Re-running Seurat

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Last updated: 2025-04-03

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Knit directory: muse/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/seurat_rerun.Rmd) and HTML (docs/seurat_rerun.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	b6fe07d	Dave Tang	2025-04-03	UMAPs for comparing Seurat version 4
html	2e98820	Dave Tang	2025-04-03	Build site.
Rmd	56646ff	Dave Tang	2025-04-03	Test gene and cell/barcode order separately
html	4e49f2d	Dave Tang	2025-04-01	Build site.
Rmd	476c4ad	Dave Tang	2025-04-01	Create new Seurat object with the same order
html	319c615	Dave Tang	2025-04-01	Build site.
Rmd	8c145e4	Dave Tang	2025-04-01	Using Seurat version 4
html	4f8b0d2	Dave Tang	2025-04-01	Build site.
Rmd	90696a0	Dave Tang	2025-04-01	Re-running Seurat

---

If I re-run Seurat in the same manner with the same dataset, will I get identical results?

Seurat object

Import raw pbmc3k dataset from my server.

seurat_obj <- readRDS(url("https://davetang.org/file/pbmc3k_seurat.rds", "rb"))
seurat_obj

An object of class Seurat 
32738 features across 2700 samples within 1 assay 
Active assay: RNA (32738 features, 0 variable features)
 1 layer present: counts

Filter.

pbmc3k <- CreateSeuratObject(
  counts = seurat_obj@assays$RNA$counts,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)
pbmc3k

An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
 1 layer present: counts

Seurat workflows

Seurat workflows as functions.

seurat_wf_v4 <- function(seurat_obj, scale_factor = 1e4, num_features = 2000, num_pcs = 30, cluster_res = 0.5, debug_flag = FALSE){
  
  seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", scale.factor = scale_factor, verbose = debug_flag)
  seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = num_features, verbose = debug_flag)
  seurat_obj <- ScaleData(seurat_obj, verbose = debug_flag)
  seurat_obj <- RunPCA(seurat_obj, verbose = debug_flag)
  seurat_obj <- RunUMAP(seurat_obj, dims = 1:num_pcs, verbose = debug_flag)
  seurat_obj <- FindNeighbors(seurat_obj, dims = 1:num_pcs, verbose = debug_flag)
  seurat_obj <- FindClusters(seurat_obj, resolution = cluster_res, verbose = debug_flag)
  
  seurat_obj
}

seurat_wf_v5 <- function(seurat_obj, scale_factor = 1e4, num_features = 2000, num_pcs = 30, cluster_res = 0.5, debug_flag = FALSE){
  
  seurat_obj <- SCTransform(seurat_obj, verbose = debug_flag)
  seurat_obj <- RunPCA(seurat_obj, verbose = debug_flag)
  seurat_obj <- RunUMAP(seurat_obj, dims = 1:num_pcs, verbose = debug_flag)
  seurat_obj <- FindNeighbors(seurat_obj, dims = 1:num_pcs, verbose = debug_flag)
  seurat_obj <- FindClusters(seurat_obj, resolution = cluster_res, verbose = debug_flag)
  
  seurat_obj
}

First run

Process pbmc3k using the Seurat version 5 workflow.

pbmc3k_v5_1 <- seurat_wf_v5(pbmc3k)

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

pbmc3k_v5_1

An object of class Seurat 
26286 features across 2700 samples within 2 assays 
Active assay: SCT (12572 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap

Second run

Process pbmc3k using the Seurat version 5 workflow again.

pbmc3k_v5_2 <- seurat_wf_v5(pbmc3k)
pbmc3k_v5_2

An object of class Seurat 
26286 features across 2700 samples within 2 assays 
Active assay: SCT (12572 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap

Compare first and second runs

Compare UMAPs.

