Saving Seurat objects
2025-04-15
Last updated: 2025-04-15
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Knit directory: muse/
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| File | Version | Author | Date | Message |
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| Rmd | deec653 | Dave Tang | 2025-04-15 | Saving Seurat objects |
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"))
pbmc3kAn object of class Seurat
32738 features across 2700 samples within 1 assay
Active assay: RNA (32738 features, 0 variable features)
1 layer present: countsUse BPCells
Sparse matrix.
class(pbmc3k@assays$RNA$counts)[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"Write a matrix directory, load the matrix, and create a Seurat object.
my_outdir <- "data/pbmc3k_bpcells_mat"
if(!dir.exists(my_outdir)){
BPCells::write_matrix_dir(
mat = pbmc3k@assays$RNA$counts,
dir = my_outdir
)
}Warning: Matrix compression performs poorly with non-integers.
• Consider calling convert_matrix_type if a compressed integer matrix is intended.
This message is displayed once every 8 hours.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# Now that we have the matrix on disk, we can load it
pbmc3k.mat <- open_matrix_dir(dir = my_outdir)
pbmc3k.mat32738 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 Seurat Object
pbmc3k_bpcells <- CreateSeuratObject(
counts = pbmc3k.mat,
project = 'pbmc3k',
min.cells = 3,
min.features = 200
)
pbmc3k_bpcells@assays$RNA$counts13714 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 dimnamesSeurat version 4
Seurat workflow.
debug_flag <- FALSE
start_time <- Sys.time()
pbmc3k_bpcells <- NormalizeData(pbmc3k_bpcells, normalization.method = "LogNormalize")Normalizing layer: countspbmc3k_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 sessionpbmc3k_bpcells <- FindNeighbors(pbmc3k_bpcells, dims = 1:30, verbose = debug_flag)
pbmc3k_bpcells <- FindClusters(pbmc3k_bpcells, resolution = 0.5, verbose = debug_flag)
pbmc3k_bpcellsAn 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, umapend_time <- Sys.time()
end_time - start_timeTime difference of 11.39049 secsCounts.
pbmc3k_bpcells@assays$RNA$counts13714 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 dimnamesData.
pbmc3k_bpcells@assays$RNA$data13714 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
4. Scale by 1e+04
5. Scale columns by 0.000413, 0.000204 ... 0.000504
6. Transform log1p
7. Reset dimnamesScale data.
pbmc3k_bpcells@assays$RNA$scale.data2000 x 2700 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: 513, 139 ... 1247 and cols: all
8. Reset dimnames
9. Transform min by row: 5.2, 19.4 ... 5.41
10. Scale rows by 1.95, 0.55 ... 1.91
11. Shift rows by -0.143, -0.645 ... -0.33
12. Select rows: 640, 1274 ... 456 and cols: all
13. Reset dimnamesPlots
DimPlot(pbmc3k_bpcells)
Exporting
Save.
saveRDS(object = pbmc3k_bpcells, file = "pbmc3k_save_rds.rds")Load.
pbmc3k_read_rds <- readRDS("pbmc3k_save_rds.rds")
pbmc3k_read_rds@assays$RNA$counts13714 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 dimnamespbmc3k_read_rds@assays$RNA$data13714 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
4. Scale by 1e+04
5. Scale columns by 0.000413, 0.000204 ... 0.000504
6. Transform log1p
7. Reset dimnamespbmc3k_read_rds@assays$RNA$scale.data2000 x 2700 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: 513, 139 ... 1247 and cols: all
8. Reset dimnames
9. Transform min by row: 5.2, 19.4 ... 5.41
10. Scale rows by 1.95, 0.55 ... 1.91
11. Shift rows by -0.143, -0.645 ... -0.33
12. Select rows: 640, 1274 ... 456 and cols: all
13. Reset dimnamespbmc3k_read_rdsAn 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
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 spatstat.utils_3.1-2 farver_2.1.2
[7] rmarkdown_2.28 fs_1.6.4 vctrs_0.6.5
[10] ROCR_1.0-11 spatstat.explore_3.3-4 htmltools_0.5.8.1
[13] sass_0.4.9 sctransform_0.4.1 parallelly_1.38.0
[16] KernSmooth_2.23-24 bslib_0.8.0 htmlwidgets_1.6.4
[19] ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[22] zoo_1.8-13 cachem_1.1.0 whisker_0.4.1
[25] igraph_2.1.4 mime_0.12 lifecycle_1.0.4
[28] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.5.1
[31] fastmap_1.2.0 MatrixGenerics_1.18.1 fitdistrplus_1.2-2
[34] future_1.34.0 shiny_1.10.0 digest_0.6.37
[37] colorspace_2.1-1 patchwork_1.3.0 ps_1.8.1
[40] rprojroot_2.0.4 tensor_1.5 RSpectra_0.16-2
[43] irlba_2.3.5.1 labeling_0.4.3 progressr_0.15.0
[46] spatstat.sparse_3.1-0 httr_1.4.7 polyclip_1.10-7
[49] abind_1.4-8 compiler_4.4.1 withr_3.0.2
[52] fastDummies_1.7.5 highr_0.11 MASS_7.3-60.2
[55] tools_4.4.1 lmtest_0.9-40 httpuv_1.6.15
[58] future.apply_1.11.3 goftest_1.2-3 glue_1.8.0
[61] callr_3.7.6 nlme_3.1-164 promises_1.3.2
[64] grid_4.4.1 Rtsne_0.17 getPass_0.2-4
[67] cluster_2.1.6 reshape2_1.4.4 generics_0.1.3
[70] gtable_0.3.6 spatstat.data_3.1-4 tidyr_1.3.1
[73] data.table_1.16.2 spatstat.geom_3.3-5 RcppAnnoy_0.0.22
[76] ggrepel_0.9.6 RANN_2.6.2 pillar_1.10.1
[79] stringr_1.5.1 spam_2.11-1 RcppHNSW_0.6.0
[82] later_1.3.2 splines_4.4.1 dplyr_1.1.4
[85] lattice_0.22-6 survival_3.6-4 deldir_2.0-4
[88] tidyselect_1.2.1 miniUI_0.1.1.1 pbapply_1.7-2
[91] knitr_1.48 git2r_0.35.0 gridExtra_2.3
[94] scattermore_1.2 xfun_0.48 matrixStats_1.5.0
[97] stringi_1.8.4 lazyeval_0.2.2 yaml_2.3.10
[100] evaluate_1.0.1 codetools_0.2-20 tibble_3.2.1
[103] cli_3.6.3 uwot_0.2.3 xtable_1.8-4
[106] reticulate_1.41.0 munsell_0.5.1 processx_3.8.4
[109] jquerylib_0.1.4 Rcpp_1.0.13 globals_0.16.3
[112] spatstat.random_3.3-2 png_0.1-8 spatstat.univar_3.1-2
[115] parallel_4.4.1 ggplot2_3.5.1 dotCall64_1.2
[118] listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0
[121] ggridges_0.5.6 purrr_1.0.2 rlang_1.1.4
[124] cowplot_1.1.3