Using Seurat merged object for pseudo time analysis in Monocle 3 #2833

CaroSegami · 2020-04-08T10:29:34Z

I have an SCTtransformed merged Seurat object and I would like to follow up with a pseudo time analysis. I've seen that last year Seurat didn't support conversion of Seurat objects to Monocle 3 cds because it was still beta. Is this still the case? Which pseudo time analysis that could be compatible with Seurat object would you recommend? I expect my data to be linear rather than branched. Thank you.

KinSimon96 · 2020-04-09T20:15:57Z

Hello,
Here is how I convert the object of class Seurat into a cds object (Monocle3) for pseudotime analysis. Eveything will be unchanged. I don't know if it will work with SCTransformed, but you should be able to do your own modifications with the code below. My Seurat object here is try

gene_annotation <- as.data.frame(rownames(try@reductions[["pca"]]@feature.loadings),
                                 row.names = rownames(try@reductions[["pca"]]@feature.loadings))
colnames(gene_annotation) <- "gene_short_name"


cell_metadata <- as.data.frame(try@assays[["RNA"]]@counts@Dimnames[[2]],
                               row.names = try@assays[["RNA"]]@counts@Dimnames[[2]])
colnames(cell_metadata) <- "barcode"


New_matrix <- try@assays[["RNA"]]@counts
New_matrix <- New_matrix[rownames(try@reductions[["pca"]]@feature.loadings), ]
expression_matrix <- New_matrix

library(monocle3)

cds_from_seurat <- new_cell_data_set(expression_matrix,
                                     cell_metadata = cell_metadata,
                                     gene_metadata = gene_annotation)


recreate.partition <- c(rep(1, length(cds_from_seurat@colData@rownames)))
names(recreate.partition) <- cds_from_seurat@colData@rownames
recreate.partition <- as.factor(recreate.partition)

cds_from_seurat@clusters@listData[["UMAP"]][["partitions"]] <- recreate.partition



list_cluster <- try@active.ident
names(list_cluster) <- try@assays[["RNA"]]@data@Dimnames[[2]]

cds_from_seurat@clusters@listData[["UMAP"]][["clusters"]] <- list_cluster



cds_from_seurat@clusters@listData[["UMAP"]][["louvain_res"]] <- "NA"


cds_from_seurat@int_colData@listData$reducedDims@listData[["UMAP"]] <-try@reductions[["umap"]]@cell.embeddings


cds_from_seurat@preprocess_aux$gene_loadings <- try@reductions[["pca"]]@feature.loadings

Then perform pseudotime analysis:

cds_from_seurat <- learn_graph(cds_from_seurat, use_partition = F)

plot_cells(cds_from_seurat, 
                   color_cells_by = 'cluster',
                   label_groups_by_cluster=TRUE,
                   label_leaves=FALSE,
                   label_branch_points=TRUE,
           graph_label_size=4)

Hope it will help. Good luck!

CaroSegami · 2020-04-10T07:24:17Z

Thanks a lot! It worked!

satijalab · 2020-04-10T16:09:17Z

This is very helpful - thanks! We're preparing to release some updates as well to make this a bit easier, stay tuned!

mojaveazure · 2020-04-10T16:12:23Z

Beta Monocle 3 support us available on SeuratWrappers on the feat/monocle3 branch. Conversion to and from Seurat objects is available with as.cell_data_set and as.Seurat

CaroSegami · 2020-04-13T09:33:33Z

Hi @mojaveazure, I couldn't make the as.cell_data_set command work after installing SeuratWrappers:

library(SeuratWrappers)
cds<-as.cell_data_set(my_seurat_object)

It gives me:

Error in as.cell_data_set(my_seurat_object) :
could not find function "as.cell_data_set"

mojaveazure · 2020-04-13T14:56:04Z

You need to use the feat/monocle3 branch of SeuratWrappers, not the master or develop branches.

CaroSegami · 2020-04-13T15:00:37Z

How do I do that?
Is the installation different?
I used this:

devtools::install_github('satijalab/seurat-wrappers')

CaroSegami · 2020-04-14T08:44:44Z

Nevermind! I managed, but when I try to run "learn_graph" from Monocle3 it gives me this error:

No dimensionality reduction for UMAP calculated. Please run reduce_dimensions with reduction_method = UMAP and cluster_cells before running learn_graph.

