Cannot convert the inetgarted seurat object to the cds for monocle 3
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8 weeks ago

Hello,

I am receiving the follow error:

cds <- as.cell_data_set(b.seu)

Warning: Monocle 3 trajectories require cluster partitions, which Seurat does not calculate. Please run 'cluster_cells' on your cell_data_set object.

library(Seurat)
library(patchwork)
library(SeuratWrappers)
library(ggplot2)
library(tidyverse)

control = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu1_099")
inhibitor = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu2_099")
dnavaccine = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu3_099")
combitr = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu4_099")

Created the Seurat Object:
control_seurat = CreateSeuratObject(counts = control$Gene Expression,
project = "control")
inhibitor_seurat = CreateSeuratObject(counts = inhibitor$Gene
Expression, project = "inhibitor")
dnavaccine_seurat = CreateSeuratObject(counts = dnavaccine$Gene
Expression, project = "dnavaccine")
combitr_seurat = CreateSeuratObject(counts = combitr$Gene Expression,
project = "combitr")

Merged the datasets
merge_seurat = merge(x = control_seurat, y = c(inhibitor_seurat,
dnavaccine_seurat, combitr_seurat), add.cell.id = c("control",
"inhibitor","dnavaccine", "combitr"))
head(merge_seurat@meta.data,5)

Filtering the mitochondrial genes
merge_seurat$mitoRatio <- PercentageFeatureSet(object = merge_seurat,
pattern = "^mt-")
metadata <- merge_seurat@meta.data
metadata$cells <- rownames(metadata)
library(dplyr)
metadata <- metadata %>%
dplyr::rename(group = orig.ident,
nUMI = nCount_RNA,
nGene = nFeature_RNA)
library(stringr)
metadata$sample <- NA
metadata$sample[which(str_detect(metadata$cells, "^control_"))] <- "control"
metadata$sample[which(str_detect(metadata$cells, "^inhibitor_"))] <- "inhibitor"
metadata$sample[which(str_detect(metadata$cells, "^dnavaccine_"))] <-
"dnavaccine"
metadata$sample[which(str_detect(metadata$cells, "^combitr_"))] <- "combitr"
merge_seurat@meta.data <- metadata
head(merge_seurat@meta.data,5)
tail(merge_seurat@meta.data,5)
data.list <- SplitObject(merge_seurat, split.by = "sample")

data.list <- lapply(X = data.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst")
})

Integration of datasets
features <- SelectIntegrationFeatures(object.list = data.list)
immune.anchors <- FindIntegrationAnchors(object.list = data.list,
anchor.features = features)
immune.combined <- IntegrateData(anchorset = immune.anchors)
DefaultAssay(immune.combined) <- "integrated"
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:10)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca",
dims = 1:30)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "sample")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE)
DimPlot(immune.combined, reduction = "umap", split.by = "sample")

DefaultAssay(immune.combined) <- "RNA"
library(metap)
all.markers084 <- FindAllMarkers(immune.combined, only.pos = TRUE,
min.pct = 0.25, logfc.threshold = 0.25)

b.seu <- WhichCells(immune.combined, idents= c("B", "CD21+ B", "Immature B", "Tuft", "Inflammatory mono", "Goblet cell"))
b.seu = subset(immune.combined, cells = b.seu)
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 = 'seurat_clusters', label = T)

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)

Warning: Monocle 3 trajectories require cluster partitions, which Seurat does not calculate. Please run 'cluster_cells' on your cell_data_set object.

monocle 3 seurat • 396 views
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Entering edit mode

First of all, it's a warning and not an error. I guess your cell data object has been created.

As suggested in the warning either run cluster_cells function OR you can assign a dummy partition manually.

recreate.partition <- c(rep(1, length(cds@colData@rownames)))
names(recreate.partition) <- cds@colData@rownames
recreate.partition <- as.factor(recreate.partition)
cds@clusters@listData[["UMAP"]][["partitions"]] <- recreate.partition

Regards,

Nitin N.

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Entering edit mode

Hello, Thank you very much for your reply. I did run the recreate partition code and added the following code. Im still receiving an error:

> recreate.partition <- c(rep(1, length(cds@colData@rownames)))
> names(recreate.partition) <- cds@colData@rownames
> recreate.partition <- as.factor(recreate.partition)
> cds@clusters@listData[["UMAP"]][["partitions"]] <- recreate.partition

> list_cluster <- b.seu@meta.data[[sprintf("seurat_clusters")]]
> #list_cluster <- b.seu@meta.data[[sprintf("ClusterNames_%s_%sPC", 0.5, 20)]]
> names(list_cluster) <- b.seu@assays[["RNA"]]@data@Dimnames[[2]]

> list_cluster <- b.seu@meta.data[[sprintf("seurat_clusters")]]
> #list_cluster <- b.seu@meta.data[[sprintf("ClusterNames_%s_%sPC", 0.5, 20)]]
> names(list_cluster) <- b.seu@assays[["RNA"]]@data@Dimnames[[2]]

> cds@clusters@listData[["UMAP"]][["clusters"]] <- list_cluster
> cds@clusters@listData[["UMAP"]][["louvain_res"]] <- "NA"

> cds <- monocle3::preprocess_cds(cds, method = "PCA", norm_method = "none")
> cds <- monocle3::reduce_dimension(cds, reduction_method = "UMAP")
No preprocess_method specified, using preprocess_method = 'PCA'

> reducedDim(cds, "UMAP") <- Embeddings(b.seu, "umap")

> cds@preprocess_aux$gene_loadings <- b.seu@reductions[["pca"]]@feature.loadings
Error in cds@preprocess_aux$gene_loadings <- b.seu@reductions[["pca"]]@feature.loadings : no slot of name "preprocess_aux" for this object of class "cell_data_set"

> cds@preprocess_aux$gene_loadings <- Loadings(seu, "pca")
Error in Loadings(seu, "pca") : object 'seu' not found

> cds@preprocess_aux$gene_loadings <- Loadings(b.seu, "pca")
Error in cds@preprocess_aux$gene_loadings <- Loadings(b.seu, "pca") : no slot of name "preprocess_aux" for this object of class "cell_data_set"

Anything that can be done?

Thank you.

Kind Regards, Shweta Johari

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Entering edit mode

This thread might help to create a monocle3 object using Seurat object.

Using Monocle 3 with Seurat 3 integrated object

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