Create an Rds file for the full heart maturation dataset

extract_shiny_data_Full_dataset.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"))
                                                  


## Load Seurat object
seurat_object <- readRDS("../Objects/Heart_maturation.joined_data.10pcs.Rds")

## Read marker list
marker_list <- read.table("../Marker/Seurat_all_cluster_markers.10pcs.tsv",
                                header=TRUE,
                                sep="\t")
                                
## Only retain top 20 Marker based on avg_logFC
marker_list_subset <- marker_list %>%
    group_by(cluster) %>%
    top_n(n=50, wt=avg_logFC)
    
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)
                                     
saveRDS(clustering_object,file=paste("../Shiny_server_data/Heart_maturation.FullDataset.clustering_module.Rds",sep=""))