Does this mean that it didn't save all the characteristics of the previous merged Seurat object? I could directly run the learn_graph with the cds object resulting from the code above.

vlcm2019 · 2020-04-26T13:00:04Z

Thank you KinSimon96 for sharing your code. It worked for pseudotime analysis.

ahoffrichter · 2020-05-07T08:40:17Z

I had to change

cds_from_seurat@int_colData@listData$reducedDims@listData[["UMAP"]] <-try@reductions[["umap"]]@cell.embeddings

to

cds_from_seurat@reducedDims@listData[["UMAP"]] <-try@reductions[["umap"]]@cell.embeddings

in @KinSimon96 's solution to make it work for me.
Thanks for the workaround!

Edit: Unfortunately, I get stuck at the order_cells command with this. The shiny window opens, but I get an error message:

object 'V1' not found

With the original solution though, I got an Error at:

cds_from_seurat@int_colData@listData$reducedDims@listData[["UMAP"]] <-try@reductions[["umap"]]@cell.embeddings

Error in [...] trying to get slot "listData" from an object of a basic class ("NULL") with no slots

nbahlis · 2020-06-19T20:20:03Z

I downloaded the develop version of monocle 3 but I still get this error below, any help is greatly appreciated

cds <- as.cell_data_set(integrated)
Error in as.cell_data_set(integrated) :
could not find function "as.cell_data_set"

chlee-tabin · 2020-06-19T20:30:58Z

install SeuratWrappers and follow https://htmlpreview.github.io/?https://github.com/satijalab/seurat-wrappers/blob/master/docs/monocle3.html

GhobrialMoheb · 2020-07-14T22:10:24Z

@mojaveazure , thanks for the update in seuratwrappers, I have a question regarding plotting in tSNE, rather than UMAP?
It seems that I can only plot UMAP, rather than tSNE in the cell dataset object converted from Seurat

qoiopipq · 2021-04-07T06:22:11Z

@Mohebg1234 same issue

Have you figured out how to do it using SeuratWrappers? Or have to do it manually?

vicscott · 2022-03-24T12:02:01Z

Hello, Here is how I convert the object of class Seurat into a cds object (Monocle3) for pseudotime analysis. Eveything will be unchanged. I don't know if it will work with SCTransformed, but you should be able to do your own modifications with the code below. My Seurat object here is try

gene_annotation <- as.data.frame(rownames(try@reductions[["pca"]]@feature.loadings),
                                 row.names = rownames(try@reductions[["pca"]]@feature.loadings))
colnames(gene_annotation) <- "gene_short_name"


cell_metadata <- as.data.frame(try@assays[["RNA"]]@counts@Dimnames[[2]],
                               row.names = try@assays[["RNA"]]@counts@Dimnames[[2]])
colnames(cell_metadata) <- "barcode"


New_matrix <- try@assays[["RNA"]]@counts
New_matrix <- New_matrix[rownames(try@reductions[["pca"]]@feature.loadings), ]
expression_matrix <- New_matrix

library(monocle3)

cds_from_seurat <- new_cell_data_set(expression_matrix,
                                     cell_metadata = cell_metadata,
                                     gene_metadata = gene_annotation)


recreate.partition <- c(rep(1, length(cds_from_seurat@colData@rownames)))
names(recreate.partition) <- cds_from_seurat@colData@rownames
recreate.partition <- as.factor(recreate.partition)

cds_from_seurat@clusters@listData[["UMAP"]][["partitions"]] <- recreate.partition



list_cluster <- try@active.ident
names(list_cluster) <- try@assays[["RNA"]]@data@Dimnames[[2]]

cds_from_seurat@clusters@listData[["UMAP"]][["clusters"]] <- list_cluster



cds_from_seurat@clusters@listData[["UMAP"]][["louvain_res"]] <- "NA"


cds_from_seurat@int_colData@listData$reducedDims@listData[["UMAP"]] <-try@reductions[["umap"]]@cell.embeddings


cds_from_seurat@preprocess_aux$gene_loadings <- try@reductions[["pca"]]@feature.loadings

Then perform pseudotime analysis:

cds_from_seurat <- learn_graph(cds_from_seurat, use_partition = F)

plot_cells(cds_from_seurat, 
                   color_cells_by = 'cluster',
                   label_groups_by_cluster=TRUE,
                   label_leaves=FALSE,
                   label_branch_points=TRUE,
           graph_label_size=4)

Hope it will help. Good luck!

