CellChat_Basics.Rmd

---
title: "CellChatAnalysis"
author: "Daniel Lee"
date: "2023-08-01"
output: pdf_document
---

## Overall analysis
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, eval = TRUE,  
                      warning = FALSE, message = FALSE, 
                      fig.align = "center",
                      R.options = list(max.print=100))
```

```{r}
getwd()

# running renv
# install.packages("renv")
library(renv)
# renv::init()
# renv::snapshot()
# renv::status()
# renv::clean()
# renv::restore()
# renv::history()
```

```{r}
# install.packages("devtools")

# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install(version = "3.17")

# BiocManager::install("NMF")
# BiocManager::install("BiocGenerics")
# install.packages("car")
# BiocManager::install("Biobase")
# BiocManager::install("ComplexHeatmap")
# BiocManager::install("Seurat")

# svglite, expm, RSpectra, ggpubr, car, gert, mgcv, BiocParallel, BiocNeighbors, clue manually downloaded from CRAN & BiocManager and moved to renv project directory
# devtools::install_github("sqjin/CellChat")

library(CellChat)
library(Seurat)

```

```{r}
# Load your data
setwd("/Volumes/biologos/Bioinformatics/Using-CellChat-for-Ligand-Receptor-Network-Analysis/Data")
myeloid <- readRDS("myeloid_srt_int.rds")
lymphoid <- readRDS("lymphoid_srt_int.rds")

# Assign cell types to myeloid clusters
myeloid$cell_type <- factor(
  recode(myeloid$seurat_clusters,
         `0` = "IL1B+ APOE+ Macrophages",
         `1` = "IL1B+ APOE- Macrophages",
         `2` = "Monocytes",
         `3` = "KIT+ Mast cells",
         `4` = "Dendritic cells",
         `5` = "IL1B- APOE+ Macrophages",
         `6` = "LYVE1+ Macrophages",
         `7` = "IFN-associated Myeloid cells"
  )
)

# Assign cell types to lymphoid clusters
lymphoid$cell_type <- factor(
  recode(lymphoid$seurat_clusters,
         `0` = "IL7R+ CD4+ Effector T cells",
         `1` = "GZMK+ CD8+ T cells",
         `2` = "GZMH+ CD8+ T cells",
         `3` = "Natural Killer cells",
         `4` = "CXCL8+ T cells",
         `5` = "CD79A+ B cells type 1",
         `6` = "CD79A+ B cells type 2",
         `7` = "TIGIT+ CD4+ Treg cells",
         `8` = "Plasma cells"
  )
)

# Designate the specific clusters
myeloid_clusters <- c("IL1B+ APOE+ Macrophages",
                      "IL1B+ APOE- Macrophages",
                      "Dendritic cells",
                      "IL1B- APOE+ Macrophages",
                      "LYVE1+ Macrophages")

lymphoid_clusters <- c("IL7R+ CD4+ Effector T cells",
                       "GZMK+ CD8+ T cells",
                       "GZMH+ CD8+ T cells",
                       "CXCL8+ T cells",
                       "TIGIT+ CD4+ Treg cells")

# Subset the Seurat objects based on the cell type
myeloid_subset <- subset(myeloid, subset = cell_type %in% myeloid_clusters)

lymphoid_subset <- subset(lymphoid, subset = cell_type %in% lymphoid_clusters)

# Check the subset step
print(unique(myeloid_subset$cell_type))
print(unique(lymphoid_subset$cell_type))

# Combining the two datasets into a single Seurat object
combined_data <- merge(x = lymphoid_subset, y = myeloid_subset)
```

```{r}
# Check the merge
unique(combined_data$cell_type)

# Create a new CellChat object with the subsetted data
cellchat <- createCellChat(object = combined_data, meta = combined_data@meta.data, group.by = "cell_type")
```

```{r}
# Set the ligand-receptor interaction database
CellChatDB.use <- CellChatDB.human

# Using a subset of CellChatDB for cell-cell communication analysis
CellChatDB.use <- subsetDB(CellChatDB.human, search = "Secreted Signaling")

# Add the Secreted Signaling database in the CellChat object
cellchat@DB <- CellChatDB.use

```

```{r}
# Pre-process the expression data
cellchat <- subsetData(cellchat)
cellchat <- identifyOverExpressedGenes(cellchat)
cellchat <- identifyOverExpressedInteractions(cellchat)

```

```{r}
# Project gene expression data onto protein-protein interaction (PPI)
cellchat <- projectData(cellchat, PPI.human)
```

```{r}
# Compute communication probabilities again
cellchat <- computeCommunProb(cellchat, raw.use = FALSE)

```

```{r}
# Filter out the cell-cell communication if there are only few number of cells 
# in certain cell groups
cellchat <- filterCommunication(cellchat, min.cells = 10)

```

```{r}
# Infer the cell-cell communication at a signaling pathway level
cellchat <- computeCommunProbPathway(cellchat)

```

```{r}
# Calculate the aggregated cell-cell communication network
cellchat <- aggregateNet(cellchat)
cellchat@net$count
cellchat@net$weight

```

```{r}
# Create your plots
groupSize <- as.numeric(table(cellchat@idents))
par(mfrow = c(1, 2), xpd=TRUE)
netVisual_circle(cellchat@net$count, vertex.weight = groupSize, 
   weight.scale = T, label.edge= T, title.name = "Number of interactions")
netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, 
   weight.scale = T, label.edge= T, title.name = "Interaction weights/strength")
```

```{r}
str(cellchat)

# Save the CellChat object for future reference
setwd("/Volumes/biologos/Bioinformatics/Using-CellChat-for-Ligand-Receptor-Network-Analysis/CellChatObjects")
saveRDS(cellchat, file = "Myeloid_Lymphoid_scRNA_cellchat.rds")
```

```{r, echo=F}
## DO NOT DELETE THIS BLOCK!
Sys.info()
sessionInfo()
```