---
title: "Analysis of scRNA-seq data (compilation)"
date: "Last modified: `r format(Sys.time(), '%B %d, %Y')`"
tags: [scRNA-seq, melanoma, immunotherapy, PBMCs] 
output:
  html_document:
    theme: flatly
    highlight: zenburn
    toc: true
    number_sections: false
    toc_depth: 2
    toc_float:
      collapsed: false
      smooth_scroll: true
    code_folding: hide
    self_contained: yes
---

# Project Description

This project aims to identify therapeutic targets for melanoma patients based on scRNAseq data analysis.  
  
## Datasets

Disease datasets:  

  * Dataset 1 (Treatment Response Dataset)
    - Contains treatment response data
    - Includes responder/non-responder classifications
    - Focus on immunotherapy outcomes

  * Dataset 2 (Tumor Cell Dataset)
    - Characterizes tumor cell heterogeneity
    - Includes cell type annotations
    - Focus on tumor cell populations

  * Dataset 3 (Tumor Microenvironment Dataset)
    - Maps the tumor microenvironment
    - Contains malignant/non-malignant classifications
    - Includes detailed immune cell typing
  
Control datasets:  

  * Dataset 4 (Control Melanocyte Dataset)
    - Normal melanocyte control data
    - Reference for non-malignant state
    - Baseline expression profiles

  * Dataset 5 (Control Immune Dataset)
    - Normal immune cell control data
    - Reference for immune cell states
    - Baseline immune profiles
  
## Processes/Analyses

The following processing or analytical steps were conducted:  

  0. Environment initialization
  1. scRNAseq data consolidation and quality control  
  2. Cell clustering based on UMAP dimensionality reduction  
  3. Biomarker identification based on UMAP cluster groups  
  4. Differential gene expression analysis  
  5. Gene set enrichment analysis  
  6. Cell classification using Random Forests  
  7. Output saving (if prompted)  
  
  
# 0. Environment initialization

Install required packages.

```{r install_packages, results="hide", message=F, warning=F, error=F}

# Initiate Renv (for reproducibility)
renv::init()

# Define packages to install
pkg.list = c('renv', 'svDialogs', 'vroom', 'dplyr', 'DT', 'openxlsx', 'progress',
             'ggplot2', 'ggrepel', 'ggvenn', 'ggfortify', 'pheatmap', 
             'patchwork', 'Seurat', 'harmony', 'MAST', 'enrichR', 'UpSetR', 
             'singleCellNet')

# Install MAST (if needed)
if ('MAST' %in% pkg.install) {
  if (!requireNamespace('BiocManager', quietly=T)) {
    install.packages('BiocManager')
  }
  BiocManager::install('MAST')
  pkg.install <- pkg.install[!('MAST' %in% pkg.install)]
}

# Install SingleCellNet (if needed)
if ('singleCellNet' %in% pkg.install) {
  install.packages("devtools")
  devtools::install_github("pcahan1/singleCellNet")
  pkg.install <- pkg.install[!('singleCellNet' %in% pkg.install)]
}

# Define packages not already installed
pkg.install <- pkg.list[!(pkg.list %in% installed.packages()[, 'Package'])]

# Install uninstalled packages
if (length(pkg.install) > 0) {
  install.packages(pkg.install)
}

```

Load installed packages.  

```{r load_packages, results="hide", message=F, warning=F, error=F}

# Load packages
library(renv)           # for version control + reproducibility
library(svDialogs)      # for prompting user-input
library(vroom)          # for quickly reading data
library(dplyr)          # for data processing
library(DT)             # to display datatables
library(rio)            # to load all worksheets in a workbook
library(openxlsx)       # to write data to excel
library(progress)       # to display progress bar
library(ggplot2)        # for data visualization
library(ggrepel)        # to use geom_pont_repel()
library(ggvenn)         # to visualize venn diagrams
library(ggfortify)      # to visualize PCA plots
library(pheatmap)       # to visualize heatmaps
library(patchwork)      # for combining plots
library(Seurat)         # for scRNA-seq analysis
library(harmony)        # to integration of scRNA-seq data
library(MAST)           # for scRNAseq DEG analysis
library(enrichR)        # to perform GSEA
library(UpSetR)         # to visualize multiple set comparisons
library(singleCellNet)  # for RF implementation

```

## Custom functions

`qc_filter()`: ilter data based on QC for scRNA-seq data. 

```{r qc_filter, results="hide", message=F, warning=F, error=F}

qc_filter <- function(obj, feat.t=c(200, 2500), pct.mt.t=5, var.method='vst', 
                      feat.n=2000, qc.plot=T, top.n=10, title='') {
  
  ############################ FUNCTION DESCRIPTION ############################
  # feat.t = lower and upper limits on unique gene counts
  # pct.mt.t = threshold of level in mitochondrial contamination
  # var.method = method for selecting highly variable genes
  # feat.n = number of variable genes to select
  # qc.plot = boolean whether to generate plots to decide downstream analyses
  # title = string to use for plot title
  ############################## BEGIN FUNCTION ################################
  
  # determine percentage of mitochondrial contamination
  obj[['pct.mt']] <- PercentageFeatureSet(obj, pattern='^MT-')
  # filter + nomalize + scale data
  obj <- obj %>% 
    subset(., subset=(nFeature_RNA > feat.t[1]) & (nFeature_RNA < feat.t[2]) & 
             (pct.mt < pct.mt.t)) %>% NormalizeData(.) %>% 
    FindVariableFeatures(., selection.method=var.method) %>% 
    ScaleData(.) %>% RunPCA(., features=VariableFeatures(object=.))
  # generate follow-up QC plots (if prompted)
  if (qc.plot) { 
    p1 <- VariableFeaturePlot(obj) %>% 
      LabelPoints(plot=., points=head(VariableFeatures(obj), top.n), repel=T)
    p2 <- ElbowPlot(obj)
    plot(p1 + p2 + plot_annotation(title=title))
  }
  # return output
  return(obj)
}
```