DimPlot(pbmc3k_v5_1) + DimPlot(pbmc3k_v5_2)

Past versions of compare_umaps-1.png

Version	Author	Date
4f8b0d2	Dave Tang	2025-04-01

Looks the same but let’s double check.

identical(
  pbmc3k_v5_1@reductions$umap@cell.embeddings,
  pbmc3k_v5_2@reductions$umap@cell.embeddings
)

[1] TRUE

Compare clustering.

identical(
  row.names(pbmc3k_v5_1@meta.data),
  row.names(pbmc3k_v5_2@meta.data)
)

[1] TRUE

identical(
  pbmc3k_v5_1@meta.data$seurat_clusters,
  pbmc3k_v5_2@meta.data$seurat_clusters
)

[1] TRUE

Reorder

Use the same dataset but re-order the genes in the count matrix randomly.

my_mat <- seurat_obj@assays$RNA$counts
set.seed(1984)
row_order <- sample(rownames(my_mat))
my_mat <- my_mat[row_order, ]

stopifnot(all(rownames(my_mat) %in% rownames(seurat_obj@assays$RNA$counts)))
stopifnot(all(colnames(my_mat) %in% colnames(seurat_obj@assays$RNA$counts)))

pbmc3k_reorder_genes <- CreateSeuratObject(
  counts = my_mat,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

stopifnot(all(colnames(pbmc3k_reorder_genes@assays$RNA$counts) %in% colnames(pbmc3k@assays$RNA$counts)))
stopifnot(all(rownames(pbmc3k_reorder_genes@assays$RNA$counts) %in% rownames(pbmc3k@assays$RNA$counts)))

pbmc3k_reorder_genes

An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
 1 layer present: counts

Use the same dataset but re-order the barcodes in the count matrix randomly.

my_mat <- seurat_obj@assays$RNA$counts
set.seed(1984)
col_order <- sample(colnames(my_mat))
my_mat <- my_mat[, col_order]

stopifnot(all(rownames(my_mat) %in% rownames(seurat_obj@assays$RNA$counts)))
stopifnot(all(colnames(my_mat) %in% colnames(seurat_obj@assays$RNA$counts)))

pbmc3k_reorder_cells <- CreateSeuratObject(
  counts = my_mat,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

stopifnot(all(colnames(pbmc3k_reorder_cells@assays$RNA$counts) %in% colnames(pbmc3k@assays$RNA$counts)))
stopifnot(all(rownames(pbmc3k_reorder_cells@assays$RNA$counts) %in% rownames(pbmc3k@assays$RNA$counts)))

pbmc3k_reorder_cells

An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
 1 layer present: counts

Process the re-ordered pbmc3k dataset using the Seurat version 5 workflow again.

pbmc3k_v5_genes <- seurat_wf_v5(pbmc3k_reorder_genes)
pbmc3k_v5_cells <- seurat_wf_v5(pbmc3k_reorder_cells)

pbmc3k_v5_1

An object of class Seurat 
26286 features across 2700 samples within 2 assays 
Active assay: SCT (12572 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap

pbmc3k_v5_cells

An object of class Seurat 
26286 features across 2700 samples within 2 assays 
Active assay: SCT (12572 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap

Compare third run

Compare UMAPs.

(DimPlot(pbmc3k_v5_1) + ggtitle("Re-ordered genes")) + DimPlot(pbmc3k_v5_genes)

Past versions of compare_umaps_3-1.png

Version	Author	Date
2e98820	Dave Tang	2025-04-03
4f8b0d2	Dave Tang	2025-04-01

(DimPlot(pbmc3k_v5_1) + ggtitle("Re-ordered cells")) + DimPlot(pbmc3k_v5_cells)

Past versions of compare_umaps_3-2.png

Version	Author	Date
2e98820	Dave Tang	2025-04-03

Compare clustering.

compare_clustering <- function(obj1, obj2){
  idx <- match(row.names(obj1@meta.data), row.names(obj2@meta.data))
  stopifnot(row.names(obj1@meta.data) == row.names(obj2@meta.data)[idx])
  table(
    obj1@meta.data$seurat_clusters,
    obj2@meta.data$seurat_clusters[idx]
  )
}

compare_clustering(pbmc3k_v5_1, pbmc3k_v5_genes)

