Monocle_Pseudotime_mouse.R

library(Seurat)
library(SeuratDisk)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
library(BiocManager)
memory.limit(size=7800000000000)
library(RColorBrewer)
n <- 30
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
a<-DiscretePalette(n, palette = NULL)
#BiocManager::install("monocle")
library(monocle)

######set working directory ######
setwd ("D:/HPC_DATA_MOUSE_NEW COVID_April2022/Rrun")
######load the seurat object first ######
#seuratobject<-readRDS(YOURPATH/SEURATRDSfile)

mouse<-readRDS("All.rds") 

data <- as(as.matrix(FRC@assays$RNA@data), 'sparseMatrix')
pd <- new('AnnotatedDataFrame', data = FRC@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))# save list of gene names
fd <- new('AnnotatedDataFrame', data = fData)

FibroMono <- newCellDataSet(data,
                            phenoData  = pd,
                            featureData = fd,expressionFamily = uninormal())

pData(FibroMono)
fData(FibroMono)


#Run ordering algorithm
var_genes <- FRC[["RNA"]]@var.features
ordering_genes <- var_genes

FibroMono <- setOrderingFilter(FibroMono, ordering_genes)
print(dim(exprs(FibroMono)))


## reduce dimension - do not normalize or include pseudo count. Use monocle scaling
FibroMono <- reduceDimension(FibroMono,norm_method="none", 
                             reduction_method="DDRTree",
                             max_components=4,
                             scaling=TRUE,
                             verbose=TRUE,
                             pseudo_expr=0)

# First decide what you want to color your cells by
print(head(pData(FibroMono)))

## order cells change colors and theta to match your plot
FibroMono <- orderCells(FibroMono)




plot_cell_trajectory(FibroMono, 
                     color_by = "Celltypes",
                     theta = -15,
                     show_branch_points = TRUE,show_backbone = TRUE,
                     backbone_color = "black",
                     show_tree = TRUE,use_color_gradient = FALSE,
                     cell_size = 1.5, values=a) + scale_color_manual(values = a, name = "Only FRCs Pseudotime")+ theme(legend.position = "top")

saveRDS(FibroMono, "Pseudotime.rds")

# now onwards to pseudotemporal plots
genex <- c("Inmt")

sig_gene_names <- (genex)
head(sig_gene_names)
pseudotemporalplot <- plot_pseudotime_heatmap(FibroMono[my_genes],
                                              num_clusters = 9, 
                                              cores = 4,
                                              hmcols = NULL,
                                              show_rownames = T)

pseudotemporalplot 
print(pData(cds_subset)$)

###################################### OLd one ##################3

####ESTIMATE DISPERSIONS AND SIZE FACTORS############
FibroMono <- estimateSizeFactors(FibroMono)
FibroMono <- estimateDispersions(FibroMono)


#######FILTER LOW QUALITY GENES/CELLS##########
FibroMono <- detectGenes(FibroMono, min_expr = 0.1)
print(head(fData(FibroMono)))

expressed_genes <- row.names(subset(fData(FibroMono),
                                    num_cells_expressed >= 10))
######DEG TEST##########


diff_test_res <- differentialGeneTest(FibroMono[1:100,],
                                      fullModelFormulaStr = "~updated_subtypes")
ordering_genes <- row.names (subset(diff_test_res, qval < 0.01))

#######################################
## Plotting Figures ###############
png(filename="Trajectory.png")
plot_ordering_genes(FibroMono)
dev.off()
png(filename="TrajectorybyGroup.png")
plot_cell_trajectory(FibroMono, color_by = "group")
dev.off()


###SIGNIFICANT GENES CHANGING WITH PSEUDOTIME##########
GM_state <- function(cds){
  if (length(unique(pData(cds)$State)) > 1){
    T0_counts <- table(pData(cds)$State, pData(cds)$Hours)[,"0"]
    return(as.numeric(names(T0_counts)[which
                                       (T0_counts == max(T0_counts))]))
  } else {
    return (1)
  }
}
diff_test_res <- differentialGeneTest(FibroMono[marker_genes,],
                                      fullModelFormulaStr = "~sm.ns(Pseudotime)")
sig_gene_names <- row.names(subset(diff_test_res, qval < 0.1))
FibroMono <- orderCells(FibroMono, root_state = GM_state(FibroMono))

###PLOT####
png(filename="TrajectorybyPSEUDOTIME.png")
plot_cell_trajectory(FibroMono, color_by = "Conditon")
dev.off()
png(filename="PSEUDOTIMEHEATMAP.png")
plot_pseudotime_heatmap(FibroMono[my_genes,],
                        num_clusters = 3,
                        cores = 1,
                        show_rownames = T)
dev.off()