Re-running Seurat

Seurat object

Seurat workflow

First run

Second run

Compare first and second runs

Third run

Compare third run

Summary

Last updated: 2025-04-01

Checks: 7 0

Knit directory: muse/

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File	Version	Author	Date	Message
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 workflow

Seurat version 5 workflow as a function: seurat_wf_v5.

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_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_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_2 <- seurat_wf_v5(pbmc3k)
pbmc3k_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_1) + DimPlot(pbmc3k_1)

Looks the same but let’s double check.

identical(
  pbmc3k_1@reductions$umap@cell.embeddings,
  pbmc3k_2@reductions$umap@cell.embeddings
)

[1] TRUE

Compare clustering.

identical(
  row.names(pbmc3k_1@meta.data),
  row.names(pbmc3k_2@meta.data)
)

[1] TRUE

identical(
  pbmc3k_1@meta.data$seurat_clusters,
  pbmc3k_2@meta.data$seurat_clusters
)

[1] TRUE

Third run

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

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

pbmc3k_reordered <- CreateSeuratObject(
  counts = my_mat,
  min.cells = 3,
  min.features = 200,
  project = "pbmc3k"
)
pbmc3k_reordered

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_3 <- seurat_wf_v5(pbmc3k_reordered)
pbmc3k_3

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_1) + DimPlot(pbmc3k_3)

Compare clustering.

idx <- match(row.names(pbmc3k_1@meta.data), row.names(pbmc3k_3@meta.data))

stopifnot(row.names(pbmc3k_1@meta.data) == row.names(pbmc3k_3@meta.data)[idx])

table(
  pbmc3k_1@meta.data$seurat_clusters,
  pbmc3k_3@meta.data$seurat_clusters[idx]
)

   
      0   1   2   3   4   5   6   7   8   9
  0 968   0   1   0   0   0   6   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   4 355   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   1   0   0   0   0   0  99   0   0   0
  7   0   0   2   1   0   0   0  43   0   0
  8   0   0   0   0   0   0   0   0  34   0
  9   0   0   0   0   0   0   0   0   0  12

Summary

Using 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 produced different results.

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