So great, it can even help me at 2022!

paulatn240 · 2022-09-29T11:42:34Z

Hello, Here is how I convert the object of class Seurat into a cds object (Monocle3) for pseudotime analysis. Eveything will be unchanged. I don't know if it will work with SCTransformed, but you should be able to do your own modifications with the code below. My Seurat object here is try

gene_annotation <- as.data.frame(rownames(try@reductions[["pca"]]@feature.loadings),
                                 row.names = rownames(try@reductions[["pca"]]@feature.loadings))
colnames(gene_annotation) <- "gene_short_name"


cell_metadata <- as.data.frame(try@assays[["RNA"]]@counts@Dimnames[[2]],
                               row.names = try@assays[["RNA"]]@counts@Dimnames[[2]])
colnames(cell_metadata) <- "barcode"


New_matrix <- try@assays[["RNA"]]@counts
New_matrix <- New_matrix[rownames(try@reductions[["pca"]]@feature.loadings), ]
expression_matrix <- New_matrix

library(monocle3)

cds_from_seurat <- new_cell_data_set(expression_matrix,
                                     cell_metadata = cell_metadata,
                                     gene_metadata = gene_annotation)


recreate.partition <- c(rep(1, length(cds_from_seurat@colData@rownames)))
names(recreate.partition) <- cds_from_seurat@colData@rownames
recreate.partition <- as.factor(recreate.partition)

cds_from_seurat@clusters@listData[["UMAP"]][["partitions"]] <- recreate.partition



list_cluster <- try@active.ident
names(list_cluster) <- try@assays[["RNA"]]@data@Dimnames[[2]]

cds_from_seurat@clusters@listData[["UMAP"]][["clusters"]] <- list_cluster



cds_from_seurat@clusters@listData[["UMAP"]][["louvain_res"]] <- "NA"


cds_from_seurat@int_colData@listData$reducedDims@listData[["UMAP"]] <-try@reductions[["umap"]]@cell.embeddings


cds_from_seurat@preprocess_aux$gene_loadings <- try@reductions[["pca"]]@feature.loadings

Then perform pseudotime analysis:

cds_from_seurat <- learn_graph(cds_from_seurat, use_partition = F)

plot_cells(cds_from_seurat, 
                   color_cells_by = 'cluster',
                   label_groups_by_cluster=TRUE,
                   label_leaves=FALSE,
                   label_branch_points=TRUE,
           graph_label_size=4)

Hope it will help. Good luck!

Thank you very much! It works for me, I just had to change preprocess_aux for reduce_dim_aux :)

qimiaonnegliguo · 2024-01-24T10:16:57Z

Hello, Here is how I convert the object of class Seurat into a cds object (Monocle3) for pseudotime analysis. Eveything will be unchanged. I don't know if it will work with SCTransformed, but you should be able to do your own modifications with the code below. My Seurat object here is try

gene_annotation <- as.data.frame(rownames(try@reductions[["pca"]]@feature.loadings),
                                 row.names = rownames(try@reductions[["pca"]]@feature.loadings))
colnames(gene_annotation) <- "gene_short_name"


cell_metadata <- as.data.frame(try@assays[["RNA"]]@counts@Dimnames[[2]],
                               row.names = try@assays[["RNA"]]@counts@Dimnames[[2]])
colnames(cell_metadata) <- "barcode"


