• Seurat object
  • Seurat workflows
    • Data layer
    • Compare clustering

Last updated: 2025-03-10

Checks: 7 0

Knit directory: muse/

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Rmd 48661b3 Dave Tang 2025-03-10 Seurat version 4 vs. 5

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

Process with the Seurat 4 workflow.

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
}

pbmc3k_v4 <- seurat_wf_v4(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_v4
An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 2 dimensional reductions calculated: pca, umap

UMAP.

DimPlot(pbmc3k_v4, reduction = "umap")

Version Author Date
0d51a69 Dave Tang 2025-03-10
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
}

pbmc3k_v5 <- seurat_wf_v5(pbmc3k)
pbmc3k_v5
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

UMAP.

DimPlot(pbmc3k_v5, reduction = "umap")

Version Author Date
0d51a69 Dave Tang 2025-03-10

Data layer

Version 4 store log normalised data.

colSums(pbmc3k_v4@assays$RNA$data)[1:6]
AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1 
        1605.823         2027.859         2040.169         1902.960 
AAACCGTGTATGCG-1 AAACGCACTGGTAC-1 
        1388.125         1653.061 

The data layer is in the SCT assay.

colSums(pbmc3k_v5@assays$SCT$data)[1:6]
AAACATACAACCAC-1 AAACATTGAGCTAC-1 AAACATTGATCAGC-1 AAACCGTGCTTCCG-1 
        786.2686        1024.4731        1029.3032         934.4454 
AAACCGTGTATGCG-1 AAACGCACTGGTAC-1 
        666.1142         764.8101 

Compare clustering

More granular clustering of version 4’s cluster 0 in version 5.

stopifnot(all(row.names(pbmc3k_v4@meta.data) == row.names(pbmc3k_v5@meta.data)))

table(
  pbmc3k_v4@meta.data$seurat_clusters,
  pbmc3k_v5@meta.data$seurat_clusters
)
   
      0   1   2   3   4   5   6   7   8   9
  0 970   0  71   2   0   0 100  44   0   0
  1   0 479   0   0   0   9   0   0   3   0
  2   1   0   0 349   0   0   0   1   0   0
  3   4   0 290   1   5   0   0   1   0   0
  4   0   0   5   6 152   0   0   0   0   0
  5   0  16   0   0   0 145   0   0   0   0
  6   0   1   0   0   0   0   0   0  31   0
  7   0   1   0   1   0   0   0   0   0  12

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_1.9.4   
 [5] forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4        purrr_1.0.4       
 [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_1.9.1              magrittr_2.0.3             
  [5] spatstat.utils_3.1-2        farver_2.1.2               
  [7] rmarkdown_2.29              zlibbioc_1.52.0            
  [9] fs_1.6.5                    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_1.42.0           KernSmooth_2.23-24         
 [21] bslib_0.9.0                 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_2.6.1                   
 [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_1.9.0                    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_0.15.1           
 [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] MASS_7.3-60.2               DelayedArray_0.32.0        
 [63] tools_4.4.1                 lmtest_0.9-40              
 [65] httpuv_1.6.15               future.apply_1.11.3        
 [67] goftest_1.2-3               glmGamPoi_1.18.0           
 [69] glue_1.8.0                  callr_3.7.6                
 [71] nlme_3.1-164                promises_1.3.2             
 [73] grid_4.4.1                  Rtsne_0.17                 
 [75] getPass_0.2-4               cluster_2.1.6              
 [77] reshape2_1.4.4              generics_0.1.3             
 [79] gtable_0.3.6                spatstat.data_3.1-4        
 [81] tzdb_0.4.0                  data.table_1.17.0          
 [83] hms_1.1.3                   XVector_0.46.0             
 [85] BiocGenerics_0.52.0         spatstat.geom_3.3-5        
 [87] RcppAnnoy_0.0.22            ggrepel_0.9.6              
 [89] RANN_2.6.2                  pillar_1.10.1              
 [91] spam_2.11-1                 RcppHNSW_0.6.0             
 [93] later_1.4.1                 splines_4.4.1              
 [95] lattice_0.22-6              survival_3.6-4             
 [97] deldir_2.0-4                tidyselect_1.2.1           
 [99] miniUI_0.1.1.1              pbapply_1.7-2              
[101] knitr_1.49                  git2r_0.35.0               
[103] gridExtra_2.3               IRanges_2.40.1             
[105] SummarizedExperiment_1.36.0 scattermore_1.2            
[107] stats4_4.4.1                xfun_0.51                  
[109] Biobase_2.66.0              matrixStats_1.5.0          
[111] UCSC.utils_1.2.0            stringi_1.8.4              
[113] lazyeval_0.2.2              yaml_2.3.10                
[115] evaluate_1.0.3              codetools_0.2-20           
[117] cli_3.6.4                   uwot_0.2.3                 
[119] xtable_1.8-4                reticulate_1.41.0          
[121] munsell_0.5.1               processx_3.8.6             
[123] jquerylib_0.1.4             GenomeInfoDb_1.42.3        
[125] Rcpp_1.0.14                 globals_0.16.3             
[127] spatstat.random_3.3-2       png_0.1-8                  
[129] spatstat.univar_3.1-2       parallel_4.4.1             
[131] dotCall64_1.2               sparseMatrixStats_1.18.0   
[133] listenv_0.9.1               viridisLite_0.4.2          
[135] scales_1.3.0                ggridges_0.5.6             
[137] crayon_1.5.3                rlang_1.1.5                
[139] cowplot_1.1.3