`de_analyze()`: conduct differential expression (DE) analysis based on unpaired Student's t-test. 

```{r de_analyze, results="hide", message=F, warning=F, error=F}

de_analyze <- function(m1, m2, alt='two.sided', paired=F, var.equal=F, 
                       adj.method='bonferroni', t=0.05, de.plot=F, title='') { 
  
  ############################ FUNCTION DESCRIPTION ############################
  # m1, m2 = expression matrices to compare
  # alt, paired, var.equal = arguments for t.test() function
  # adj.method = method for calculating adjusted p-value
  # t = threshold for significance 
  # de.plot = boolean whether to generate a volcano plot
  ############################## BEGIN FUNCTION ################################
  
  # make sure two matrices have same number of rows
  if (nrow(m1) != nrow(m2)) { 
    stop('Row length does not match between the provided matrices.')
  }
  # make sure gene names align between matrices
  if (!all(rownames(m1) == rownames(m2))) { 
    stop('Gene names do not align between provided matrices.')
    }
  # instantiate output variable
  results <- data.frame(gene=rownames(m1), 
                        t.stat=vector(mode='numeric', length=nrow(m1)), 
                        p.val=vector(mode='numeric', length=nrow(m1)))
  # conduct unpaired t-test with unequal variance for each gene
  pb <- progress_bar$new(
    format='  analyzing [:bar] :percent time left: :eta', total=nrow(m1))
  for (i in 1:nrow(m1)) { 
    pb$tick()
    x <- m1[i, ]; y <- m2[i, ]
    r <- t.test(x, y, alternative=alt, paired=paired, var.equal=var.equal)
    results$t.stat[i] <- r$statistic
    results$p.val[i] <- r$p.value
  }
  # determine adjusted p-values
  results$q.val <- p.adjust(results$p.val, method=adj.method)
  # add additional fields
  results <- results %>%
    mutate(Significance=case_when(q.val < t & t.stat > 0 ~ 'Up',
                                  q.val < t & t.stat < 0 ~ 'Down',
                                  T ~ 'NS')) %>% arrange(q.val)
  # generate volcano plot (if prompted)
  if (de.plot) { 
    p <- results %>% arrange(t.stat) %>% ggplot(data=., 
           aes(x=t.stat, y=-log10(q.val), col=Significance, label=gene)) +
      geom_point() + geom_text_repel() + theme_minimal() + 
      scale_color_manual(values=c('blue', 'black', 'red')) + ggtitle(title)
    plot(p)
  }
  # return output
  return(results)
}

```

## Additional settings.  

```{r settings}

# Adjust system settings
Sys.setenv("VROOM_CONNECTION_SIZE" = 131072 * 2)

# Save plots? (default: F)
save.plots <- dlgInput('Save all plots? (T/F)', F)$res

# Set seed (for reproducibility)
set.seed(123)