New_matrix <- try@assays[["RNA"]]@counts
New_matrix <- New_matrix[rownames(try@reductions[["pca"]]@feature.loadings), ]
expression_matrix <- New_matrix

library(monocle3)

cds_from_seurat <- new_cell_data_set(expression_matrix,
                                     cell_metadata = cell_metadata,
                                     gene_metadata = gene_annotation)


recreate.partition <- c(rep(1, length(cds_from_seurat@colData@rownames)))
names(recreate.partition) <- cds_from_seurat@colData@rownames
recreate.partition <- as.factor(recreate.partition)

cds_from_seurat@clusters@listData[["UMAP"]][["partitions"]] <- recreate.partition



list_cluster <- try@active.ident
names(list_cluster) <- try@assays[["RNA"]]@data@Dimnames[[2]]

cds_from_seurat@clusters@listData[["UMAP"]][["clusters"]] <- list_cluster



cds_from_seurat@clusters@listData[["UMAP"]][["louvain_res"]] <- "NA"


cds_from_seurat@int_colData@listData$reducedDims@listData[["UMAP"]] <-try@reductions[["umap"]]@cell.embeddings


cds_from_seurat@preprocess_aux$gene_loadings <- try@reductions[["pca"]]@feature.loadings

Then perform pseudotime analysis:

cds_from_seurat <- learn_graph(cds_from_seurat, use_partition = F)

plot_cells(cds_from_seurat, 
                   color_cells_by = 'cluster',
                   label_groups_by_cluster=TRUE,
                   label_leaves=FALSE,
                   label_branch_points=TRUE,
           graph_label_size=4)

Hope it will help. Good luck!

hi thanks for your great work. I have a small question. If im working on an integrated Seurat object, should I use 'integrated' instead of 'RNA' assay? Thank you.

satijalab closed this as completed Apr 10, 2020

vlcm2019 mentioned this issue Apr 26, 2020

Error in DefaultDimReduc(object = x, assay = assay) : could not find function "DefaultDimReduc" #2909

Closed

micosacak mentioned this issue Oct 26, 2023

Is it possible to use Scanpy Generated UMAP for Monocle3 trajectory analysis? cole-trapnell-lab/monocle-release#444

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WeipengMO mentioned this issue Nov 24, 2023

Error in make_nn_matrix when running graph_test cole-trapnell-lab/monocle3#617

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Using Seurat merged object for pseudo time analysis in Monocle 3 #2833

Using Seurat merged object for pseudo time analysis in Monocle 3 #2833

CaroSegami commented Apr 8, 2020

KinSimon96 commented Apr 9, 2020 •

edited

CaroSegami commented Apr 10, 2020

satijalab commented Apr 10, 2020

mojaveazure commented Apr 10, 2020

CaroSegami commented Apr 13, 2020

mojaveazure commented Apr 13, 2020

CaroSegami commented Apr 13, 2020

CaroSegami commented Apr 14, 2020

vlcm2019 commented Apr 26, 2020

ahoffrichter commented May 7, 2020 •

edited

nbahlis commented Jun 19, 2020

chlee-tabin commented Jun 19, 2020

GhobrialMoheb commented Jul 14, 2020

qoiopipq commented Apr 7, 2021

vicscott commented Mar 24, 2022

paulatn240 commented Sep 29, 2022

qimiaonnegliguo commented Jan 24, 2024

Using Seurat merged object for pseudo time analysis in Monocle 3 #2833

Using Seurat merged object for pseudo time analysis in Monocle 3 #2833

Comments

CaroSegami commented Apr 8, 2020

KinSimon96 commented Apr 9, 2020 • edited

CaroSegami commented Apr 10, 2020

satijalab commented Apr 10, 2020

mojaveazure commented Apr 10, 2020

CaroSegami commented Apr 13, 2020

mojaveazure commented Apr 13, 2020

CaroSegami commented Apr 13, 2020

CaroSegami commented Apr 14, 2020

vlcm2019 commented Apr 26, 2020

ahoffrichter commented May 7, 2020 • edited

nbahlis commented Jun 19, 2020

chlee-tabin commented Jun 19, 2020

GhobrialMoheb commented Jul 14, 2020

qoiopipq commented Apr 7, 2021

vicscott commented Mar 24, 2022

paulatn240 commented Sep 29, 2022

qimiaonnegliguo commented Jan 24, 2024

KinSimon96 commented Apr 9, 2020 •

edited

ahoffrichter commented May 7, 2020 •

edited