Public Dataset Final

knitr::opts_chunk$set(echo = TRUE)

library('Seurat')

## Attaching SeuratObject

## Seurat v4 was just loaded with SeuratObject v5; disabling v5 assays and
## validation routines, and ensuring assays work in strict v3/v4
## compatibility mode

#reading in data from Chen et al.2020

list.files(getwd())

##  [1] "Feature plot of NK cells.jpeg" "GSEA vlnplots.pdf"            
##  [3] "GSEA.emf"                      "NK cell clusters final.ai"    
##  [5] "NK cell clusters final.emf"    "NK cell clusters.emf"         
##  [7] "NK cell dot plot NEW.emf"      "NK cell dot plot.emf"         
##  [9] "NK clusters New.emf"           "Public-Dataset-Final.html"    
## [11] "Public-Dataset-Final.Rmd"      "Public Dataset Final.Rmd"     
## [13] "Public dataset.R"              "Public Dataset.Rproj"         
## [15] "Public Dataset.Rproj.RData"    "QC before Public Dataset.jpeg"
## [17] "QC plots before.emf"           "QC post public dataset.jpeg"  
## [19] "Sample 1"                      "Sample 2"                     
## [21] "Sample 3"                      "Sample 4"                     
## [23] "Sample 5"                      "Trscore of clusters.ai"       
## [25] "Trscore of clusters.emf"       "UMAP clusters.jpeg"

cancer01 <- Read10X(data.dir = "Sample 1", strip.suffix = TRUE)
cancer02 <- Read10X(data.dir = "Sample 2", strip.suffix = TRUE)
cancer03 <- Read10X(data.dir = "Sample 3", strip.suffix = TRUE)
cancer04 <- Read10X(data.dir = "Sample 4", strip.suffix = TRUE)
cancer05 <- Read10X(data.dir = "Sample 5", strip.suffix = TRUE)

#Creating a SeuratObject bladder tumour samples
cancer01 <- CreateSeuratObject(counts = cancer01, project = "cancer01", min.cells = 3)
cancer02 <- CreateSeuratObject(counts = cancer02, project = "cancer02", min.cells = 3)
cancer03 <- CreateSeuratObject(counts = cancer03, project = "cancer03", min.cells = 3)
cancer04 <- CreateSeuratObject(counts = cancer04, project = "cancer04", min.cells = 3)
cancer05 <- CreateSeuratObject(counts = cancer05, project = "cancer05", min.cells = 3)

#mt
cancer01[["percent.mt"]] <- PercentageFeatureSet(cancer01, pattern = "^MT-")
cancer02[["percent.mt"]] <- PercentageFeatureSet(cancer02, pattern = "^MT-")
cancer03[["percent.mt"]] <- PercentageFeatureSet(cancer03, pattern = "^MT-")
cancer04[["percent.mt"]] <- PercentageFeatureSet(cancer04, pattern = "^MT-")
cancer05[["percent.mt"]] <- PercentageFeatureSet(cancer05, pattern = "^MT-")

cancer01 <- subset(cancer01, subset = nFeature_RNA < 6000 & nCount_RNA > 1000 & percent.mt < 10)
cancer02 <- subset(cancer02, subset = nFeature_RNA < 6000 & nCount_RNA > 1000 & percent.mt < 10)
cancer03 <- subset(cancer03, subset = nFeature_RNA < 6000 & nCount_RNA > 1000 & percent.mt < 10)
cancer04 <- subset(cancer04, subset = nFeature_RNA < 6000 & nCount_RNA > 1000 & percent.mt < 10)
cancer05 <- subset(cancer05, subset = nFeature_RNA < 6000 & nCount_RNA > 1000 & percent.mt < 10)

#Visualizing QC features 

VlnPlot(cancer01, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(cancer02, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(cancer03, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(cancer04, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(cancer05, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

Make a cancer list

knitr::opts_chunk$set(echo = TRUE)

cancer.list <- list()
cancer.list[["cancer01"]] <- cancer01
cancer.list[["cancer02"]] <- cancer02
cancer.list[["cancer03"]] <- cancer03
cancer.list[["cancer04"]] <- cancer04
cancer.list[["cancer05"]] <- cancer05