```


# 1. scRNAseq data consolidation and quality control

Load all scRNA-seq files or load already saved workspace.  

```{r load_data, warning=F, message=F}

f <- list.files()
if (any(endsWith(f, '.RData'))) {
  load(f[endsWith(f, '.RData')][1])
} else { 
  # Define variables to store data
  obj <- list(); meta <- list()
  
  # Dataset 1: Treatment Response Dataset
  meta$dataset1 <- read.delim(file='data/dataset1/metadata.txt')
  x <- vroom('data/dataset1/counts.txt', col_names=F)
  x <- x[, 1:nrow(meta$dataset1)] %>% as.matrix(.)
  y <- read.delim('data/dataset1/genes.txt', header=F)
  rownames(x) <- make.unique(y$V1)
  colnames(x) <- meta$dataset1$ID
  obj$dataset1 <- CreateSeuratObject(x, project='dataset1',
                                      min.cells=3, min.features=200)
  obj$dataset1$Treatment <- as.factor(meta$dataset1$Treatment)
  obj$dataset1$Response <- as.factor(meta$dataset1$Response)
  rm(x, y); gc()
  
  # Dataset 2: Tumor Cell Dataset
  meta$dataset2 <- read.csv('data/dataset2/metadata.csv')
  x <- vroom('data/dataset2/expression_matrix.csv')
  y <- as.matrix(x[, -1]); rownames(y) <- x$...1
  obj$dataset2 <- CreateSeuratObject(y, project='dataset2',
                                      min.cells=3, min.features=200)
  obj$dataset2$type <- as.factor(meta$dataset2$cell.types)
  
  # Dataset 3: Tumor Microenvironment Dataset
  x <- vroom('data/dataset3/expression_matrix.txt')
  y <- as.matrix(x[-c(1:3), -1]); rownames(y) <- x$Cell[-c(1:3)]
  obj$dataset3 <- CreateSeuratObject(y, project='dataset3',
                                     min.cells=3, min.features=200)
  meta$dataset3 <- data.frame(Tumor=t(x[1, -1]),
                              Malignant=case_when(x[2, -1] == 1 ~ 'No',
                                                  x[2, -1] == 2 ~ 'Yes',
                                                  x[2, -1] == 0 ~ 'Unresolved'),
                              NMType=case_when(x[3, -1] == 1 ~ 'T',
                                               x[3, -1] == 2 ~ 'B',
                                               x[3, -1] == 3 ~ 'Macro',
                                               x[3, -1] == 4 ~ 'Endo',
                                               x[3, -1] == 5 ~ 'CAF',
                                               x[3, -1] == 6 ~ 'NK',
                                               x[3, -1] == 0 ~ 'NA'))
  obj$dataset3$Malignant <- meta$dataset3$Malignant
  obj$dataset3$NMType <- meta$dataset3$NMType

  # Dataset 4: Control Melanocyte Dataset
  x <- vroom('data/dataset4/expression_matrix.csv')
  y <- as.matrix(x[, -1]); rownames(y) <- x$...1
  obj$dataset4 <- CreateSeuratObject(y, project='dataset4',
                                      min.cells=3, min.features=200)
  
  # Dataset 5: Control Immune Dataset
  meta$dataset5 <- read.table('data/dataset5/metadata.tsv',
                               sep='\t', header=T)
  x <- vroom('data/dataset5/matrix.tsv')
  y <- as.matrix(x[, -1]); rownames(y) <- x$Gene.Name
  obj$dataset5 <- CreateSeuratObject(y, project='dataset5',
                                      min.cells=3, min.features=200)
  obj$dataset5$sample <- as.factor(meta$dataset5$sample)
  
  # Clear unnecessary memory
  rm(f, x, y, meta); gc()
}

```

Combine all datasets together into single Seurat object. Also apply `harmony` to remove clustering bias based on dataset source.  

```{r combine_data, warning=F, message=F} 

# Combine all datasets
if (!exists('data.all')) { 
  data.all <- list()
}
if (!('raw' %in% names(data.all))) { 
  data.all$raw <- merge(obj$dataset1, obj$dataset2) %>%
    merge(., obj$dataset3) %>% merge(., obj$dataset4) %>% merge(., obj$dataset5)
}

# Add source information
if (!('Source' %in% names(data.all$raw@meta.data))) { 
  data.all$raw$Source <- c(rep(names(obj)[1], ncol(obj$dataset1)),
                           rep(names(obj)[2], ncol(obj$dataset2)),
                           rep(names(obj)[3], ncol(obj$dataset3)),
                           rep(names(obj)[4], ncol(obj$dataset4)),
                           rep(names(obj)[5], ncol(obj$dataset5)))
}

# Apply QC
if (!('proc' %in% names(data.all))) { 
  data.all$proc <- qc_filter(data.all$raw)
}

# Visualize integration results
p1 <- DimPlot(object=data.all$proc, reduction='pca', pt.size=0.1, 
              group.by='Source')
p2 <- VlnPlot(object=data.all$proc, features='PC_1', pt.size=0.1, 
              group.by='Source')
p1 + p2
if (save.plots) { 
  ggsave('plots/QC_no_harmony.pdf', width=10, height=5, units='in')
}

# Apply harmony (to remove clustering based on dataset source)
if (!('harmony' %in% names(data.all$proc@reductions))) { 
  data.all$proc <- data.all$proc %>% RunHarmony('Source', plot_convergence=T)
}

# Visualize integration results (after harmony)
p1 <- DimPlot(object=data.all$proc, reduction='harmony', pt.size=0.1, 
              group.by='Source')
p2 <- VlnPlot(object=data.all$proc, features='harmony_1', pt.size=0.1, 
              group.by='Source')
p1 + p2
if (save.plots) { 
  ggsave('plots/QC_w_harmony.pdf', width=10, height=5, units='in')
}

```


# 2. Cell clustering based on UMAP dimensionality reduction

Cluster cells based on uniform manifold approximation and projection (UMAP).  

```{r UMAP_clustering, warning=F, message=T}

# Visualize UMAP plots (runtime: ~5 minutes)
if (!('umap' %in% names(data.all$proc@reductions))) { 
  data.all$proc <- data.all$proc %>% 
      RunUMAP(reduction='harmony', dims=1:20) %>% 
      FindNeighbors(reduction='harmony', dims=1:20) %>% 
      FindClusters(resolution=0.5) %>% identity()
}

#   by dataset source
p <- DimPlot(data.all$proc, reduction='umap', group.by='Source', pt.size=0.1, 
        split.by='Source') + ggtitle('UMAP split by dataset source'); plot(p)
#   by cluster (unlabeled)
p <- DimPlot(data.all$proc, reduction='umap', label=T, pt.size=0.1) + 
  ggtitle('UMAP of combined scRNA-seq data (unlabeled)'); plot(p)
if (save.plots) { 
  ggsave('plots/UMAP_unlabeled.pdf', width=10, height=5, units='in')
}

#   by cluster (labeled)
lab <- c('Tumor cell', 'Technical error 1', 'Cytotoxic CD8 T cell 1', 'B cell', 
         'Melanocytes 1', 'Macrophage', 'Cytotoxic CD8 T cell 2', 'Technical error 2', 
         'Memory T cell', 'Melanocytes 2', 'Dysfunctional CD8 T cell', 
         'Cytotoxic CD8 T cell 3', '? 1', 'Melanocytes 3', 'Activated cell', 
         'Exhausted T cell', 'Cytotoxic T cell', '? 2', '? 3', 'Melanocytes 4', '? 4')
names(lab) <- levels(data.all$proc)
plot.data <- data.all$proc %>% RenameIdents(., lab)
p <- DimPlot(plot.data, reduction='umap', label=T, pt.size=0.1) + 
  ggtitle('UMAP of combined scRNA-seq data (labeled)'); plot(p)
if (save.plots) { 
  ggsave('plots/UMAP_labeled.pdf', width=10, height=5, units='in')
}