   
      0   1   2   3   4   5   6   7   8   9
  0 970   0   4   0   0   0   0   0   1   0
  1   0 497   0   0   0   0   0   0   0   0
  2   1   0 365   0   0   0   0   0   0   0
  3   0   0   0 359   0   0   0   0   0   0
  4   0   0   1   0 156   0   0   0   0   0
  5   0   0   0   0   0 154   0   0   0   0
  6   3   0   0   0   0   0  97   0   0   0
  7   2   0  25   0   0   0   0   0  19   0
  8   0   0   0   0   0   0   0  34   0   0
  9   0   0   0   0   0   0   0   0   0  12

compare_clustering(pbmc3k_v5_1, pbmc3k_v5_cells)

   
      0   1   2   3   4   5   6   7   8   9
  0 970   0   5   0   0   0   0   0   0   0
  1   0 497   0   0   0   0   0   0   0   0
  2   1   0 365   0   0   0   0   0   0   0
  3   0   0   5 354   0   0   0   0   0   0
  4   0   0   1   0 156   0   0   0   0   0
  5   0   0   0   0   0 154   0   0   0   0
  6   0   0   0   0   0   0 100   0   0   0
  7   1   0  25   1   0   0   0   0  19   0
  8   0   0   0   0   0   0   0  34   0   0
  9   0   0   0   0   0   0   0   0   0  12

compare_clustering(pbmc3k_v5_genes, pbmc3k_v5_cells)

   
      0   1   2   3   4   5   6   7   8   9
  0 971   0   2   0   0   0   3   0   0   0
  1   0 497   0   0   0   0   0   0   0   0
  2   0   0 393   1   1   0   0   0   0   0
  3   0   0   5 354   0   0   0   0   0   0
  4   0   0   1   0 155   0   0   0   0   0
  5   0   0   0   0   0 154   0   0   0   0
  6   0   0   0   0   0   0  97   0   0   0
  7   0   0   0   0   0   0   0  34   0   0
  8   1   0   0   0   0   0   0   0  19   0
  9   0   0   0   0   0   0   0   0   0  12

Use Seurat version 4

What if we used version 4?

pbmc3k_v4_1 <- seurat_wf_v4(pbmc3k)
pbmc3k_v4_genes <- seurat_wf_v4(pbmc3k_reorder_genes)
pbmc3k_v4_cells <- seurat_wf_v4(pbmc3k_reorder_cells)

(DimPlot(pbmc3k_v4_1) + ggtitle("Re-ordered genes")) + DimPlot(pbmc3k_v4_genes)

(DimPlot(pbmc3k_v4_1) + ggtitle("Re-ordered cells")) + DimPlot(pbmc3k_v4_cells)

compare_clustering(pbmc3k_v4_1, pbmc3k_v4_genes)

   
       0    1    2    3    4    5    6    7
  0 1187    0    0    0    0    0    0    0
  1    0  491    0    0    0    0    0    0
  2    0    0  351    0    0    0    0    0
  3    0    0    0  301    0    0    0    0
  4    0    0    0    0  163    0    0    0
  5    0    0    0    0    0  161    0    0
  6    0    0    0    0    0    0   32    0
  7    0    0    0    0    0    0    0   14

compare_clustering(pbmc3k_v4_1, pbmc3k_v4_cells)

   
       0    1    2    3    4    5    6    7
  0 1182    0    0    5    0    0    0    0
  1    0  489    0    0    2    0    0    0
  2    0    0  351    0    0    0    0    0
  3    6    0    0  295    0    0    0    0
  4    0    0    0    1    0  162    0    0
  5    0    0    0    0  161    0    0    0
  6    0    0    0    0    0    0   32    0
  7    0    1    0    0    0    0    0   13

Re-ordering genes results in the same results using Seurat version 4 but different results when re-ordering cells.

