SC-2-13

SC-2-13.R

# Loading Seurat
library(Seurat)

# Moving to downloads
setwd("../Downloads/")

# Getting the input files from 10X Genomics
inputFile <- "http://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_1k_protein_v3/pbmc_1k_protein_v3_filtered_feature_bc_matrix.tar.gz"
download.file(inputFile, "PBMC.tar.gz")
untar("PBMC.tar.gz")

#Input Folder
inputFolder <- "filtered_feature_bc_matrix/"

# Loading Seurat
library(Seurat)

# Reading the dataset
PBMC <- Read10X(inputFolder)
PBMC <- PBMC[[1]]

# QC
mtReads <- PBMC[grepl("MT-", rownames(PBMC)),]
mtRate <- (apply(mtReads,2,sum)/apply(PBMC,2,sum))
plot(mtRate, pch=16)
abline(h = 0.1, col = "red", lty = 2)

# Filtering
PBMC <- PBMC[apply(PBMC,1,sum) != 0,]
PBMC <- PBMC[,mtRate < 0.1]

# Creating a Seurat Object
PBMC <- CreateSeuratObject(PBMC)

# Normalizing the dataset
PBMC <- NormalizeData(PBMC)
PBMC <- ScaleData(PBMC)

# Accessing to the raw data
PBMC@assays$RNA@counts

# Accessing to the normalized data
PBMC@assays$RNA@data

# Dimentionality Reduction
PBMC <- FindVariableFeatures(PBMC)
PBMC <- RunPCA(PBMC)
PCAPlot(PBMC)
PBMC <- RunTSNE(PBMC, perplexity = 100)
TSNEPlot(PBMC)
PBMC <- RunUMAP(PBMC, dims = 1:50)
UMAPPlot(PBMC)

# Marker Genes
FeaturePlot(PBMC, features = c("MS4A1", "GNLY", "CD3E", "CD14", "FCER1A", "FCGR3A", "LYZ", "PPBP", "CD8A"))