---
title: "Meta-analysis Testing"
output: html_document
---
```{r}
# Load required libraries
library(SingleCellExperiment)
library(Seurat)
library(tidyverse)
library(MetaNeighbor)
```

```{r eval = FALSE}
# Load the Seurat objects
hSeurat <- readRDS("hSeurat_annotated.rds")
vtSeurat <- readRDS("vtSeurat2_annotated.rds")

# Convert to SCE objects
hSCE <- as.SingleCellExperiment(hSeurat)
vtSCE <- as.SingleCellExperiment(vtSeurat)

# Create my_data
mydata <- list(hSCE = hSCE, 
               vtSCE = vtSCE)

# Check whether the gene information align between the two datasets
lapply(mydata, function(x) head(rownames(x), 3))

# cell-type information is labeled identically in all datasets
lapply(mydata, function(x) colnames(colData(x)))

# add the cell type information onto a new column name called "cell type"
mydata$hSCE$"cell type" <- mydata$hSCE$ident
mydata$vtSCE$"cell type" <- mydata$vtSCE$ident

# Check that count matrices stored in the assays slot, have identical names
lapply(mydata, function(x) names(assays(x)))

# Create a merged dataset by using mergeSCE, which takes a list of SCE objects
# as an input and outputs a single SCE object
fused_data <- mergeSCE(mydata)
dim(fused_data)
dim(hSCE)
dim(vtSCE)

head(colData(fused_data))

# To avoid having to recreate the merged object, save the R object to a file
# by using R's RDS format.

saveRDS(fused_data, "merged_2_datasets.rds")
```

We notice that the fused data contains the cells from both datasets, as the # of columns is the sum of the two datasets.

```{r}
# Load RDS file
two_datasets <- readRDS("merged_2_datasets.rds")

# To obtain a cursory overview of cell-type composition by study, we cross-tabulate cell-type annotations by study IDs

table(two_datasets$"cell type", two_datasets$study_id)
# It is okay to have identical cell type names at this stage.
```

<Procedure 1>: Hierarchical cell-type replicability analysis
```{r, fig.height = 8, fig.width = 10}
# Picking the highly variable genes
global_hvgs <- variableGenes(dat = two_datasets,
                            exp_labels = two_datasets$study_id)

length(global_hvgs)

# MetaNeighborUS returns a cell-type-by-cell-type matrix containing cell-type similarities.
# This fast version should always be used on large datasets but can also be run on smaller datasets.

aurocs <- MetaNeighborUS(var_genes = global_hvgs,
  dat = two_datasets,
  study_id = two_datasets$study_id,
  cell_type = two_datasets$"cell type",
  fast_version = TRUE)

# Apparently, we can use this line of code as well.
MetaNeighbor::ggPlotHeatmap(aurocs, label_size = 10)

# To identify pairs of replicable cell types, run the following function
top_hits <- topHits(aurocs, 
                    dat = two_datasets, 
                    study_id = two_datasets$study_id,
        cell_type = two_datasets$"cell type", threshold = 0.9)

top_hits
```

```{r fig.height = 8, fig.width = 10}
# When there is a clear structure in the data, we can refine AUROCs by
# splitting the data. 
level1_split <- splitClusters(aurocs, k = 2)
level1_split

first_split <- level1_split[[1]]
first_split

# Repeat the MetaNeighbor analysis on immune cells only
full_labels <- makeClusterName(two_datasets$study_id,
                               two_datasets$`cell type`)
subdata <- two_datasets[, full_labels %in% first_split]
dim(subdata)

# To focus on variability specific to endocrine cells, re-pick highly variable genes
var_genes <- variableGenes(dat = subdata, exp_labels = subdata$study_id)

# recompute cell-type similarities and visualize AUROCs
aurocs <- MetaNeighborUS(var_genes = var_genes,
                         dat = subdata, fast_version = TRUE,
                         study_id = subdata$study_id,
                         cell_type = subdata$"cell type")
plotHeatmap(aurocs, cex = 0.7)

# Continue to zoom in as long as there are at least two cell types per dataset.
# Repeat steps 13-16

level2_split <- splitClusters(aurocs, k = 3)
level2_split

my_split <- level2_split[[2]]
my_split

subdata <- two_datasets[, full_labels %in% my_split]
var_genes <- variableGenes(dat = subdata, exp_labels = subdata$study_id)

length(var_genes)
aurocs <- MetaNeighborUS(var_genes = var_genes, dat = subdata,
                         fast_version = TRUE,
                         study_id = subdata$study_id,
                         cell_type = subdata$"cell type")
plotHeatmap(aurocs, cex = 1)
```

However, if we use one-vs-best AUROCs, MetaNeighbor can automatically identify the two closest matching cell types in each target dataset.

```{r fig.height = 8, fig.width = 10}
best_hits <- MetaNeighborUS(var_genes = global_hvgs,
                            dat = two_datasets,
                            study_id = two_datasets$study_id,
                            cell_type = two_datasets$"cell type",
                            fast_version = TRUE,
                            one_vs_best = TRUE, symmetric_output = FALSE)
plotHeatmap(best_hits, cex = 0.5)
```
```{r}
# Extracting cell types as meta-clusters
mclusters <- extractMetaClusters(best_hits, threshold = 0.7)
mcsummary <- scoreMetaClusters(mclusters, best_hits)

# scoreMetaClusters() function provides a good summary of meta-clusters

# Visualize the number of robust cell types, the replicability structure
# can be summarized as an Upset plot with plotUpset()

plotUpset(mclusters)

# Save the output as pdf.
pdf("meta_clusters.pdf")
plotMetaClusters(mclusters, best_hits)
while (!is.null(dev.list()))  dev.off()
```

```{r fig.height = 6, fig.width = 8}
cluster_graph <- makeClusterGraph(best_hits, low_threshold = 0.3)
plotClusterGraph(cluster_graph, two_datasets$study_id, 
                 two_datasets$"cell type", size_factor = 3)

# We can focus on a cluster of interest (coi).
# Take a closer look at 'hSCE|Dendritic cell', query its closest neighbors in the graph with extendClusterSet() and then zoom in on its subgraph with subsetClusterGraph()

coi <- "hSCE|Dendritic cell"
coi <- extendClusterSet(cluster_graph, initial_set = coi,
                        max_neighbor_distance = 2)
subgraph <- subsetClusterGraph(cluster_graph, coi)
plotClusterGraph(subgraph, two_datasets$study_id,
                 two_datasets$"cell type", size_factor = 5)
```