Same order

Since it seems a different order of barcodes/cells generates slightly different results, what if we used the same order? (The easiest way is to simply create a new Seurat object with the new order. Originally, I had tried to re-order an existing Seurat object but ended up creating an invalid Seurat object.)

pbmc3k <- CreateSeuratObject(
  counts = seurat_obj@assays$RNA$counts,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

set.seed(1941)
rs <- sample(rownames(pbmc3k@assays$RNA$counts))
cs <- sample(colnames(pbmc3k@assays$RNA$counts))

pbmc3k_c <- CreateSeuratObject(
  counts = pbmc3k@assays$RNA$counts[rs, cs],
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

pbmc3k_d <- CreateSeuratObject(
  counts = pbmc3k_reorder_cells@assays$RNA$counts[rs, cs],
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)

pbmc3k_v4_c <- seurat_wf_v4(pbmc3k_c)
pbmc3k_v4_d <- seurat_wf_v4(pbmc3k_d)

stopifnot(row.names(pbmc3k_v4_c@meta.data) == row.names(pbmc3k_v4_d@meta.data))
stopifnot(row.names(pbmc3k_v4_c@reductions$pca@cell.embeddings) == row.names(pbmc3k_v4_d@reductions$pca@cell.embeddings))
stopifnot(row.names(pbmc3k_v4_c@reductions$umap@cell.embeddings) == row.names(pbmc3k_v4_d@reductions$umap@cell.embeddings))

DimPlot(pbmc3k_v4_c, reduction = "pca") + DimPlot(pbmc3k_v4_d, reduction = "pca")

Past versions of compare_same_order-1.png

Version	Author	Date
4e49f2d	Dave Tang	2025-04-01

identical(
  pbmc3k_v4_c@reductions$pca@cell.embeddings,
  pbmc3k_v4_d@reductions$pca@cell.embeddings
)

[1] TRUE

DimPlot(pbmc3k_v4_c) + DimPlot(pbmc3k_v4_d)

Past versions of compare_same_order-2.png

Version	Author	Date
4e49f2d	Dave Tang	2025-04-01

identical(
  pbmc3k_v4_c@reductions$umap@cell.embeddings,
  pbmc3k_v4_d@reductions$umap@cell.embeddings
)

[1] TRUE

table(
  pbmc3k_v4_c@meta.data$seurat_clusters,
  pbmc3k_v4_d@meta.data$seurat_clusters
)

   
       0    1    2    3    4    5    6    7
  0 1182    0    0    0    0    0    0    0
  1    0  491    0    0    0    0    0    0
  2    0    0  351    0    0    0    0    0
  3    0    0    0  307    0    0    0    0
  4    0    0    0    0  162    0    0    0
  5    0    0    0    0    0  161    0    0
  6    0    0    0    0    0    0   32    0
  7    0    0    0    0    0    0    0   14

Summary

Seurat version 5:

• Re-running Seurat on the same object produced the same UMAP and clustering results.
• Re-running Seurat on the same data but shuffled genes produced different results.
• Re-running Seurat on the same data but shuffled barcodes/cells produced different results.

Tim answered with respect to re-ordering cells:

The graph-based clustering algorithm (Louvain, SLM, Leiden, etc.) is non-deterministic. Identical results across different runs of the algorithm are obtained by setting the same random seed. Changing the order of the cells will change the order that nodes are visited during the local moving phase of the algorithm and will potentially change the final cluster identities of cells.

However it is concerning that re-ordering genes produced different results when using Seurat version 5.

Seurat version 4:

• Re-running Seurat on the same data but shuffled genes produced the same result.
• Re-running Seurat on the same data but shuffled barcodes/cells produced different results.

Differences can be mitigated if the same order of cells is used.

