02_Seurat_processing.Rmd

---
title: "Analyzing the scRNASeq data with Seurat"
author: "Alex Germanos, Sonali Arora"
date: "Feb 24, 2022"
output: 
  html_document:
    toc: true
    theme: united
---

# Introduction


```{r setup}
library(Seurat)
library(monocle3)
library(ggplot2)
library(SingleR)
library(celldex)
library(GSVA)
library(biomaRt)

human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")

# Basic function to convert human to mouse gene names
convertHumanGeneList <- function(x,human=human, mouse=mouse){
  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])
  return(genesV2)
}

```

## Seurat processing 

In this vignette, we will process the data using [Seurat](https://satijalab.org/seurat/)
We will perform the following steps - read in the SingleCellExperiment object from the previous vignette, 
calculate percentage of mitochondrial and ribosomal genes. Next, we will normalize the data, 
find 2000 variable features, followed by scaling the data, and using 
dimension reduction techniques such as PCA, UMAP and finalizing our clusters.

```{r}
sce = readRDS("data/filtered_sce.rds")

exprs_mat = assay(sce)
cell_metadata = colData(sce)
gene_annotation = rowData(sce)
colnames(gene_annotation)[3] = "gene_short_name"

seurat <- CreateSeuratObject(counts =  exprs_mat, project = "10X", assay = "RNA", min.cells = 0, min.features = 0)

mito.genes <- grep("Mt", rownames(seurat))
rb.genes <- grep("^Rp", rownames(seurat))
percent.mito <- Matrix::colSums(seurat@assays[["RNA"]][mito.genes, ])/Matrix::colSums(seurat@assays[["RNA"]])
percent.rb <- Matrix::colSums(seurat@assays[["RNA"]][rb.genes, ])/Matrix::colSums(seurat@assays[["RNA"]])
seurat$percent.mito <- percent.mito
seurat$percent.rb <- percent.rb

seurat$genotype <- cell_metadata$genotype
seurat$genotype <- factor(x = seurat$genotype, levels = c("WT_intact", "PTEN_intact", "PTEN_castrate", "PTEN_castrate_4EBP1"))
seurat$sample_number <- cell_metadata$sample_number
seurat$replicate <- cell_metadata$replicate
seurat$sample_id <- cell_metadata$sample_id


# Normalize the data.
seurat <- NormalizeData(seurat, normalization.method = "LogNormalize", scale.factor = 10000)

# find variable features
seurat <- FindVariableFeatures(seurat, selection.method = "vst", nfeatures = 2000)

# scale data
all.genes <- rownames(seurat)
seurat <- ScaleData(seurat, features = all.genes)
# run PCA
seurat <- RunPCA(seurat, features = VariableFeatures(object = seurat))
ElbowPlot(seurat, ndims = 50)

seurat <- FindNeighbors(seurat, dims = 1:50)
seurat <- RunUMAP(seurat, dims = 1:50)
seurat <- FindClusters(seurat, resolution = 1.7)

```

##  SingleR to classify clusters
 
In the next chunk of code, we will use R package [SingleR](https://bioconductor.org/packages/release/bioc/html/SingleR.html) to identify each cluster
and then add it to our Seurat object.

```{r}

immgen <-celldex::ImmGenData()
count_mat = seurat@assays[["RNA"]]@counts
singler.immgen.500 <- SingleR(method = "cluster",
                              test = count_mat,
                              ref = immgen, ## ref dataset
                              labels = immgen$label.main, ## Start by using main types
                              genes = "de", ## Use de method as opposed to sd
                              clusters = seurat@active.ident )

# create a map for celltype labels :
celltype_map = singler.immgen.500[, 4]
names(celltype_map) = 0:19
celltype_vec = seurat@active.ident
seurat$expanded_celltype = celltype_map[celltype_vec]

