```

```{r}
sData <- scDataConstructor(as.matrix(colorectal))
sData <- determineDropoutCandidates(sData)

#sData <- wThreshold(sData)

FloWuenne

library(dplyr)
library(tidyr)

time_point <- readline(prompt="Enter time point: ")

## Load Seurat object
seurat_object <- readRDS(paste("../Objects/",time_point,".Seurat_object.Rds",sep=""))

## Normalized expression data
norm_exprs_sparse <- seurat_object@data

FloWuenne

library(dplyr)
library(tidyr)

time_points <- c("E14.5","E16.5","E18.5","P1","P4","P7")

for(time_point in time_points){

## Load Seurat object
seurat_object <- readRDS(paste("../Objects/",time_point,".Seurat_object.Rds",sep=""))

FloWuenne

library(dplyr)
library(tidyr)

## Define S4 object with required components
Clustering_info <- setClass("clustering_module",slots=c(tsne="data.frame",
                                                  metadata="data.frame",
                                                  norm_exprs="data.frame",
                                                  marker_list="data.frame"))
                                                  
## Let user enter the time point to process