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


```{r eval = FALSE}
# Load the Seurat objects
hSeurat <- readRDS("/home/youkim/MetaMarkers/4-dataset-test/hSeurat_annotated.rds")
vtSeurat <- readRDS("/home/youkim/MetaMarkers/4-dataset-test/vtSeurat2_annotated.rds")
lSeurat <- readRDS("/home/youkim/MetaMarkers/4-dataset-test/lSeurat_annotated.rds")
sSeurat <- readRDS("/home/youkim/MetaMarkers/4-dataset-test/sSeurat_20230515.rds")
aSeurat <- readRDS("/home/youkim/Meta-analysis/Arutyanyan_et_al.2023/aSeurat_analyzed.rds")

(aSeurat@assays$RNA$counts != 0) %>% 
  colSums() %>% 
  mean()
(vtSeurat@assays$RNA$counts != 0) %>% 
  colSums() %>% 
  mean()

```

```{r}

# Collapse the cell types in Han dataset
hmeta_copy <- hSeurat@meta.data %>% 
  select(Annotation)

dim(hSeurat@meta.data)

# read the cell types data frame
h_cell_types <- read.csv("/home/youkim/MetaMarkers/4-dataset-test/Han Cell Types.csv") %>% 
  dplyr::rename(Annotation = Annotations)

# use inner_join() on hmeta_copy and h_cell_types
df <- inner_join(hmeta_copy, h_cell_types)

# Check if the order is maintained
summary(df$Annotation == hmeta_copy$Annotation)

# add to meta.data
hSeurat$merged_annotation <- df$Merged

# Collapse the cell types in VT dataset
vtmeta_copy <- vtSeurat@meta.data %>% 
  select(Annotation)

# Do the same thing for the vtSeurat
vt_cell_types <- read.csv("/home/youkim/MetaMarkers/4-dataset-test/VT Cell Types.csv") %>% 
  dplyr::rename(Annotation = Annotations)

# use inner_join() on vtmeta_copy and vt_cell_types
df_vt <- inner_join(vtmeta_copy, vt_cell_types)

# Check if the order is maintained
summary(df_vt$Annotation == vtmeta_copy$Annotation)

# add to meta.data
vtSeurat$merged_annotation <- df_vt$Merged

# Collapse the cell types in Liu dataset
lmeta_copy <- lSeurat@meta.data %>% 
  select(Annotation)

dim(lSeurat@meta.data)

# read the cell types data frame
l_cell_types <- read.csv("/home/youkim/MetaMarkers/4-dataset-test/Liu Cell Types.csv") %>% 
  dplyr::rename(Annotation = Annotations)

# use inner_join() on lmeta_copy and l_cell_types
df_l <- inner_join(lmeta_copy, l_cell_types)

# Check if the order is maintained
summary(df_l$Annotation == lmeta_copy$Annotation)

# add to meta.data
lSeurat$merged_annotation <- df_l$Merged


# Collapse the cell types in Suryawanshi dataset
smeta_copy <- sSeurat@meta.data %>% 
  select(CellType) %>% 
  mutate(Annotation = CellType)

sSeurat@meta.data$CellType %>% 
  table()

dim(lSeurat@meta.data)

# read the cell types data frame
s_cell_types <- read.csv("/home/youkim/MetaMarkers/4-dataset-test/Suryawanshi Cell Types.csv") %>% 
  dplyr::rename(Annotation = Annotations)

# use inner_join() on smeta_copy and s_cell_types
df_s <- inner_join(smeta_copy, s_cell_types)

# Check if the order is maintained
summary(df_s$Annotation == smeta_copy$Annotation)

# add to meta.data
sSeurat$merged_annotation <- df_s$Merged

# Collapse the cell types in Arutyanyan dataset
ameta_copy <- aSeurat@meta.data %>% 
  select(cell_type) %>% 
  mutate(Annotation = cell_type)

ameta_copy %<>% 
  mutate(Annotation = as.data.frame(Idents(aSeurat))[,1])

# Check if order is preserved
summary(ameta_copy$Annotation == Idents(aSeurat))

dim(aSeurat@meta.data)

# read the cell types data frame
a_cell_types <- read.csv("/home/youkim/Meta-analysis/Arutyanyan_et_al.2023/Arutyanyan Cell Types.csv") %>% 
  dplyr::rename(Annotation = Annotations)

# use inner_join() on lmeta_copy and l_cell_types
df_a <- inner_join(ameta_copy, a_cell_types)

# Check if the order is maintained
summary(df_a$Annotation == ameta_copy$Annotation)

# add to meta.data
aSeurat$merged_annotation <- df_a$Merged

aSeurat@meta.data %>% group_by(sample) %>% 
  count()

# Change the Idents of the Seurat Objects
Idents(object = vtSeurat) <- "merged_annotation"
Idents(object = hSeurat) <- "merged_annotation"
Idents(object = lSeurat) <- "merged_annotation"
Idents(object = sSeurat) <- "merged_annotation"
Idents(object = aSeurat) <- "merged_annotation"
```


```{r eval = FALSE}
# Convert to SCE objects
hSCE <- as.SingleCellExperiment(hSeurat)
vtSCE <- as.SingleCellExperiment(vtSeurat)
lSCE <- as.SingleCellExperiment(lSeurat)
sSCE <- as.SingleCellExperiment(sSeurat)
aSCE <- as.SingleCellExperiment(aSeurat)

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

# Check whether the gene information align between the five 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
mydata$lSCE$"cell type" <- mydata$lSCE$ident
mydata$sSCE$"cell type" <- mydata$sSCE$ident
mydata$shSCE$"cell type" <- mydata$shSCE$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)
dim(lSCE)
dim(sSCE)
dim(aSCE)

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_5_datasets(20230614).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
five_datasets <- readRDS("merged_5_datasets(20230614).rds")

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

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

Since there were some problems in the legends for the heatmap, we use this function.
```{r}
my_heatmap <- function (aurocs, cex = 1, margins = c(8, 8), ...) 
{
  auroc_cols <- rev((grDevices::colorRampPalette(RColorBrewer::brewer.pal(11, 
                                                                          "RdYlBu")))(100))
  breaks <- seq(0, 1, length = 101)