Session information

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] Seurat_5.2.1       SeuratObject_5.0.2 sp_2.2-0           lubridate_1.9.3   
 [5] forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4        purrr_1.0.2       
 [9] readr_2.1.5        tidyr_1.3.1        tibble_3.2.1       ggplot2_3.5.1     
[13] tidyverse_2.0.0    workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.17.1          
  [3] jsonlite_1.8.9              magrittr_2.0.3             
  [5] spatstat.utils_3.1-2        farver_2.1.2               
  [7] rmarkdown_2.28              zlibbioc_1.52.0            
  [9] fs_1.6.4                    vctrs_0.6.5                
 [11] ROCR_1.0-11                 DelayedMatrixStats_1.28.1  
 [13] spatstat.explore_3.3-4      S4Arrays_1.6.0             
 [15] htmltools_0.5.8.1           SparseArray_1.6.2          
 [17] sass_0.4.9                  sctransform_0.4.1          
 [19] parallelly_1.38.0           KernSmooth_2.23-24         
 [21] bslib_0.8.0                 htmlwidgets_1.6.4          
 [23] ica_1.0-3                   plyr_1.8.9                 
 [25] plotly_4.10.4               zoo_1.8-13                 
 [27] cachem_1.1.0                whisker_0.4.1              
 [29] igraph_2.1.4                mime_0.12                  
 [31] lifecycle_1.0.4             pkgconfig_2.0.3            
 [33] Matrix_1.7-0                R6_2.5.1                   
 [35] fastmap_1.2.0               GenomeInfoDbData_1.2.13    
 [37] MatrixGenerics_1.18.1       fitdistrplus_1.2-2         
 [39] future_1.34.0               shiny_1.10.0               
 [41] digest_0.6.37               colorspace_2.1-1           
 [43] S4Vectors_0.44.0            patchwork_1.3.0            
 [45] ps_1.8.1                    rprojroot_2.0.4            
 [47] tensor_1.5                  RSpectra_0.16-2            
 [49] irlba_2.3.5.1               GenomicRanges_1.58.0       
 [51] labeling_0.4.3              progressr_0.15.0           
 [53] spatstat.sparse_3.1-0       timechange_0.3.0           
 [55] httr_1.4.7                  polyclip_1.10-7            
 [57] abind_1.4-8                 compiler_4.4.1             
 [59] withr_3.0.2                 fastDummies_1.7.5          
 [61] highr_0.11                  MASS_7.3-60.2              
 [63] DelayedArray_0.32.0         tools_4.4.1                
 [65] lmtest_0.9-40               httpuv_1.6.15              
 [67] future.apply_1.11.3         goftest_1.2-3              
 [69] glmGamPoi_1.18.0            glue_1.8.0                 
 [71] callr_3.7.6                 nlme_3.1-164               
 [73] promises_1.3.2              grid_4.4.1                 
 [75] Rtsne_0.17                  getPass_0.2-4              
 [77] cluster_2.1.6               reshape2_1.4.4             
 [79] generics_0.1.3              gtable_0.3.6               
 [81] spatstat.data_3.1-4         tzdb_0.4.0                 
 [83] data.table_1.16.2           hms_1.1.3                  
 [85] XVector_0.46.0              BiocGenerics_0.52.0        
 [87] spatstat.geom_3.3-5         RcppAnnoy_0.0.22           
 [89] ggrepel_0.9.6               RANN_2.6.2                 
 [91] pillar_1.10.1               spam_2.11-1                
 [93] RcppHNSW_0.6.0              later_1.3.2                
 [95] splines_4.4.1               lattice_0.22-6             
 [97] survival_3.6-4              deldir_2.0-4               
 [99] tidyselect_1.2.1            miniUI_0.1.1.1             
[101] pbapply_1.7-2               knitr_1.48                 
[103] git2r_0.35.0                gridExtra_2.3              
[105] IRanges_2.40.1              SummarizedExperiment_1.36.0
[107] scattermore_1.2             stats4_4.4.1               
[109] xfun_0.48                   Biobase_2.66.0             
[111] matrixStats_1.5.0           UCSC.utils_1.2.0           
[113] stringi_1.8.4               lazyeval_0.2.2             
[115] yaml_2.3.10                 evaluate_1.0.1             
[117] codetools_0.2-20            cli_3.6.3                  
[119] uwot_0.2.3                  xtable_1.8-4               
[121] reticulate_1.41.0           munsell_0.5.1              
[123] processx_3.8.4              jquerylib_0.1.4            
[125] GenomeInfoDb_1.42.3         Rcpp_1.0.13                
[127] globals_0.16.3              spatstat.random_3.3-2      
[129] png_0.1-8                   spatstat.univar_3.1-2      
[131] parallel_4.4.1              dotCall64_1.2              
[133] sparseMatrixStats_1.18.0    listenv_0.9.1              
[135] viridisLite_0.4.2           scales_1.3.0               
[137] ggridges_0.5.6              crayon_1.5.3               
[139] rlang_1.1.4                 cowplot_1.1.3