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AUCell.Rmd

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+---
+title: "AUCell"
+author: "Xue Xiao"
+date: "2024-06-20"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+## Install AUCell
+```{r eval=FALSE, echo=FALSE, message=FALSE, warning=FALSE}
+BiocManager::install("AUCell")
+```
+
+## Load Packages
+```{r}
+library(AUCell)
+library(Matrix)
+library(SummarizedExperiment)
+library(Seurat)
+```
+
+
+## Load Genesets and Datasets
+```{r}
+setwd("/Users/xuexiao/Library/CloudStorage/[email protected]/My Drive/Lab/Projects/Dedifferentiation/Data")
+CC1_markers <- read.csv("CC1_markers.csv", header = T, row.names = 1)
+CC1_geneset <- CC1_markers$Genes
+
+CSC_markers_0.007 <- read.csv("filtered_markers_above_0_007.csv", header = T, row.names = 1)
+CSC_geneset_0.007 <- CSC_markers_0.007$GENE
+
+CSC_markers_0.006 <- read.csv("filtered_markers_above_0_006.csv", header = T, row.names = 1)
+CSC_geneset_0.006 <- CSC_markers_0.006$GENE
+
+CSC_markers_0.005 <- read.csv("filtered_markers_above_0_005.csv", header = T, row.names = 1)
+CSC_geneset_0.005 <- CSC_markers_0.005$GENE
+
+gene_sets <- list(
+  CC1 = CC1_geneset,
+  CSC_0.007 = CSC_geneset_0.007,
+  CSC_0.006 = CSC_geneset_0.006,
+  CSC_0.005 = CSC_geneset_0.005
+)
+common_genes <- intersect(CC1_geneset, CSC_geneset_0.005)
+```
+
+
+```{r}
+# Open Seurat File
+seurat_obj <- readRDS("~/Library/CloudStorage/[email protected]/My Drive/Lab/Projects/Dedifferentiation/Data/Cancer_cell_in_house_magic_cytotrace.rds")
+
+# Extract the expression matrix
+expr_matrix <- GetAssayData(seurat_obj, layer = "counts")
+
+```
+
+## Run AUCell
+```{r}
+# Build the rankings
+cells_rankings <- AUCell_buildRankings(expr_matrix, nCores = 1, plotStats = TRUE)
+
+# Calculate the AUCell scores
+cells_AUC <- AUCell_calcAUC(gene_sets, cells_rankings)
+
+# Add AUCell scores to Seurat metadata
+aucell_scores <- as.data.frame(t(as.data.frame(cells_AUC@assays@data$AUC)))
+colnames(aucell_scores) <- paste0("AUCell_", colnames(aucell_scores))
+seurat_obj <- AddMetaData(seurat_obj, metadata = aucell_scores)
+
+# View the Seurat object metadata to check if scores were added
+head([email protected])
+
+```
+
+## Visualization
+```{r inline_plot, fig.width=7, fig.height=5}
+
+# Define custom color palette
+custom_colors <- scale_color_gradientn(colors = c("gray", "yellow", "red"))
+
+# Visualize the AUCell scores with custom color scheme
+FeaturePlot(seurat_obj, features = colnames(aucell_scores), pt.size = 0.2, label = T) & custom_colors
+
+```
+
+

