In satijalab/seurat-wrappers: Community-Provided Methods and Extensions for the Seurat Object

This vigettte demonstrates how to run trajectory inference and pseudotime calculations with Monocle 3 on Seurat objects. If you use Monocle 3, please cite:

The single-cell transcriptional landscape of mammalian organogenesis

Junyue Cao, Malte Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M. Ibrahim, Andrew J. Hill, Fan Zhang, Stefan Mundlos, Lena Christiansen, Frank J. Steemers, Cole Trapnell & Jay Shendure

doi: 10.1038/s41586-019-0969-x

Website: https://cole-trapnell-lab.github.io/monocle3/

knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE,
  fig.height = 10,
  fig.width = 16
)

Prerequisites to install:

• Seurat
• SeuratWrappers
• SeuratData
• Monocle 3

if (!requireNamespace("monocle3", quietly = TRUE)) {
  setRepositories(ind = 1:2)
  remotes::install_github(
    repo = "cole-trapnell-lab/monocle3",
    upgrade = FALSE
  )
}

library(monocle3)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
library(ggplot2)
library(patchwork)
library(magrittr)

HCA Bone Marrow 40k

InstallData("hcabm40k")
data("hcabm40k")

hcabm40k <- SplitObject(hcabm40k, split.by = "orig.ident")
for (i in seq_along(hcabm40k)) {
  hcabm40k[[i]] <- NormalizeData(hcabm40k[[i]]) %>% FindVariableFeatures()
}
features <- SelectIntegrationFeatures(hcabm40k)
for (i in seq_along(along.with = hcabm40k)) {
  hcabm40k[[i]] <- ScaleData(hcabm40k[[i]], features = features) %>%
    RunPCA(features = features)
}

anchors <- FindIntegrationAnchors(
  hcabm40k,
  reference = c(1, 2),
  reduction = "rpca",
  dims = 1:30
)
integrated <- IntegrateData(anchors, dims = 1:30)

integrated <- ScaleData(integrated)
integrated <- RunPCA(integrated)
integrated <- RunUMAP(integrated, dims = 1:30, reduction.name = "UMAP")
integrated <- FindNeighbors(integrated, dims = 1:30)
integrated <- FindClusters(integrated)
DimPlot(integrated, group.by = c("orig.ident", "ident"))

con <- url("https://seurat.nygenome.org/monocle3/hcabm40k_integrated.Rds")
integrated <- readRDS(file = con)
close(con = con)
DimPlot(object = integrated, group.by = c("orig.ident", "ident"))

cds <- as.cell_data_set(integrated)
cds <- cluster_cells(cds)
p1 <- plot_cells(cds, show_trajectory_graph = FALSE)
p2 <- plot_cells(cds, color_cells_by = "partition", show_trajectory_graph = FALSE)
wrap_plots(p1, p2)

rm(integrated)
gc(verbose = FALSE)

integrated.sub <- subset(as.Seurat(cds), monocle3_partitions == 1)
cds <- as.cell_data_set(integrated.sub)
cds <- learn_graph(cds)
plot_cells(
  cds,
  label_groups_by_cluster = FALSE,
  label_leaves = FALSE,
  label_branch_points = FALSE
)

gc(verbose = FALSE)

max.avp <- which.max(unlist(FetchData(integrated.sub, "AVP")))
max.avp <- colnames(integrated.sub)[max.avp]
cds <- order_cells(cds, root_cells = max.avp)
plot_cells(
  cds,
  color_cells_by = "pseudotime",
  label_cell_groups = FALSE,
  label_leaves = FALSE,
  label_branch_points = FALSE
)
# Set the assay back as "integrated"
integrated.sub <- as.Seurat(cds, assay = "integrated")
FeaturePlot(integrated.sub, "monocle3_pseudotime")

satijalab/seurat-wrappers documentation built on April 10, 2024, 3:25 p.m.