Saving Seurat objects

workflowr

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Last updated: 2025-04-16

Checks: 7 0

Knit directory: muse/

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---

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Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    data/1M_neurons_filtered_gene_bc_matrices_h5.h5
    Ignored:    data/293t/
    Ignored:    data/293t_3t3_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/293t_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/97516b79-8d08-46a6-b329-5d0a25b0be98.h5ad
    Ignored:    data/Parent_SC3v3_Human_Glioblastoma_filtered_feature_bc_matrix.tar.gz
    Ignored:    data/brain_counts/
    Ignored:    data/cl.obo
    Ignored:    data/cl.owl
    Ignored:    data/jurkat/
    Ignored:    data/jurkat:293t_50:50_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/jurkat_293t/
    Ignored:    data/jurkat_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/pbmc20k/
    Ignored:    data/pbmc20k_seurat/
    Ignored:    data/pbmc3k/
    Ignored:    data/pbmc3k_bpcells_mat/
    Ignored:    data/pbmc3k_seurat.rds
    Ignored:    data/pbmc4k_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/pbmc_1k_v3_filtered_feature_bc_matrix.h5
    Ignored:    data/pbmc_1k_v3_raw_feature_bc_matrix.h5
    Ignored:    data/refdata-gex-GRCh38-2020-A.tar.gz
    Ignored:    data/seurat_1m_neuron.rds
    Ignored:    data/t_3k_filtered_gene_bc_matrices.tar.gz
    Ignored:    r_packages_4.4.1/

Untracked files:
    Untracked:  analysis/bioc_scrnaseq.Rmd
    Untracked:  pbmc3k_before_filtering.rds
    Untracked:  pbmc3k_save_rds.rds
    Untracked:  rsem.merged.gene_counts.tsv

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/seurat_save.Rmd) and HTML (docs/seurat_save.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	41cf543	Dave Tang	2025-04-16	Save as separate RDS instead of additional assay when using BPCells
html	1de8ba4	Dave Tang	2025-04-16	Build site.
Rmd	445cb01	Dave Tang	2025-04-16	Save additional RDS
html	68b9532	Dave Tang	2025-04-16	Build site.
Rmd	e923354	Dave Tang	2025-04-16	Assay classes
html	24107a2	Dave Tang	2025-04-16	Build site.
Rmd	5dd72fd	Dave Tang	2025-04-16	Save additional assay
html	a22d5e1	Dave Tang	2025-04-15	Build site.
Rmd	77f2810	Dave Tang	2025-04-15	Add miscellaneous data
html	478564c	Dave Tang	2025-04-15	Build site.
Rmd	deec653	Dave Tang	2025-04-15	Saving Seurat objects

---

if ("BPCells" %in% row.names(installed.packages()) == FALSE){
  remotes::install_github("bnprks/BPCells/r")
}

suppressPackageStartupMessages(library(BPCells))
suppressPackageStartupMessages(library(Seurat))

Load Data

Load from my server.

pbmc3k <- readRDS(url("https://davetang.org/file/pbmc3k_seurat.rds", "rb"))
pbmc3k

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

Use BPCells

Sparse matrix.

class(pbmc3k@assays$RNA$counts)

[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"

Write a matrix directory and load the matrix using {BPCells}.

my_outdir <- "data/pbmc3k_bpcells_mat"
if(!dir.exists(my_outdir)){
  BPCells::write_matrix_dir(
    mat = pbmc3k@assays$RNA$counts,
    dir = my_outdir
  )
}

# Now that we have the matrix on disk, we can load it
pbmc3k.mat <- open_matrix_dir(dir = my_outdir)
pbmc3k.mat

32738 x 2700 IterableMatrix object with class MatrixDir

Row names: MIR1302-10, FAM138A ... AC002321.1
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat

Create a Seurat object.

pbmc3k_bpcells <- CreateSeuratObject(
  counts = pbmc3k.mat,
  project = 'pbmc3k',
  min.cells = 3,
  min.features = 200
)

pbmc3k_bpcells@assays$RNA$counts

13714 x 2700 IterableMatrix object with class RenameDims

Row names: AL627309.1, AP006222.2 ... SRSF10.1
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat
2. Select rows: 6, 9 ... 32733 and cols: 1, 2 ... 2700
3. Reset dimnames