BCSCdb.Rmd

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+---
+title: "BCSCdb"
+author: "Xue Xiao"
+date: "2024-06-18"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+## Load packages
+```{r}
+library(ggplot2)
+```
+
+
+## Load all CSC markers
+```{r}
+setwd("/Users/xuexiao/Library/CloudStorage/[email protected]/My Drive/Lab/Projects/Dedifferentiation/Data")
+all_CSC_markers <- read.csv("CSC_Biomarker_2022_All.csv", header = F)
+head(all_CSC_markers)
+length(unique(all_CSC_markers$V1))
+```
+
+## Filter top 100 markers
+```{r}
+## Unique markers
+unique_CSC_markers <- all_CSC_markers[!duplicated(all_CSC_markers$V1), ]
+
+## Sort the CSC markers in descending order of global score
+sorted_CSC_markers <- unique_CSC_markers[order(-as.numeric(unique_CSC_markers$V11)), ]
+
+## Check distribution of global score
+ggplot(unique_CSC_markers, aes(x = V11)) +
+  geom_histogram(binwidth = 0.05, fill = "blue", color = "black", alpha = 0.7) +
+  scale_x_continuous(limits = c(-0.1, 1), breaks = seq(-1, 1, by = 0.05)) +
+  labs(title = "Distribution of Global Scores",
+       x = "Global Score",
+       y = "Frequency") +
+  theme_minimal() +
+  stat_bin(binwidth = 0.05, geom = "text", aes(label = ..count..), vjust = -0.5, color = "black")
+## Select the top 100 markers according to global score
+table(unique_CSC_markers$V11)
+```
+
+
+```{r}
+sum(unique_CSC_markers$V11 > 0.007, na.rm = TRUE)  # 105 genes
+sum(unique_CSC_markers$V11 > 0.006, na.rm = TRUE)  # 158 genes
+sum(unique_CSC_markers$V11 > 0.005, na.rm = TRUE)  # 269 genes
+```
+## Filter genes with global score
+```{r}
+filtered_markers <- unique_CSC_markers[unique_CSC_markers$V11 > 0.005, ]
+unique_gene_names <- as.list(filtered_markers$V1)
+colnames(filtered_markers) <- c("GENE", "MARKER_TYPE", "EXPRESSION_LEVEL", "HGNC_ID", "CANCER_TYPE", "HISTOLOGICAL_TYPE", "CELL_LINE", "CSC_ENRICHMENT", "METHOD", "CONFIDENCE_SCORING", "GLOBAL_SCORING", "PUBMED_ID")
+write.csv(filtered_markers, "/Users/xuexiao/Library/CloudStorage/[email protected]/My Drive/Lab/Projects/Dedifferentiation/Data/filtered_markers_above_0_005.csv")
+```
+

