Create shiny cluster modules for renamed datasets

cluster_module_shiny_ALL_renamed.R

library(dplyr)
library(tidyr)

## Define S4 object with required components
Clustering_info <- setClass("clustering_module",slots=c(tsne="data.frame",
                                                  metadata="data.frame",
                                                  norm_exprs="data.frame",
                                                  marker_list="data.frame"))
                                                  

time_points <- c("E14.5","E16.5","E18.5","P1","P4","P7")

for(time_point in time_points){

## Load Seurat object
seurat_object <- readRDS(paste("../Objects/",time_point,".expression_seurat.renamed_clusters.Rds",sep=""))
  
## Read marker list
marker_list <- read.table(paste("../Marker/",time_point,".all_markers_identified.renamed_clusters.tsv",sep=""),
                                header=T,
                                sep="\t")
                                
## Only retain top 20 Marker based on avg_logFC
marker_list$pct_diff <- marker_list$pct.1 - marker_list$pct.2
marker_list_subset <- marker_list %>%
    group_by(cluster) %>%
   top_n(n=50, wt=pct_diff)
    
marker_list_subset <- as.data.frame(marker_list_subset)

## Normalized expression data
norm_exprs_sparse <- seurat_object@data
norm_exprs_matrix <- as.matrix(seurat_object@data)
norm_exprs_df <- as.data.frame(norm_exprs_matrix)

## Create a new data frame that contains tSNE embeddings and cluster identities
tsne_mappings <- seurat_object@[email protected]
cell_identities <- data.frame(seurat_object@ident)
rownames(cell_identities) <- names(seurat_object@ident)
tsne_mappings <- merge(tsne_mappings,cell_identities,by=0,all=TRUE)
rownames(tsne_mappings) <- tsne_mappings$Row.names
tsne_mappings <- tsne_mappings %>%
  select(-Row.names)
  
## Metadata information
metadata <- [email protected]

clustering_object <- Clustering_info(tsne=tsne_mappings,
                                     norm_exprs=norm_exprs_df,
                                     metadata = metadata,
                                     marker_list=marker_list_subset)

## With compression, faster reading, bigger file size!
saveRDS(clustering_object,file=paste("../Shiny_server_data/",time_point,".clustering_module.renamed.Rds",sep=""))  
  
}