```


# 3. Biomarker identification based on UMAP cluster groups

Determine biomarkers based on UMAP cluster assignment. 

```{r biomarkers, warning=F, message=T}

# Find all biomarkers based on clustering (runtime: ~30 minutes)
if (!('bm' %in% names(data.all))) { 
  data.all$bm <- FindAllMarkers(data.all$proc, min.pct=0.25, logfc.threshold=0.25)
}

# View table of top 3 biomarkers for each cluster
d <- data.all$bm %>% group_by(cluster) %>% slice_max(n=3, order_by=avg_log2FC)
datatable(d)

# Visualize ridge plots based on biomarkers of interest
ridge.feat <- dlg_list(sort(unique(d$gene)), multiple=T)$res
p <- RidgePlot(data.all$proc, features=ridge.feat, 
          ncol=ceiling(length(ridge.feat) / 2)); plot(p)
if (save.plots) { 
  ggsave('plots/biomarker_ridge_plots.pdf', width=10, height=10, units='in')
}

```


# 4. Differential gene expression analysis

Determine differentially expressed genes (DEGs) for the following group comparisons:  

  * Disease vs. control (tumor)  
  * Disease vs. control (immune cells, bulk)  
  * Disease vs. control (immune cells, cluster-based)  
  * Responder vs. non-responder (specific to GSE120575)  
  
Of note, DEGs were determined using a custom function for unpaired Student's t-test and [MAST](https://bioconductor.org/packages/release/bioc/html/MAST.html). 
```{r DE_initialization, warning=F, message=T, eval=!('data' %in% de)}

# Instantiate DE variable + relevant fields
de <- list(); de$ttest <- list(); de$mast <- list()

# Define data to work with
de$data <- GetAssayData(object=data.all$proc, slot='data')

```

## Case: disease vs. control (tumor)

Comparison between benign vs. tumor skin cells. 
Estimated runtime: ~15 minutes. 

```{r DE_tumor, warning=F, message=T, eval=!('tumor' %in% de$ttest)}

# Start timer
t.start <- Sys.time()

# Define cell groups to compare
ix <- intersect(rownames(obj$dataset3), rownames(obj$dataset2)) %>%
  intersect(., rownames(obj$dataset4))
jx1 <- data.all$proc$Source == names(obj)[3] | data.all$proc$Malignant %in% 'Yes'
jx2 <- data.all$proc$Source == names(obj)[4]

# DE analysis (t-test)
x <- de$data[ix, jx1]; y <- de$data[ix, jx2]
de$ttest$tumor <- de_analyze(x, y) %>% na.omit

# DE analysis (MAST)
x <- cbind(de$data[ix, jx1], de$data[ix, jx2]) %>% CreateSeuratObject(.)
Idents(object=x, cells=1:sum(jx1)) <- 'Disease'
Idents(object=x, cells=sum(jx1)+1:ncol(x)) <- 'Control'
de$mast$tumor <- FindMarkers(object=x, ident.1='Disease', ident.2='Control', 
                             test.use='MAST')

# End timer + log time elapsed
t.end <- Sys.time()
t.end - t.start

```

## Case: disease vs. control (immune cells, bulk)

Bulk comparison between healthy vs. diseased immune cells. 
Estimated runtime: ~20 minutes. 

```{r DE_immune_bulk,warning=F, message=T, eval=!('immune' %in% de$ttest)}

# Start timer
t.start <- Sys.time()

# Define cell groups to compare
ix <- intersect(rownames(obj$dataset1), rownames(obj$dataset2)) %>% 
  intersect(., rownames(obj$dataset5))
jx1 <- data.all$proc$Source == names(obj)[1] | data.all$proc$Malignant %in% 'No'
jx2 <- data.all$proc$Source == names(obj)[5]

# DE analysis (t-test)
x <- de$data[ix, jx1]; y <- de$data[ix, jx2]
de$ttest$immune <- de_analyze(x, y) %>% na.omit()

# DE analysis (MAST)
x <- cbind(de$data[ix, jx1], de$data[ix, jx2]) %>% CreateSeuratObject(.)
Idents(object=x, cells=1:sum(jx1)) <- 'Tumor'
Idents(object=x, cells=sum(jx1)+1:ncol(x)) <- 'Benign'
de$mast$immune <- FindMarkers(object=x, ident.1='Tumor', ident.2='Benign', 
                              test.use='MAST')

# End timer + log time elapsed
t.end <- Sys.time()
t.end - t.start

