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SeuratWrappers provides two-way conversion between Seurat objects and Monocle 3 cell_data_set objects.

​
as.cell_data_set()

Converts a Seurat object to a Monocle 3 cell_data_set for trajectory analysis.

​
Syntax

as.cell_data_set(
  x,
  assay = DefaultAssay(object = x),
  reductions = AssociatedDimReducs(object = x, assay = assay),
  default.reduction = DefaultDimReduc(object = x, assay = assay),
  graph = paste0(assay, "_snn"),
  group.by = NULL,
  ...
)

​
Parameters

​
x
Seurat
required
A Seurat object to convert.
​
assay
character
Assay to use. Defaults to the active default assay.
​
reductions
character vector
Names of dimensional reductions to transfer. Defaults to all reductions associated with assay plus global reductions.
​
default.reduction
character
Name of the reduction to use as the primary clustering reduction in Monocle 3. Defaults to the default DimReduc (UMAP > tSNE > PCA).
​
graph
character
Name of the nearest-neighbor graph to transfer for clustering. Defaults to <assay>_snn.
​
group.by
character
default:"NULL"
Metadata column to use for cell identities. If NULL, uses the active Seurat identities.

​
What gets transferred

SeuratMonocle 3Notes
EmbeddingsreducedDimsNames uppercased (e.g., umap → UMAP)
Feature loadingsreduce_dim_aux$gene_loadingsLast reduction only
Stdevreduce_dim_aux$prop_var_explLast reduction only
Seurat clustersclusters slotWith nearest-neighbor graph
monocle3_clusters / monocle3_partitions metadataclusters slotIf present, used directly
Monocle 3 trajectory inference requires cell partitions, which Seurat does not compute. After conversion, run cluster_cells() on the cell_data_set to generate partitions before calling learn_graph().

​
as.Seurat.cell_data_set()

Converts a Monocle 3 cell_data_set back to a Seurat object.

​
Syntax

as.Seurat(
  x,
  counts = "counts",
  data = NULL,
  assay = "RNA",
  project = "cell_data_set",
  loadings = NULL,
  clusters = NULL,
  ...
)

​
Parameters

​
x
cell_data_set
required
A Monocle 3 cell_data_set object.
​
counts
character
default:"counts"
Name of the assay in the cell_data_set to use as counts.
​
assay
character
default:"RNA"
Name for the assay in the resulting Seurat object.
​
loadings
character
default:"NULL"
Name of the reduction to transfer gene loadings into. Defaults to the first reduction containing "pca" (case-insensitive). Pass NA to suppress loadings transfer.
​
clusters
character
default:"NULL"
Name of the Monocle clustering method to use for Seurat identities. Defaults to DefaultDimReduc(object).

​
What gets transferred back

Monocle 3SeuratNotes
reduce_dim_aux$gene_loadingsLoadings of loadings reduction
Monocle clustersActive identity + monocle3_clusters metadata
Monocle partitionsmonocle3_partitions metadata
Monocle pseudotimemonocle3_pseudotime metadata
Nearest-neighbor graph<assay>_monocle3_graphConverted to Seurat Graph

​
Examples

library(SeuratWrappers)
remotes::install_github("cole-trapnell-lab/monocle3")

# Seurat → Monocle 3
cds <- as.cell_data_set(seurat_obj)
cds <- cluster_cells(cds)
cds <- learn_graph(cds)
cds <- order_cells(cds)

# Monocle 3 → Seurat
seurat_obj <- as.Seurat(cds)

​
See Also

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