  ordering <- stats::as.dendrogram(orderCellTypes(aurocs))
  arg_list <- list(x = aurocs, margins = margins, key = TRUE, 
                   keysize = 1, key.xlab = "AUROC", key.title = "", 
                   offsetRow = 0.1, offsetCol = 0.1, trace = "none", density.info = "none", 
                   Rowv = ordering, Colv = ordering, col = auroc_cols, breaks = breaks, 
                   na.color = grDevices::gray(0.95), cexRow = cex, cexCol = cex)
  additional_args <- list(...)
  arg_list[names(additional_args)] <- additional_args
  do.call(gplots::heatmap.2, arg_list)
}
```


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


plotHeatmap(aurocs,  labRow = FALSE, labCol = FALSE,
            ColSideColors = study_colors %>% pull(color),
            RowSideColors = study_colors %>% pull(color))

# Apparently, we can use this line of code as well.
my_heatmap(aurocs,
           cex = 1,
           margins = c(10,10),
           ColSideColors = study_colors %>% pull(color),
           RowSideColors = study_colors %>% pull(color))

# To identify pairs of replicable cell types, run the following function
top_hits <- topHitsByStudy(aurocs, threshold = 0.9)

# Using the topHitsByStudy function is more desirable.
# top_hits_before <- topHits(aurocs, 
#                     dat = four_datasets, 
#                     study_id = four_datasets$study_id,
#         cell_type = four_datasets$"cell type", threshold = 0.9)

top_hits %>% 
  filter(Match_type != "Reciprocal_top_hit")
# top_hits_before %>% 
#   filter(Match_type != "Reciprocal_top_hit")
```

```{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(five_datasets$study_id,
                               five_datasets$merged_annotation)
subdata <- five_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$study_id,
                         cell_type = subdata$merged_annotation)

my_heatmap(aurocs,
           cex = 0.5,
           margins = c(10,10))

pdf("all-vs-all_split1(20230614).pdf")
my_heatmap(aurocs,
           label_size = 10,
           cex = 0.3,
           margins = c(15,15))
while (!is.null(dev.list()))  dev.off()

# 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 = 2)
level2_split

my_split <- level2_split[[1]]
my_split

subdata <- five_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$study_id,
                         cell_type = subdata$merged_annotation)

my_heatmap(aurocs,
           cex = 1,
           margins = c(15,15))

# Define the rows to exclude based on conditions
rows_to_exclude <- c("hSCE|Neutrophil", "hSCE|Epithelial cell")  # Replace with actual row names or indices

# Find the row indices that match the conditions
rows_to_exclude_indices <- which(rownames(aurocs) %in% rows_to_exclude)

# Exclude the identified rows from the matrix
aurocs_filtered <- aurocs[-rows_to_exclude_indices,-rows_to_exclude_indices, drop = FALSE]

study_colors <- tibble(study = getStudyId(rownames(aurocs_filtered)),
                       cell_type = getCellType(rownames(aurocs_filtered)))
study_colors %<>% mutate(color = recode(study,
                                        hSCE = "black",
                                        vtSCE = "purple",
                                        lSCE = "orange",
                                        sSCE = "red",
                                        aSCE = "green"),
                         color2 = recode(cell_type,
                                         `Extravillous trophoblast` = "blue",
                                         `Syncytiotrophoblast` = "grey",
                                         `Cytotrophoblast` = "brown"))

pdf("all-vs-all_split2(20231203).pdf")
my_heatmap(aurocs_filtered,
           cex = 1,
           margins = c(15,15),
           ColSideColors = study_colors %>% pull(color),
           RowSideColors = study_colors %>% pull(color2))
while (!is.null(dev.list()))  dev.off()

```

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 = 10, fig.width = 10}
best_hits <- MetaNeighborUS(var_genes = var_genes,
                            dat = five_datasets,
                            study_id = five_datasets$study_id,
                            cell_type = five_datasets$merged_annotation,
                            fast_version = TRUE,
                            one_vs_best = TRUE, symmetric_output = FALSE)

my_heatmap(best_hits,
           label_size = 1, 
           margins = c(12,12)
           ) 

pdf("1-vs-best(20230614).pdf")
my_heatmap(best_hits,
           label_size = 1,
           cex = 0.5,
           margins = c(12,12),
           ColSideColors = study_colors %>% pull(color),
           RowSideColors = study_colors %>% pull(color)) 
while (!is.null(dev.list()))  dev.off()

```


```{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, five_datasets$study_id, 
                 five_datasets$merged_annotation, 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|Extravillous trophoblast"
coi <- extendClusterSet(cluster_graph, initial_set = coi,
                        max_neighbor_distance = 2)
subgraph <- subsetClusterGraph(cluster_graph, coi)

pdf(file = "Extracted_network_20231202.pdf")
plotClusterGraph(subgraph, five_datasets$study_id,
                 five_datasets$merged_annotation, size_factor = 4)
dev.off()
```