Seurat version 4

Mitochondrial percent.

mito.genes <- grep(pattern = "^MT-", x = rownames(x = pbmc3k_bpcells@assays$RNA), ignore.case = TRUE, value = TRUE)

pbmc3k_bpcells[["percent.mt"]] <- PercentageFeatureSet(pbmc3k_bpcells, features = mito.genes)
VlnPlot(pbmc3k_bpcells, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3, layer = "counts")

Past versions of mito_genes-1.png

Version	Author	Date
24107a2	Dave Tang	2025-04-16

Save original data into RAW assay before filtering.

pbmc3k_bpcells[["RAW"]] <- pbmc3k_bpcells@assays$RNA

Warning: Key 'rna_' taken, using 'raw_' instead

Seurat::Assays(pbmc3k_bpcells)

[1] "RNA" "RAW"

Seurat::DefaultAssay(pbmc3k_bpcells)

[1] "RNA"

Save separate RDS file.

saveRDS(object = pbmc3k_bpcells, file = "pbmc3k_before_filtering.rds")

Filter.

pbmc3k_bpcells <- subset(pbmc3k_bpcells, subset = percent.mt < 15)
pbmc3k_bpcells

An object of class Seurat 
27428 features across 2698 samples within 2 assays 
Active assay: RNA (13714 features, 0 variable features)
 1 layer present: counts
 1 other assay present: RAW

Unfortunately, the RAW assay becomes filtered as well.

dim(pbmc3k_bpcells@assays$RNA$counts)

[1] 13714  2698

dim(pbmc3k_bpcells@assays$RAW$counts)

[1] 13714  2698

Seurat workflow.

debug_flag <- FALSE
start_time <- Sys.time()

pbmc3k_bpcells <- NormalizeData(pbmc3k_bpcells, normalization.method = "LogNormalize")

Normalizing layer: counts

pbmc3k_bpcells <- FindVariableFeatures(pbmc3k_bpcells, selection.method = 'vst', nfeatures = 2000, verbose = debug_flag)
pbmc3k_bpcells <- ScaleData(pbmc3k_bpcells, verbose = debug_flag)
pbmc3k_bpcells <- RunPCA(pbmc3k_bpcells, verbose = debug_flag)
pbmc3k_bpcells <- RunUMAP(pbmc3k_bpcells, dims = 1:30, verbose = debug_flag)

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_bpcells <- FindNeighbors(pbmc3k_bpcells, dims = 1:30, verbose = debug_flag)
pbmc3k_bpcells <- FindClusters(pbmc3k_bpcells, resolution = 0.5, verbose = debug_flag)
pbmc3k_bpcells

An object of class Seurat 
27428 features across 2698 samples within 2 assays 
Active assay: RNA (13714 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: RAW
 2 dimensional reductions calculated: pca, umap

end_time <- Sys.time()
end_time - start_time

Time difference of 11.79401 secs

Counts.

pbmc3k_bpcells@assays$RNA$counts

13714 x 2698 IterableMatrix object with class RenameDims

Row names: AL627309.1, AP006222.2 ... SRSF10.1
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat
2. Select rows: 6, 9 ... 32733 and cols: 1, 2 ... 2700
3. Reset dimnames

Data.

pbmc3k_bpcells@assays$RNA$data

13714 x 2698 IterableMatrix object with class RenameDims

Row names: AL627309.1, AP006222.2 ... SRSF10.1
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat
2. Select rows: 6, 9 ... 32733 and cols: 1, 2 ... 2700
3. Reset dimnames
4. Scale by 1e+04
5. Scale columns by 0.000413, 0.000204 ... 0.000504
6. Transform log1p
7. Reset dimnames

Scale data.

pbmc3k_bpcells@assays$RNA$scale.data

2000 x 2698 IterableMatrix object with class RenameDims

Row names: ISG15, CPSF3L ... MT-ND6
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat
2. Select rows: 43, 63 ... 32708 and cols: 1, 2 ... 2700
3. Reset dimnames
4. Scale by 1e+04
5. Scale columns by 0.000413, 0.000204 ... 0.000504
6. Transform log1p
7. Select rows: 514, 139 ... 580 and cols: all
8. Reset dimnames
9. Transform min by row: 5.2, 19.4 ... 2.3
10. Scale rows by 1.95, 0.55 ... 4.4
11. Shift rows by -0.143, -0.645 ... -0.132
12. Select rows: 636, 1273 ... 455 and cols: all
13. Reset dimnames

Plots

DimPlot(pbmc3k_bpcells)

Past versions of dim_plot-1.png

Version	Author	Date
24107a2	Dave Tang	2025-04-16
478564c	Dave Tang	2025-04-15

Add miscellaneous data

Get and set miscellaneous data.