CellChat2.Rmd

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+---
+title: "OC_CAF_Crosstalk"
+output: html_document
+date: "2024-05-21"
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+## Package Installation
+```{r, eval=FALSE}
+# install.packages("devtools")
+devtools::install_github("immunogenomics/presto")
+devtools::install_github("jinworks/cellchat")
+```
+
+## Load Packages
+```{r}
+library(Seurat)
+library(cellchat)
+library(ggplot2)
+library(ggplotify)
+```
+
+
+## Load Data
+```{r}
+load("~/Library/CloudStorage/[email protected]/My Drive/Lab/Projects/OC_CAF_Crosstalk/GSE165897/Secreted_Signaling_GSE165897_Object.RData")
+load("~/Desktop/Lab/Projects/OC_CAF_Crosstalk/Secreted_Signalingin_house_Object.RData")
+load("~/Desktop/Lab/Projects/OC_CAF_Crosstalk/Secreted_Signaling_Object.RData")
+cellchat@meta$labels[cellchat@meta$labels == "Epithelial_cells"] <- "OC_cells"
+cellchat@meta$labels[cellchat@meta$labels == "Smooth_muscle_cells"] <- "CAFs"
+table(cellchat@idents)
+cellchat <- setIdent(cellchat, ident.use = "labels")
+```
+
+## Create cellchat Object
+```{r}
+data.input <- in_house_seurat[["RNA"]]$data # normalized data matrix
+# For Seurat version >= “5.0.0”, get the normalized data via `seurat_object[["RNA"]]$data`
+Idents(in_house_seurat) <- "subcluster"
+labels <- Idents(in_house_seurat)
+colnames([email protected])
+meta <- data.frame(labels = labels, row.names = names(labels)) # create a dataframe of the cell labels
+
+cellchat <- createcellchat(object = in_house_seurat, group.by = "subcluster", assay = "RNA")
+
+```
+## Set the ligand-receptor interaction database
+```{r}
+cellchatDB <- cellchatDB.human
+cellchatDB.use <- subsetDB(cellchatDB, search = "Secreted Signaling", key = "annotation")
+cellchat@DB <- cellchatDB.use
+cellchat <- subsetData(cellchat)
+```
+
+## Run cellchat
+```{r}
+ptm = Sys.time()
+future::plan("multisession", workers = 8)
+cellchat <- identifyOverExpressedGenes(cellchat)
+cellchat <- identifyOverExpressedInteractions(cellchat)
+execution.time = Sys.time() - ptm
+print(as.numeric(execution.time, units = "secs"))
+cellchat <- computeCommunProb(cellchat, type = "triMean")
+cellchat <- filterCommunication(cellchat, min.cells = 10)
+cellchat <- computeCommunProbPathway(cellchat)
+```
+
+```{r}
+cellchat <- aggregateNet(cellchat)
+execution.time = Sys.time() - ptm
+print(as.numeric(execution.time, units = "secs"))
+```
+
+
+## Visualization
+```{r}
+# Aggregated Cell-Cell Communication Network (Total Interactions) 
+ptm = Sys.time()
+groupSize <- as.numeric(table(cellchat@idents))
+par(mfrow = c(1,2), xpd=TRUE)
+netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions")
+netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")
+
+plot1 <- as.ggplot(~netVisual_circle(cellchat@net$count, vertex.weight = groupSize, 
+                                     weight.scale = TRUE, label.edge = FALSE, 
+                                     title.name = "Number of interactions"))
+# Save the first plot
+ggsave("Number_of_interactions.pdf", plot = plot1, width = 6, height = 6)
+
+# Convert the second plot to a ggplot object
+plot2 <- as.ggplot(~netVisual_circle(cellchat@net$weight, vertex.weight = groupSize, 
+                                     weight.scale = TRUE, label.edge = FALSE, 
+                                     title.name = "Interaction weights/strength"))
+# Save the second plot
+ggsave("Interaction_weights_strength.pdf", plot = plot2, width = 6, height = 6)
+getwd()
+setwd("/Users/xuexiao/Library/CloudStorage/[email protected]/My Drive/Lab/Figure/Data")
+```
+
+
+```{r}
+# Network Centrality Scores
+cellchat <- netAnalysis_computeCentrality(cellchat, slot.name = "netP") 
+netAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 13, height = 5, font.size = 10)
+```
+```{r}
+# Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
+ht1 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "outgoing")
+ht2 <- netAnalysis_signalingRole_heatmap(cellchat, pattern = "incoming")
+ht1 + ht2
+netAnalysis_signalingRole_heatmap(cellchat, signaling = c("PDGF", "ncWNT"), width = 8, height = 5, font.size = 10)
+ht
+```
+```{r}
+# show all the significant signaling pathways from some cell groups (defined by 'sources.use') to other cell groups (defined by 'targets.use')
+netVisual_chord_gene(cellchat, sources.use = c(1:3), targets.use = c(4:13), slot.name = "netP", legend.pos.x = 10, small.gap = 0.2, lab.cex = 0.5)
+```
+
+
+```{r}
+pathways.show <- c("PDGF") 
+```
+
+### Circle plot
+```{r}
+par(mfrow=c(1,1))
+netVisual_aggregate(cellchat, signaling = pathways.show, layout = "circle")
+```
+### Chord diagram
+```{r}
+par(mfrow=c(1,1))
+netVisual_aggregate(cellchat, signaling = pathways.show, layout = "chord")
+```
+### Heatmap
+```{r}
+par(mfrow=c(1,1))
+netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds")
+```
+### Contribution
+```{r}
+netAnalysis_contribution(cellchat, signaling = pathways.show, font.size = 10, width = 20)
+
+```
+### Single L-R pair
+```{r}
+pairLR.PDGF <- extractEnrichedLR(cellchat, signaling = pathways.show, geneLR.return = FALSE)
+LR.show <- pairLR.PDGF[4,] # show one ligand-receptor pair
+```
+
+```{r}
+# Chord plot
+netVisual_individual(cellchat, signaling = pathways.show,  pairLR.use = LR.show, layout = "chord")
+# Circle plot
+#netVisual_individual(cellchat, signaling = pathways.show, pairLR.use = LR.show, layout = "circle")
+```
+```{r}
+netVisual_bubble(cellchat, sources.use = c(4:13), targets.use = c(1:3), signaling = c("PDGF"), remove.isolate = FALSE, sort.by.target = TRUE)
+netVisual_bubble(cellchat, sources.use = c(1:3), targets.use = c(4:13), signaling = c("PDGF"), remove.isolate = FALSE)
+netVisual_bubble(cellchat, sources.use = c(1:3), targets.use = c(4:13), signaling = c("ncWNT"), remove.isolate = FALSE)
+```
+```{r}
+netVisual_chord_gene(cellchat, sources.use = c(4:13), targets.use = c(1:3), signaling = c("PDGF"),legend.pos.x = 8)
+netVisual_chord_gene(cellchat, sources.use = c(1:3), targets.use = c(4:13), signaling = c("ncWNT"),legend.pos.x = 8, small.gap = 0.1)
+
+```
+
+## Save CellChat object
+```{r}
+saveRDS(cellChat, file = "~/Library/CloudStorage/[email protected]/My Drive/Lab/Projects/OC_CAF_Crosstalk/Imputed_in_house/cellchat_in_house.rds")
+```
+