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TI_monocle3.R
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TI_monocle3.R
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# script to perform trajectory analysis
# https://www.nature.com/articles/s41467-019-10291-0
# setwd("~/Desktop/demo/monocle3")
set.seed(1234)
library(monocle3)
library(SeuratWrappers)
library(Seurat)
library(ggplot2)
library(tidyverse)
# read in data
markers <- read.delim('ABC_Marker.txt', header = T) # gene metadata
metadata <- read.delim('ABC_Meta.txt', header = T) # cell metadata
expr <- read.delim('ABC_umi_matrix_7551_cells.csv', header = T, sep = ',') # expression matrix
# create seurat object ---------------
expr.t <- t(expr)
seu.obj <- CreateSeuratObject(counts = expr.t)
View(seu.obj@meta.data)
seu.obj@meta.data <- merge(seu.obj@meta.data, metadata, by.x = 'row.names', by.y = 'cell_id')
View(seu.obj@meta.data)
seu.obj@meta.data <- seu.obj@meta.data %>%
column_to_rownames(var = 'Row.names')
seu.obj$mitopercent <- PercentageFeatureSet(seu.obj, pattern = '^MT-')
seu.obj.filtered <- subset(seu.obj, subset = nCount_RNA > 800 &
nFeature_RNA > 500 &
mitopercent < 10)
# subset my seurat object - B cells
unique(seu.obj.filtered@meta.data$population)
Idents(seu.obj.filtered) <- seu.obj.filtered$population
b.seu <- subset(seu.obj.filtered, idents = "b")
b.seu
unique(b.seu@meta.data$redefined_cluster)
# pre-processing using seurat
b.seu <- NormalizeData(b.seu)
b.seu <- FindVariableFeatures(b.seu)
b.seu <- ScaleData(b.seu)
b.seu <- RunPCA(b.seu)
b.seu <- FindNeighbors(b.seu, dims = 1:30)
b.seu <- FindClusters(b.seu, resolution = 0.9)
b.seu <- RunUMAP(b.seu, dims = 1:30, n.neighbors = 50)
a1 <- DimPlot(b.seu, reduction = 'umap', group.by = 'redefined_cluster', label = T)
a2 <- DimPlot(b.seu, reduction = 'umap', group.by = 'seurat_clusters', label = T)
a1|a2
# MONOCLE3 WORKFLOW ---------------------
# monocle3 requires cell_data_set object
# convert seurat object to cell_data_set object for monocle3
# ...1 Convert to cell_data_set object ------------------------
cds <- as.cell_data_set(b.seu)
cds
# to get cell metadata
colData(cds)
# to gene metdata
fData(cds)
rownames(fData(cds))[1:10]
# since it misses the gene_short_name column, let's add it
fData(cds)$gene_short_name <- rownames(fData(cds))
# to get counts
counts(cds)
# ...2. Cluster cells (using clustering info from seurat's UMAP)---------------------------
# let's use the clustering information have
# assign paritions
reacreate.partition <- c(rep(1,length(cds@colData@rownames)))
names(reacreate.partition) <- cds@colData@rownames
reacreate.partition <- as.factor(reacreate.partition)
cds@clusters$UMAP$partitions <- reacreate.partition
# Assign the cluster info
list_cluster <- b.seu@active.ident
cds@clusters$UMAP$clusters <- list_cluster
# Assign UMAP coordinate - cell embeddings
cds@int_colData@listData$reducedDims$UMAP <- b.seu@reductions$umap@cell.embeddings
# plot
cluster.before.trajectory <- plot_cells(cds,
color_cells_by = 'cluster',
label_groups_by_cluster = FALSE,
group_label_size = 5) +
theme(legend.position = "right")
cluster.names <- plot_cells(cds,
color_cells_by = "redefined_cluster",
label_groups_by_cluster = FALSE,
group_label_size = 5) +
scale_color_manual(values = c('red', 'blue', 'green', 'maroon', 'yellow', 'grey', 'cyan')) +
theme(legend.position = "right")
cluster.before.trajectory | cluster.names
# ...3. Learn trajectory graph ------------------------
cds <- learn_graph(cds, use_partition = FALSE)
plot_cells(cds,
color_cells_by = 'redefined_cluster',
label_groups_by_cluster = FALSE,
label_branch_points = FALSE,
label_roots = FALSE,
label_leaves = FALSE,
group_label_size = 5)
# ...4. Order the cells in pseudotime -------------------
cds <- order_cells(cds, reduction_method = 'UMAP', root_cells = colnames(cds[,clusters(cds) == 5]))
plot_cells(cds,
color_cells_by = 'pseudotime',
label_groups_by_cluster = FALSE,
label_branch_points = FALSE,
label_roots = FALSE,
label_leaves = FALSE)
# cells ordered by monocle3 pseudotime
pseudotime(cds)
cds$monocle3_pseudotime <- pseudotime(cds)
data.pseudo <- as.data.frame(colData(cds))
ggplot(data.pseudo, aes(monocle3_pseudotime, reorder(redefined_cluster, monocle3_pseudotime, median), fill = redefined_cluster)) +
geom_boxplot()
# ...5. Finding genes that change as a function of pseudotime --------------------
deg_bcells <- graph_test(cds, neighbor_graph = 'principal_graph', cores = 4)
deg_bcells %>%
arrange(q_value) %>%
filter(status == 'OK') %>%
head()
FeaturePlot(b.seu, features = c('E2F2', 'STMN1', 'CD52'))
# visualizing pseudotime in seurat
b.seu$pseudotime <- pseudotime(cds)
Idents(b.seu) <- b.seu$redefined_cluster
FeaturePlot(b.seu, features = "pseudotime", label = T)