```

## Case: disease vs. control (immune cells, cluster-based)

Immune cell-specific comparisons between healthy vs. diseased cells. 
Estimated runtime: ~15 minutes. 

```{r DE_immune_cluster, warning=F, message=T, eval=!('B' %in% de$ttest)}

# Start timer
t.start <- Sys.time()

# Define labeled scRNAseq data
lab <- c('Tumor cell', 'Technical error 1', 'Cytotoxic CD8 T cell 1', 'B cell', 
         'Melanocytes 1', 'Macrophage', 'Cytotoxic CD8 T cell 2', 'Technical error 2', 
         'Memory T cell', 'Melanocytes 2', 'Dysfunctional CD8 T cell', 
         'Cytotoxic CD8 T cell 3', '? 1', 'Melanocytes 3', 'Activated cell', 
         'Exhausted T cell', 'Cytotoxic T cell', '? 2', '? 3', 'Melanocytes 4', '? 4')
names(lab) <- levels(data.all$proc)
plot.data <- data.all$proc %>% RenameIdents(., lab)

# Iterate through each immune cell group (runtime: ~5 minutes)
immune.cells <- c('B', 'CD8', 'Macrophage', 'Memory', 'Activated')
for (i in 1:length(immune.cells)) { 
  
  # Define row + column indices
  ix <- intersect(rownames(obj$dataset1), rownames(obj$dataset2)) %>% 
    intersect(., rownames(obj$dataset5))
  c <- grepl(immune.cells[i], Idents(plot.data))
  jx1 <- (plot.data$Source == names(obj)[1] | 
            data.all$proc$Malignant %in% 'No') & c
  jx2 <- data.all$proc$Source == names(obj)[5] & c
  
  # # Print number of genes + cells
  # cat(sprintf('No. of genes: %d\tNo. of G1: %d\tNo. of G2: %d\n', 
  #             length(ix), sum(jx1), sum(jx2)))
  
  # DE analysis (t-test)
  x <- de$data[ix, jx1]; y <- de$data[ix, jx2]
  de$ttest[[immune.cells[i]]] <- de_analyze(x, y) %>% na.omit()

  # DE analysis (MAST)
  x <- cbind(de$data[ix, jx1], de$data[ix, jx2]) %>% CreateSeuratObject(.)
  Idents(object=x, cells=1:sum(jx1)) <- 'Tumor'
  Idents(object=x, cells=sum(jx1)+1:ncol(x)) <- 'Benign'
  de$mast[[immune.cells[i]]] <- FindMarkers(object=x, ident.1='Tumor',
                                            ident.2='Benign', test.use='MAST')
}

# End timer + log time elapsed
t.end <- Sys.time()
t.end - t.start

```

## Case: responder vs. non-responder (GSE120575)

Comparison between responder vs. non-responder cells to immunotherapy. 
Estimated runtime: ~1 hour.  

```{r DE_response, warning=F, message=T, eval=!('response' %in% de$ttest)}

# Start timer
t.start <- Sys.time()

# Define cell groups to compare
ix <- rownames(obj$dataset1)
jx1 <- data.all$proc$Response %in% 'Responder'
jx2 <- data.all$proc$Response %in% 'Non-responder'

# DE analysis (t-test)
x <- de$data[ix, jx1]; y <- de$data[ix, jx2]
de$ttest$response <- de_analyze(x, y) %>% na.omit

# DE analysis (MAST)
x <- cbind(de$data[ix, jx1], de$data[ix, jx2]) %>% CreateSeuratObject(.)
Idents(object=x, cells=1:sum(jx1)) <- 'Responder'
Idents(object=x, cells=sum(jx1)+1:ncol(x)) <- 'Non-responder'
de$mast$response <- FindMarkers(object=x, ident.1='Responder', 
                                ident.2='Non-responder', test.use='MAST')

# End timer + log time elapsed
t.end <- Sys.time()
t.end - t.start

```

## Visualizations

**Description**: visual inspection of DE analysis results. 

### T-test vs. MAST DEGs

Visual comparison of DEGs determined with t-test vs. MAST (venn diagrams).

```{r DE_venn, warning=F, message=T}

# Compare DEGs b/t the two methods
f <- names(de$mast); p <- list()
for (i in 1:length(f)) { 
  x <- de$ttest[[f[i]]]; y <- de$mast[[f[i]]]
  g <- intersect(x$gene[x$q.val < 0.05], row.names(y)[y$p_val_adj < 0.05])
  # g <- intersect(x$gene[x$q.val < 0.05], y$gene[y$p_val_adj < 0.05]) 
  cat(sprintf('No. of intersecting DEGs for %s: %d\n', f[i], length(g)))
  ggvenn(list('t-test'=x$gene[x$q.val < 0.05], 
              'MAST'=row.names(y)[y$p_val_adj < 0.05])) + ggtitle(f[i])
  ggsave(paste0('plots/DE_venn_', f[i], '.pdf'), width=5, height=5, units='in')
}