```

## Define epithelial subtypes

While SingleR gives us which cells are epithelial, we can use gene lists from 
publications in the field to identify different subgroups of epithelial subtype
First lets subset the Seurat object to contain only the cells which are Epithelial 

```{r}
seurat.epi = seurat[, (seurat$expanded_celltype == "Epithelial cells") ]
```
However, since these gene lists are from human - we will convert our mouse gene symbols
to human gene symbols

Next, we will calculate Average expression of genes for each cluster, 
and perform GSVA , using the gene lists from paper to further define epitheial subtypes


```{r}
# calculates average expression of gene for each cluster 
avg_exp = AverageExpression(object = seurat.epi, add.ident="genotype")
avg_exp = avg_exp[[1]] # non-log values
log2_avg_exp = log2(avg_exp +1) # log values. 

lumsigs <- read.csv("epithelial_ID_sigs/luminal_sigs.csv")
cd38 <- convertHumanGeneList(lumsigs$CD38_sig)
sca1 <- lumsigs$Sca1_sig[lumsigs$Sca1_sig != "--"]
cd38 <- intersect(cd38$MGI.symbol, rownames(seurat.epi))
sca1 <- intersect(sca1, rownames(seurat.epi))

science.sigs <- read_xlsx("epithelial_ID_sigs/Science_gene_expression.xlsx")
science.basal <- science.sigs$Epi_Basal_1
science.L1 <- science.sigs$Epi_Luminal_1
science.L2 <- science.sigs$Epi_Luminal_2Psca
science.L3 <- science.sigs$Epi_Luminal_3Foxi1

strand.sigs <- read.csv("epithelial_ID_sigs/Strand_topgenes.csv")
strand.basal <- strand.sigs$Basal
strand.ure <- strand.sigs$Urethral
strand.VP <- strand.sigs$VP
strand.AP <- strand.sigs$AP
strand.DLP <- strand.sigs$DLP
strand.SV <- strand.sigs$SV

## Functional signatures - AR and CCP
AR <- c('Ar', 'Klk1', 'Nkx3-1', 'Pmeipa1', 'Ell2', 'Gnmt', 'Abcc4',
        'Acsl3', 'Cenpn', 'AA986860', 'Tmprss2', 'Fkbp5', 'Herc3',
        'Ptger4', 'Adam7', 'Eaf2', 'Zbtb10', 'Nnmt', 'Maf', 'Med28', 'Mphosph9')

CCP <- c('Foxm1', 'Aspm', 'Tk1', 'Prc1', 'Cdc20', 'Bub1b', 'Pbk', 'Dtl',
         'Cdkn3', 'Rrm2', 'Asf1b', 'Cep55', 'Cdk1', 'Dlgap5', 'Ska1', 'Rad51',
         'Kif11', 'Birc5', 'Rad54l', 'Cenpm', 'Pclaf', 'Kif20a', 'Pttg1',
         'Cdca8', 'Nusap1', 'Plk1', 'Cdca3', 'Orc6', 'Cenpf', 'Top2a', 'Mcm10')

# perform GSVA

strand.l <- list("Basal" = strand.basal, "Urethral" = strand.ure,
                 "VP" = strand.VP,
                 "L1" = science.L1,
                 "Sca1hi" = sca1)
strand.score <- as.data.frame(t (gsva(data.matrix(log2_avg_exp), strand.l) ))

clust.heat <- pheatmap(strand.score, display_numbers = T)
cell.heat <- pheatmap(t(strand.score), display_numbers = T)

fun.l <- list("AR" = AR, "CCP" = CCP)
fun.score <- as.data.frame(t(gsva(data.matrix(log2_avg_exp), fun.l)))

## add scores for epithelial subtype to seurat object.
celltype_map_epi = c("Progenitor", "Differentiated","Basal", "Progenitor", #cluster no :  0,2,3,6
                 "Progenitor", # cluster-no :9
                 "Basal", "Urethral", "Differentiated",  # cluster_no : 11, 12, 13
                  "Urethral", "Basal", "Basal" ) # cluster no: 14, 18, 19
names(celltype_map_epi) = c(0, 2, 3, 6, 9, 11, 12, 13, 14, 18, 19)

celltype_vec = seurat.epi@active.ident
seurat.epi$celltypes = celltype_map_epi[celltype_vec]


celltype_map_full=c("Progenitor", "Macrophages", "Differentiated", "Basal",
               "Fibroblast", "Basal", "Progenitor", "T cells", "Neutrophils",
               "Progenitor",# cluster-9
               "Stromal", "Basal", "Urethral", "Differentiated",
               "Urethral", "B cells", "Differentiated", "Endothelial", "Basal", "Basal")
names(celltype_map_full)=c(0:19)

celltype_vec = seurat@active.ident
seurat$celltypes = celltype_map_full[celltype_vec]
```

