FloWuenne

## Been having issues lately when using tidyverse functions such as mutate or arrange 
## while having single cell packages such as scran and scater loaded, since they seem to have
## similar functions, that override the tidyverse functions
## Add this snippet to chunks that use tidyverse functions to unload packages that interfere

if(c("package:scran","package::scater") %in% search()){
  detach("package:scater", unload=TRUE)
  detach("package:scran",unload=TRUE)
}

FloWuenne

## Load library
library(dropbead)

## Set cell names
cell_IDs <- colnames(expression_seurat@data)

## Create a new single species object
test <- new("SingleSpeciesSample", 
           species1="human", ## Change to mouse if working with mouse data!
           cells=cell_IDs,

FloWuenne

library(scater)
## Create new SCE object from the seurat expression object
fData <- new("AnnotatedDataFrame", data = [email protected])
seurat_scater_sce <- newSCESet(exprsData = as.matrix(expression_seurat@data), phenoData = fData,
                            lowerDetectionLimit=0.0,
                            logExprsOffset=1)
seurat_scater_sce <- calculateQCMetrics(seurat_scater_sce)

FloWuenne

## Use scater to investigate and annotate gene and cluster properties

```{r}
library(scater)
## Create new SCE object from the seurat expression object
fData <- new("AnnotatedDataFrame", data = [email protected])
seurat_scater_sce <- newSCESet(exprsData = as.matrix(expression_seurat@data), phenoData = fData,
                            lowerDetectionLimit=0.01,
                            logExprsOffset=1)

FloWuenne

library(scran)
library(org.Hs.eg.db)

## Load pretrained model from scran
hg.pairs <- readRDS(system.file("exdata", "human_cycle_markers.rds", package="scran"))

## Translate all ensembl IDs to gene symbols for pretrained model
g1_symbols <- data.frame()
s_symbols <- data.frame()
g2m_symbols <- data.frame()

FloWuenne

## Cellview export of data for user

### Rerun TSNE with 3 dimensions

```{r}
expression_seurat_cellview <- RunTSNE(expression_seurat,
                                      dims.use = 1:20,
                                      do.fast=T,
                                      dim.embed= 3)
```

FloWuenne

library(stringr)

## Look at the number of duplicated and unique UMIs per Cell

umi_by_gene <- read.table("UMI_by_gene_dist.tab",sep="\t",header=T)

## Count number of duplicated UMIs per cell
num_of_duplicated_umis_per_cell <- umi_by_gene %>%
  group_by(Cell.Barcode) %>%
  subset(Num_Obs > 1) %>%

FloWuenne

candidate_markers <- c("GAPDH","NANOG","ACTB")

for(this_gene in candidate_markers){

   ## Violin Plot
  vln_plot_avgdiff <- VlnPlot(expression_seurat.updated,
            this_gene,
            point.size.use = -1,
            x.lab.rot=TRUE,
            remove.legend = TRUE,

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

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

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

# First we do all pairwise comparisons and retain any markers that 
# are even somewhat higher in the cell type of interest