Misc(pbmc3k_bpcells)

list()

Set and output.

Misc(pbmc3k_bpcells, slot = "seed") <- 1984
Misc(pbmc3k_bpcells, slot = "author") <- "Davo"
Misc(pbmc3k_bpcells)

$seed
[1] 1984

$author
[1] "Davo"

Get specific slot.

Misc(pbmc3k_bpcells, slot = "author")

[1] "Davo"

Exporting

Save.

saveRDS(object = pbmc3k_bpcells, file = "pbmc3k_save_rds.rds")

Load.

pbmc3k_read_rds <- readRDS("pbmc3k_save_rds.rds")
pbmc3k_read_rds@assays$RNA$counts

13714 x 2698 IterableMatrix object with class RenameDims

Row names: AL627309.1, AP006222.2 ... SRSF10.1
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat
2. Select rows: 6, 9 ... 32733 and cols: 1, 2 ... 2700
3. Reset dimnames

pbmc3k_read_rds@assays$RNA$data

13714 x 2698 IterableMatrix object with class RenameDims

Row names: AL627309.1, AP006222.2 ... SRSF10.1
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat
2. Select rows: 6, 9 ... 32733 and cols: 1, 2 ... 2700
3. Reset dimnames
4. Scale by 1e+04
5. Scale columns by 0.000413, 0.000204 ... 0.000504
6. Transform log1p
7. Reset dimnames

pbmc3k_read_rds@assays$RNA$scale.data

2000 x 2698 IterableMatrix object with class RenameDims

Row names: ISG15, CPSF3L ... MT-ND6
Col names: AAACATACAACCAC-1, AAACATTGAGCTAC-1 ... TTTGCATGCCTCAC-1

Data type: double
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc3k_bpcells_mat
2. Select rows: 43, 63 ... 32708 and cols: 1, 2 ... 2700
3. Reset dimnames
4. Scale by 1e+04
5. Scale columns by 0.000413, 0.000204 ... 0.000504
6. Transform log1p
7. Select rows: 514, 139 ... 580 and cols: all
8. Reset dimnames
9. Transform min by row: 5.2, 19.4 ... 2.3
10. Scale rows by 1.95, 0.55 ... 4.4
11. Shift rows by -0.143, -0.645 ... -0.132
12. Select rows: 636, 1273 ... 455 and cols: all
13. Reset dimnames

pbmc3k_read_rds

An object of class Seurat 
27428 features across 2698 samples within 2 assays 
Active assay: RNA (13714 features, 2000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: RAW
 2 dimensional reductions calculated: pca, umap

Get miscellaneous data.

Misc(pbmc3k_read_rds)

$seed
[1] 1984

$author
[1] "Davo"

Check RAW assay.

class(pbmc3k_read_rds@assays$RAW$counts)

[1] "RenameDims"
attr(,"package")
[1] "BPCells"

class(pbmc3k_read_rds@assays$RNA$counts)

[1] "RenameDims"
attr(,"package")
[1] "BPCells"

dim(pbmc3k_read_rds@assays$RAW$counts)

[1] 13714  2698

dim(pbmc3k_read_rds@assays$RNA$counts)

[1] 13714  2698

Load RDS that was saved before filtering.

pbmc3k_before_filtering <- readRDS("pbmc3k_before_filtering.rds")
class(pbmc3k_before_filtering@assays$RAW$counts)

[1] "RenameDims"
attr(,"package")
[1] "BPCells"

class(pbmc3k_before_filtering@assays$RNA$counts)

[1] "RenameDims"
attr(,"package")
[1] "BPCells"

dim(pbmc3k_before_filtering@assays$RAW$counts)

[1] 13714  2700

dim(pbmc3k_before_filtering@assays$RNA$counts)