```

### Responder vs. non-responder DEGs

Clustergram (or heatmap) of DEGs identified for responder vs. non-responder comparison. 

```{r DE_response_heat, warning=F, message=T}

# Define data to plot
n <- 50
g <- de$mast$response$gene[1:n]
jx1 <- which(data.all$proc$Response %in% 'Responder')[1:(n/2)]
jx2 <- which(data.all$proc$Response %in% 'Non-responder')[1:(n/2)]
x <- de$data[g, c(jx1, jx2)] %>% as.data.frame(.)

# Specify heatmap arguments
#   color palette
col.pal <- colorRampPalette(colors=c('white', 'red'), space='Lab')(100)
#   column annotations (response)
col.annot <- data.frame(Response=rep(c('Responder', 'Non-responder'), each=n/2), 
                        row.names=colnames(x))
  
# Heatmap
p <- pheatmap(x, cluster_rows=T, cluster_cols=F, scale='none', color=col.pal, 
              annotation_col=col.annot, cellheight=(500/n), cellwidth=(500/n), 
              show_rownames=T, show_colnames=F, gaps_col=n/2, 
              main='DEG heatmap (response)')
  
# Save plot
tiff('plots/DE_heatmap_response.tiff', units='in', width=11, height=8, res=300)
print({p}); dev.off()

```

PCA of DEGs identified for responder vs. non-responder comparison. 

```{r DE_response_PCA, warning=F, message=T}

# Define data to plot
g <- de$mast$response$gene[de$mast$response$p_val_adj < 0.05]
jx <- data.all$proc$Response %in% c('Responder', 'Non-responder')
x <- de$data[g, jx] %>% as.data.frame(.) %>% t(.)
m <- data.frame(Response=data.all$proc$Response[jx])

# Apply PCA
response_pca <- prcomp(x, scale.=T)
  
# Visualize PCA plot
autoplot(response_pca, data=m, colour='Response') + ggtitle('DEG PCA (response)')
  
# Save plot
ggsave('plots/DE_PCA_response.tiff', units='in', width=7, height=5, dpi=300)

```

### Tumor vs. benign DEGs

Clustergram (or heatmap) of DEGs identified for tumor vs. benign comparison. 

```{r DE_tumor_heat, warning=F, message=T}

# Define data to plot
n <- 50
g <- de$mast$tumor$gene[1:n]
jx1 <- which(data.all$proc$Source == names(obj)[3] | 
  data.all$proc$Malignant %in% 'Yes')[1:(n/2)]
jx2 <- which(data.all$proc$Source == names(obj)[4])[1:(n/2)]
x <- de$data[g, c(jx1, jx2)] %>% as.data.frame(.)

# Specify heatmap arguments
#   color palette
col.pal <- colorRampPalette(colors=c('white', 'red'), space='Lab')(100)
#   column annotations (response)
col.annot <- data.frame(Malignant=rep(c('Yes', 'No'), each=n/2), 
                        row.names=colnames(x))
  
# Heatmap
p <- pheatmap(x, cluster_rows=T, cluster_cols=F, scale='none', color=col.pal, 
              annotation_col=col.annot, cellheight=(500/n), cellwidth=(500/n), 
              show_rownames=T, show_colnames=F, gaps_col=n/2, 
              main='DEG heatmap (tumor)')
  
# Save plot
tiff('plots/DE_heatmap_tumor.tiff', units='in', width=11, height=8, res=300)
print({p}); dev.off()

```

PCA of DEGs identified for tumor vs. benign comparison. 

```{r DE_tumor_PCA, warning=F, message=T}

# Define data to plot
g <- de$mast$tumor$gene[1:100]
jx1 <- data.all$proc$Source == names(obj)[3] | data.all$proc$Malignant %in% 'Yes'
jx2 <- data.all$proc$Source == names(obj)[4]
x <- cbind(de$data[g, jx1], de$data[g, jx2]) %>% as.data.frame(.) %>% t(.)
m <- data.frame(Malignant=rep(c('Yes', 'No'), c(sum(jx1), sum(jx2))))

# Apply PCA
pca.tumor <- prcomp(x, scale.=T)
  
# Visualize PCA plot
autoplot(pca.tumor, data=m, colour='Malignant') + ggtitle('DEG PCA (tumor)')
  
# Save plot
ggsave('plots/DE_PCA_tumor.tiff', units='in', width=7, height=5, dpi=300)

```


# 5. Gene set enrichment analysis

Determine enriched pathways based on the DEG results (from MAST) using [enrichR](https://cran.r-project.org/web/packages/enrichR/vignettes/enrichR.html)..
Total runtime: ~2 minutes.  

```{r GSEA_analysis, warning=F, message=T}

# Start timer
t.start <- Sys.time()

# Instantiate GSEA variable
gsea <- list()

# Ensure comparison to human genes
setEnrichrSite('Enrichr')

# Select databases to inquire
dbs <- dlg_list(sort(listEnrichrDbs()$libraryName), multiple=T)$res

# Loop through all DE fields
f <- names(de$mast)
for (i in 1:length(f)) {
  x <- de$mast[[f[i]]]
  y <- enrichr(x$gene[x$p_val_adj < 0.05], dbs)
  z <- y$GO_Biological_Process_2021
  n <- sum(z$Adjusted.P.value < 0.05)
  gsea[[f[i]]] <- z[z$Adjusted.P.value < 0.05, ]
  cat(sprintf('No. of enriched pathways for %s: %d\n', f[i], n))
  plotEnrich(z, orderBy='Adjusted.P.value') + 
    ggtitle(sprintf('Case: %s (Total = %d)', f[i], n))
  ggsave(paste0('plots/GSEA_', f[i], '.pdf'), width=7, height=5, units='in')
}

# End timer + log time elapsed
t.end <- Sys.time()
t.end - t.start

