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This page demonstrates how to run trajectory inference and pseudotime calculations with Monocle 3 on Seurat objects using SeuratWrappers.
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 Nature, 2019. doi: 10.1038/s41586-019-0969-x

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Installation

The following packages are required before proceeding:
remotes::install_github('cole-trapnell-lab/monocle3')

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

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Workflow

1

Prepare and integrate data

Load your dataset and run standard Seurat preprocessing. This example uses the HCA Bone Marrow 40k dataset.
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)
2

Cluster and embed

Run standard dimensionality reduction and clustering in Seurat.
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"))
3

Convert to cell_data_set

Use as.cell_data_set() to convert the Seurat object to a Monocle 3 cell_data_set. Then run Monocle 3’s own clustering to compute partitions, which are required for trajectory inference.
Monocle 3 trajectories require cluster partitions, which Seurat does not calculate. After converting to a cell_data_set, you must run cluster_cells() before calling learn_graph(). If partitions are missing, as.cell_data_set() will assign all cells to a single partition and issue a warning.
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)
4

Subset to a single partition and learn graph

Trajectories can only be inferred within a single connected partition. Subset cells to the partition of interest, convert back to Seurat, then convert again to a fresh cell_data_set before learning the graph.
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
)
5

Order cells by pseudotime

Set a root cell (or cells) to define the start of the trajectory, then order all cells along the pseudotime axis.
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
)
6

Bring results back to Seurat

Use as.Seurat() to transfer Monocle 3 results back to a Seurat object for visualization with Seurat’s plotting utilities.
integrated.sub <- as.Seurat(cds)
FeaturePlot(integrated.sub, "monocle3_pseudotime")

​
Functions

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as.cell_data_set()

Converts a Seurat object to a Monocle 3 cell_data_set. Uses as.SingleCellExperiment() internally to transfer expression data and cell-level metadata, then adds the following:
  • Cell embeddings are transferred to reducedDims. Reduction names are uppercased (e.g. "umap" → "UMAP") to match Monocle 3 conventions.
  • Feature loadings from the last dimensional reduction are written to cds@reduce_dim_aux$gene_loadings.
  • Standard deviations from the last dimensional reduction are written to cds@reduce_dim_aux$prop_var_expl.
  • If monocle3_clusters and monocle3_partitions columns exist in cell metadata, they are used directly. Otherwise, Seurat’s nearest-neighbor graph is converted to an igraph object and Seurat’s cluster identities are used (with all cells assigned to a single partition).
​
x
Seurat
required
A Seurat object to convert.
​
assay
string
default:"DefaultAssay(x)"
Name of the assay to transfer. Defaults to the active assay.
​
reductions
character vector
Names of dimensional reductions to include. Defaults to all reductions associated with assay plus global reductions.
​
default.reduction
string
Name of the dimensional reduction to use for storing clustering results. Defaults to the object’s default reduction for assay.
​
graph
string
Name of the nearest-neighbor graph to use for clustering. Defaults to "{assay}_snn".
​
group.by
string
Name of a cell-level metadata column to use as cluster identities. Pass NULL (the default) to use the active identities.

​
as.Seurat() for cell_data_set

Converts a Monocle 3 cell_data_set back to a Seurat object. Builds on as.Seurat.SingleCellExperiment() and additionally transfers:
  • Feature loadings from cds@reduce_dim_aux$gene_loadings into the reduction specified by loadings (or the first reduction whose name contains "pca").
  • Monocle 3 cluster assignments → metadata column monocle3_clusters and the active identity class.
  • Monocle 3 partitions → metadata column monocle3_partitions.
  • Monocle 3 pseudotime values → metadata column monocle3_pseudotime.
  • The nearest-neighbor graph (if present) → a Seurat Graph object stored as "{assay}_monocle3_graph".
​
x
cell_data_set
required
A Monocle 3 cell_data_set object to convert.
​
counts
string
default:"counts"
Name of the assay slot in the cell_data_set to use as counts.
​
data
string
Name of the assay slot to use as normalized data. Pass NULL to skip.
​
assay
string
default:"RNA"
Name to give the assay in the resulting Seurat object.
​
project
string
default:"cell_data_set"
Project name for the resulting Seurat object.
​
loadings
string
Name of the dimensional reduction in the resulting Seurat object to receive feature loadings. Defaults to the first reduction whose name contains "pca" (case-insensitive). Pass NA to suppress transfer of loadings.
​
clusters
string
Name of the clustering method stored in the cell_data_set to use when pulling cluster assignments and pseudotime. Defaults to the default dimensional reduction of the resulting Seurat object.

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What gets transferred

DataSeurat → Monocle 3Monocle 3 → Seurat
Expression matrixcounts assay via as.SingleCellExperimentCounts/data slots via as.Seurat.SingleCellExperiment
Cell metadataAll columnsAll columns
Cell embeddingsreducedDims (uppercased names)DimReduc objects (lowercased names)
Feature loadingscds@reduce_dim_aux$gene_loadingsAdded to reduction specified by loadings
Cluster assignmentscds@clusters[[reduction]]$clustersmonocle3_clusters column + active identity
Partitionscds@clusters[[reduction]]$partitionsmonocle3_partitions column
Pseudotime—monocle3_pseudotime column
Nearest-neighbor graphigraph in cds@clusters[[reduction]]$cluster_result$g"{assay}_monocle3_graph" Graph object

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