[1] 13714  2700

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           BPCells_0.3.0     
[5] workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3      rstudioapi_0.17.1       jsonlite_1.8.9         
  [4] magrittr_2.0.3          ggbeeswarm_0.7.2        spatstat.utils_3.1-2   
  [7] farver_2.1.2            rmarkdown_2.28          fs_1.6.4               
 [10] zlibbioc_1.52.0         vctrs_0.6.5             ROCR_1.0-11            
 [13] spatstat.explore_3.3-4  htmltools_0.5.8.1       sass_0.4.9             
 [16] sctransform_0.4.1       parallelly_1.38.0       KernSmooth_2.23-24     
 [19] bslib_0.8.0             htmlwidgets_1.6.4       ica_1.0-3              
 [22] plyr_1.8.9              plotly_4.10.4           zoo_1.8-13             
 [25] cachem_1.1.0            whisker_0.4.1           igraph_2.1.4           
 [28] mime_0.12               lifecycle_1.0.4         pkgconfig_2.0.3        
 [31] Matrix_1.7-0            R6_2.5.1                fastmap_1.2.0          
 [34] GenomeInfoDbData_1.2.13 MatrixGenerics_1.18.1   fitdistrplus_1.2-2     
 [37] future_1.34.0           shiny_1.10.0            digest_0.6.37          
 [40] colorspace_2.1-1        patchwork_1.3.0         S4Vectors_0.44.0       
 [43] ps_1.8.1                rprojroot_2.0.4         tensor_1.5             
 [46] RSpectra_0.16-2         irlba_2.3.5.1           GenomicRanges_1.58.0   
 [49] labeling_0.4.3          progressr_0.15.0        spatstat.sparse_3.1-0  
 [52] polyclip_1.10-7         httr_1.4.7              abind_1.4-8            
 [55] compiler_4.4.1          withr_3.0.2             fastDummies_1.7.5      
 [58] highr_0.11              MASS_7.3-60.2           tools_4.4.1            
 [61] vipor_0.4.7             lmtest_0.9-40           beeswarm_0.4.0         
 [64] httpuv_1.6.15           future.apply_1.11.3     goftest_1.2-3          
 [67] glue_1.8.0              callr_3.7.6             nlme_3.1-164           
 [70] promises_1.3.2          grid_4.4.1              Rtsne_0.17             
 [73] getPass_0.2-4           cluster_2.1.6           reshape2_1.4.4         
 [76] generics_0.1.3          gtable_0.3.6            spatstat.data_3.1-4    
 [79] tidyr_1.3.1             data.table_1.16.2       XVector_0.46.0         
 [82] BiocGenerics_0.52.0     spatstat.geom_3.3-5     RcppAnnoy_0.0.22       
 [85] ggrepel_0.9.6           RANN_2.6.2              pillar_1.10.1          
 [88] stringr_1.5.1           spam_2.11-1             RcppHNSW_0.6.0         
 [91] later_1.3.2             splines_4.4.1           dplyr_1.1.4            
 [94] lattice_0.22-6          deldir_2.0-4            survival_3.6-4         
 [97] tidyselect_1.2.1        miniUI_0.1.1.1          pbapply_1.7-2          
[100] knitr_1.48              git2r_0.35.0            gridExtra_2.3          
[103] IRanges_2.40.1          scattermore_1.2         stats4_4.4.1           
[106] xfun_0.48               matrixStats_1.5.0       stringi_1.8.4          
[109] UCSC.utils_1.2.0        lazyeval_0.2.2          yaml_2.3.10            
[112] evaluate_1.0.1          codetools_0.2-20        tibble_3.2.1           
[115] cli_3.6.3               uwot_0.2.3              xtable_1.8-4           
[118] reticulate_1.41.0       munsell_0.5.1           processx_3.8.4         
[121] jquerylib_0.1.4         Rcpp_1.0.13             GenomeInfoDb_1.42.3    
[124] spatstat.random_3.3-2   globals_0.16.3          png_0.1-8              
[127] ggrastr_1.0.2           spatstat.univar_3.1-2   parallel_4.4.1         
[130] ggplot2_3.5.1           dotCall64_1.2           listenv_0.9.1          
[133] viridisLite_0.4.2       scales_1.3.0            ggridges_0.5.6         
[136] purrr_1.0.2             rlang_1.1.4             cowplot_1.1.3