```

Visualize intersecting pathways between comparisons.  

```{r GSEA_intersect, warning=F, message=T}

# Define plots data
df <- list('tumor'=gsea$tumor$Term, 
           'immune'=gsea$immune$Term, 
           'B'=gsea$B$Term, 
           'CD8'=gsea$CD8$Term, 
           'Macrophage'=gsea$Macrophage$Term, 
           'Memory'=gsea$Memory$Term, 
           'Activated T'=gsea$Activated$Term, 
           'response'=gsea$response$Term)

# Generate and save UpSet plot
pdf(file='plots/GSEA_intersect.pdf', width=11, height=8)
upset(fromList(df), sets=names(df), order.by='freq')

```


# 6. Cell classification using Random Forests

Apply Random Forests to construct a cell type classifier using [SingleCellNet](https://github.com/pcahan1/singleCellNet).

Define training + testing data. 

```{r ML_train_test, warning=F, message=T}

# Pull required data
s <- extractSeurat(data.all$proc, exp_slot_name='counts')
rm(data.all); gc()
st <- s$sampTab %>% mutate(ID=rownames(.))
exp <- s$expDat %>% as(., 'dgCMatrix')

# Define label field
label <- dlg_list(title='Choose label: ', sort(colnames(st)), multiple=F)$res
ID <- dlg_list(title='Choose cell ID: ', sort(colnames(st)), multiple=F)$res

# Re-order by label field
ix <- order(st[[label]])
st <- st[ix, ]
exp <- exp[, ix]

# Train/Test split
stList1 <- splitCommon(sampTab=st, ncells=100, dLevel=label)
stTrain <- stList1[[1]]
expTrain <- exp[, rownames(stTrain)]
stList2 <- splitCommon(sampTab=stList1[[2]], ncells=100, dLevel=label)
stTest <- stList2[[1]]
expTest <- exp[, rownames(stTest)]

```

Apply SCN (runtime: ~5 minutes). 

```{r SCN, warning=F, message=T}

# Start timer
t.start <- Sys.time()

# Train model
x <- scn_train(stTrain=stTrain, expTrain=expTrain, nTopGenes=10, nRand=70, 
               nTrees=1000, nTopGenePairs=25, dLevel=label)

# Test model
y <- scn_predict(cnProc=x[['cnProc']], expDat=expTest, nrand=50)

# End timer + log time elapsed
t.end <- Sys.time()
t.end - t.start

```

Model assessment and visualization of results.  

```{r ML_evaluation, warning=F, message=F}

# Assess model
z <- assess_comm(ct_scores=y, stTrain=stTrain, stQuery=stTest, dLevelSID=ID,
                 classTrain=label, classQuery=label)
plot_PRs(z) + ggtitle('Model performance (test set)')
if (save.plots) {
  ggsave('plots/ML_performance.pdf', width=5, height=5, units='in')
}

# Visualize results
#   classification results
nrand <- 50
sla <- as.vector(stTest[[label]])
names(sla) <- as.vector(rownames(stTest))
slaRand <- rep('rand', nrand)
names(slaRand) <- paste('rand_', 1:nrand, sep='')
sla <- append(sla, slaRand)
p <- sc_hmClass(classMat=y, grps=sla, max=300, isBig=T)
if (save.plots) {
  pdf('plots/ML_classification.pdf', width=7, height=5,
      title='Classification results (test set)')
  p
}
#   attribution plot
plot_attr(classRes=y, sampTab=stTest, nrand=nrand, sid=ID, dLevel=label) + 
  xlab('Predicted group') + ylab('True class ratio') + 
  ggtitle('Attribution plot (test set)')
if (save.plots) {
  ggsave('plots/ML_attribution.pdf', width=7, height=5, units='in')
}

```


# 7. Output saving (if prompted)

```{r save_outputs, warning=F, message=T}

# Save outputs
save.data <- dlgInput('Save all results? (T/F)', F)$res %>% as.logical(.)
if (save.data) { 
  # Biomarker results
  xl.list <- list('biomarkers'=data.all$bm)
  write.xlsx(xl.list, 'results/biomarkers.xlsx', row.names=F)
  # DEG results
  write.xlsx(de$ttest, 'results/DE_ttest.xlsx', row.names=F)
  write.xlsx(de$mast, 'results/DE_MAST.xlsx', row.names=T)
  # GSEA results
  write.xlsx(gsea, 'results/GSEA.xlsx', row.names=F)
}

# Save workspace
save.wksp <- dlgInput('Save R workspace? (T/F)', F)$res %>% as.logical(.)
if (save.wksp) { 
  # Save workspace (runtime: ~2 hours)
  save.image(file='scRNAseq_wksp.RData')
}

# Save project state (for reproducibility)
renv::snapshot()

```

# Visualize data

```{r visualize_data, warning=F, message=F, fig.width=12, fig.height=6}

# Create UMAP plots
p1 <- DimPlot(data.all$proc, reduction='umap', group.by='Source',
              label=T, repel=T) +
  ggtitle('Dataset Source')

p2 <- DimPlot(data.all$proc, reduction='harmony', group.by='Source',
              label=T, repel=T) +
  ggtitle('Dataset Source (Harmony)')

# Combine plots
p1 + p2

# Save plot
ggsave('plots/UMAP_integration.pdf', width=12, height=6)