## Assigning cell cycle scores

Seurat comes with list of cell cycle genes, we can pull them out
and covert them to mouse gene symbols. 

```{r}
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes

s.genes <- convertHumanGeneList(s.genes, human=human, mouse=mouse)$MGI.symbol
g2m.genes <- convertHumanGeneList(g2m.genes, human=human, mouse=mouse)$MGI.symbol

## Assign S and G2/M scores, and assign predicted phase
seurat.epi <- CellCycleScoring(seurat.epi, s.features = s.genes, g2m.features = g2m.genes, set.ident = F)
seurat <- CellCycleScoring(seurat, s.features = s.genes, g2m.features = g2m.genes, set.ident = F)
```


## Save the Seurat object

```{r}
saveRDS(seurat, file = "data/seurat_cleaned.allcells.rds")
saveRDS(seurat.epi, file = "data/seurat_cleaned.epi.rds")
```

## UMAP & Biomarker figures

In the following section, we make figures to visualize the scRNASeq data. 

```{r}

pdf("figures/Seurat_pipeline.pdf", width =10)
DimPlot(seurat, reduction = "umap")
DimPlot(seurat, reduction = "umap", group.by="sample_number")
DimPlot(seurat, reduction = "umap", group.by="genotype")
dev.off()

pdf("figures/Seurat_pipeline1_umap_split_by_genotype.pdf", width = 10, height =10)
DimPlot(seurat, split.by="genotype", ncol=2 )
dev.off()


pdf("figures/Seurat_pipeline1_biomarkers.pdf")
FeaturePlot(seurat, features = c("Cd19")) + ggtitle("B-cells - Cd19")
FeaturePlot(seurat, features = c("Cd3e")) + ggtitle("T-cells - Cd3")
FeaturePlot(seurat, features = c("S100a8")) + ggtitle("Neutrophils - S100a8")
FeaturePlot(seurat, features = c("S100a9")) + ggtitle("Neutrophils - S100a9")

FeaturePlot(seurat, features = c("C1qa")) + ggtitle("Macrophages - C1qa")
FeaturePlot(seurat, features = c("Mgp")) + ggtitle("Endothelial - Mgp")

FeaturePlot(seurat, features = c("Epcam")) + ggtitle("Epithelial - Epcam")
FeaturePlot(seurat, features = c("Krt18")) + ggtitle("Epithelial - Krt18")

FeaturePlot(seurat, features = c("Krt5")) + ggtitle("Epithelial:Basal - Krt5")
FeaturePlot(seurat, features = c("Krt14")) + ggtitle("Epithelial:Basal - Krt14")

FeaturePlot(seurat, features = c("Ppp1r1b")) + ggtitle("Epithelial:Progenitor - Ppp1r1b")
FeaturePlot(seurat, features = c("Clu")) + ggtitle("Epithelial:Progenitor - Clu")

FeaturePlot(seurat, features = c("Sbp")) + ggtitle("Epithelial:Differentiated - Sbp")
FeaturePlot(seurat, features = c("Psca")) + ggtitle("Urethral - Psca")
dev.off()


pdf("figures/Seurat_pipeline1_umap_by_celltype.pdf", width = 10)
DimPlot(seurat, reduction="umap", group.by="celltype", label=TRUE)+NoLegend()
dev.off()


```