```

# Visualize tumor vs melanocyte comparison

```{r visualize_tumor, warning=F, message=F, fig.width=12, fig.height=6}

# Create volcano plot
p1 <- EnhancedVolcano(tumor.vs.melanocyte,
                      lab=rownames(tumor.vs.melanocyte),
                      x='avg_log2FC', y='p_val_adj',
                      pCutoff=0.05, FCcutoff=1,
                      title='Tumor vs Melanocyte DEGs')

# Create heatmap
top.genes <- rownames(tumor.vs.melanocyte)[tumor.vs.melanocyte$p_val_adj < 0.05]
top.genes <- top.genes[order(abs(tumor.vs.melanocyte$avg_log2FC[
  tumor.vs.melanocyte$p_val_adj < 0.05]), decreasing=T)][1:50]

plot.data <- subset(data.all$proc,
                    subset=Source %in% c(names(obj)[3:4]))
plot.data <- ScaleData(plot.data, features=top.genes)

p2 <- DoHeatmap(plot.data, features=top.genes,
                group.by='Source') +
  ggtitle('Top 50 Tumor vs Melanocyte DEGs')

# Combine plots
p1 + p2

# Save plot
ggsave('plots/tumor_vs_melanocyte.pdf', width=12, height=6)

```

# Visualize immune vs non-malignant comparison

```{r visualize_immune, warning=F, message=F, fig.width=12, fig.height=6}

# Create volcano plot
p1 <- EnhancedVolcano(immune.vs.nonmalignant,
                      lab=rownames(immune.vs.nonmalignant),
                      x='avg_log2FC', y='p_val_adj',
                      pCutoff=0.05, FCcutoff=1,
                      title='Immune vs Non-malignant DEGs')

# Create heatmap
top.genes <- rownames(immune.vs.nonmalignant)[
  immune.vs.nonmalignant$p_val_adj < 0.05]
top.genes <- top.genes[order(abs(immune.vs.nonmalignant$avg_log2FC[
  immune.vs.nonmalignant$p_val_adj < 0.05]), decreasing=T)][1:50]

plot.data <- subset(data.all$proc,
                    subset=Source %in% c(names(obj)[c(1,2,5)]))
plot.data <- ScaleData(plot.data, features=top.genes)

p2 <- DoHeatmap(plot.data, features=top.genes,
                group.by='Source') +
  ggtitle('Top 50 Immune vs Non-malignant DEGs')

# Combine plots
p1 + p2

# Save plot
ggsave('plots/immune_vs_nonmalignant.pdf', width=12, height=6)

```

# Visualize immune cell type comparisons

```{r visualize_immune_types, warning=F, message=F, fig.width=12, fig.height=6}

# Create plots for each cell type
for (i in 1:length(immune.cells)) {
  # Read results
  immune.cell.type <- read.csv(paste0('results/',
                                     immune.cells[i],
                                     '_cell_DEGs.csv'))
  
  # Create volcano plot
  p1 <- EnhancedVolcano(immune.cell.type,
                        lab=rownames(immune.cell.type),
                        x='avg_log2FC', y='p_val_adj',
                        pCutoff=0.05, FCcutoff=1,
                        title=paste0(immune.cells[i], ' Cell DEGs'))
  
  # Create heatmap
  top.genes <- rownames(immune.cell.type)[immune.cell.type$p_val_adj < 0.05]
  top.genes <- top.genes[order(abs(immune.cell.type$avg_log2FC[
    immune.cell.type$p_val_adj < 0.05]), decreasing=T)][1:50]
  
  plot.data <- subset(data.all$proc,
                      subset=Source %in% c(names(obj)[c(1:3,5)]))
  plot.data <- ScaleData(plot.data, features=top.genes)
  
  p2 <- DoHeatmap(plot.data, features=top.genes,
                  group.by='Source') +
    ggtitle(paste0('Top 50 ', immune.cells[i], ' Cell DEGs'))
  
  # Combine plots
  p <- p1 + p2
  
  # Save plot
  ggsave(paste0('plots/', immune.cells[i], '_cell_DEGs.pdf'),
         plot=p, width=12, height=6)
}

```

# Visualize responder vs non-responder comparison

```{r visualize_response, warning=F, message=F, fig.width=12, fig.height=6}

# Create volcano plot
p1 <- EnhancedVolcano(response.vs.nonresponse,
                      lab=rownames(response.vs.nonresponse),
                      x='avg_log2FC', y='p_val_adj',
                      pCutoff=0.05, FCcutoff=1,
                      title='Response vs Non-response DEGs')

# Create heatmap
top.genes <- rownames(response.vs.nonresponse)[
  response.vs.nonresponse$p_val_adj < 0.05]
top.genes <- top.genes[order(abs(response.vs.nonresponse$avg_log2FC[
  response.vs.nonresponse$p_val_adj < 0.05]), decreasing=T)][1:50]

plot.data <- subset(data.all$proc,
                    subset=Source %in% names(obj)[1])
plot.data <- ScaleData(plot.data, features=top.genes)

p2 <- DoHeatmap(plot.data, features=top.genes,
                group.by='Response') +
  ggtitle('Top 50 Response vs Non-response DEGs')

# Combine plots
p1 + p2

# Save plot
ggsave('plots/response_vs_nonresponse.pdf', width=12, height=6)

```
