Interoperability between single-cell object formats

Interoperability between single-cell object formats
Compiled: 2021-08-30
Source: vignettes/conversion_vignette.Rmd
In this vignette, we demonstrate the ability to convert between Seurat objects, SingleCellExperiment objects, and anndata objects.

install scater https://bioconductor.org/packages/release/bioc/html/scater.html

library(scater)
library(Seurat)

install SeuratDisk from GitHub using the remotes package remotes::install_github(repo =

‘mojaveazure/seurat-disk’, ref = ‘develop’)

library(SeuratDisk)
library(SeuratData)
library(patchwork)
Converting to/from SingleCellExperiment
SingleCellExperiment is a class for storing single-cell experiment data, created by Davide Risso, Aaron Lun, and Keegan Korthauer, and is used by many Bioconductor analysis packages. Here we demonstrate converting the Seurat object produced in our 3k PBMC tutorial to SingleCellExperiment for use with Davis McCarthy’s scater package.

Use PBMC3K from SeuratData

InstallData(“pbmc3k”)
pbmc <- LoadData(ds = “pbmc3k”, type = “pbmc3k.final”)
pbmc.sce <- as.SingleCellExperiment(pbmc)
p1 <- plotExpression(pbmc.sce, features = “MS4A1”, x = “ident”) + theme(axis.text.x = element_text(angle = 45,
hjust = 1))
p2 <- plotPCA(pbmc.sce, colour_by = “ident”)
p1 + p2

Seurat also allows conversion from SingleCellExperiment objects to Seurat objects; we demonstrate this on some publicly available data downloaded from a repository maintained by Martin Hemberg’s group.

download from hemberg lab

https://scrnaseq-public-datasets.s3.amazonaws.com/scater-objects/manno_human.rds

manno <- readRDS(file = “…/data/manno_human.rds”)
manno <- runPCA(manno)
manno.seurat <- as.Seurat(manno, counts = “counts”, data = “logcounts”)

gives the same results; but omits defaults provided in the last line

manno.seurat <- as.Seurat(manno)
Idents(manno.seurat) <- “cell_type1”
p1 <- DimPlot(manno.seurat, reduction = “PCA”, group.by = “Source”) + NoLegend()
p2 <- RidgePlot(manno.seurat, features = “ACTB”, group.by = “Source”)
p1 + p2

Converting to/from loom
The loom format is a file structure imposed on HDF5 files designed by Sten Linnarsson’s group. It is designed to efficiently hold large single-cell genomics datasets. The ability to save Seurat objects as loom files is implemented in SeuratDisk For more details about the loom format, please see the loom file format specification.

pbmc.loom <- as.loom(pbmc, filename = “…/output/pbmc3k.loom”, verbose = FALSE)
pbmc.loom

Class: loom

Filename: /__w/2/s/output/pbmc3k.loom

Access type: H5F_ACC_RDWR

Listing:

name obj_type dataset.dims dataset.type_class

attrs H5I_GROUP

col_attrs H5I_GROUP

col_graphs H5I_GROUP

layers H5I_GROUP

matrix H5I_DATASET 2638 x 13714 H5T_FLOAT

row_attrs H5I_GROUP

row_graphs H5I_GROUP

Always remember to close loom files when done

pbmc.loom$close_all()
Seurat can also read in loom files connected via SeuratDisk into a Seurat object; we demonstrate this on a subset of the Mouse Brain Atlas created by the Linnarsson lab.

download from linnarsson lab

https://storage.googleapis.com/linnarsson-lab-loom/l6_r1_immune_cells.loom

l6.immune <- Connect(filename = “…/data/l6_r1_immune_cells.loom”, mode = “r”)
l6.immune

Class: loom

Filename: /__w/2/s/data/l6_r1_immune_cells.loom

Access type: H5F_ACC_RDONLY

Attributes: CreationDate, last_modified

Listing:

name obj_type dataset.dims dataset.type_class

col_attrs H5I_GROUP

col_graphs H5I_GROUP

layers H5I_GROUP

matrix H5I_DATASET 14908 x 27998 H5T_FLOAT

row_attrs H5I_GROUP

row_graphs H5I_GROUP

l6.seurat <- as.Seurat(l6.immune)
Idents(l6.seurat) <- “ClusterName”
VlnPlot(l6.seurat, features = c(“Sparc”, “Ftl1”, “Junb”, “Ccl4”), ncol = 2)

Always remember to close loom files when done

l6.immune$close_all()
For more details about interacting with loom files in R and Seurat, please see loomR on GitHub.

Converting to/from AnnData
AnnData provides a Python class, created by Alex Wolf and Philipp Angerer, that can be used to store single-cell data. This data format is also use for storage in their Scanpy package for which we now support interoperability. Support for reading data from and saving data to AnnData files is provided by SeuratDisk; please see their vignette showcasing the interoperability.

Acknowledgments
Many thanks to Davis McCarthy and Alex Wolf for their help in drafting the conversion functions.

Session Info
sessionInfo()

R version 4.1.0 (2021-05-18)

Platform: x86_64-pc-linux-gnu (64-bit)

Running under: Ubuntu 20.04.2 LTS

Matrix products: default

BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

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=C

[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

attached base packages:

[1] parallel stats4 stats graphics grDevices utils datasets

[8] methods base

other attached packages:

[1] patchwork_1.1.1 thp1.eccite.SeuratData_3.1.5

[3] stxBrain.SeuratData_0.1.1 ssHippo.SeuratData_3.1.4

[5] pbmcsca.SeuratData_3.0.0 pbmcMultiome.SeuratData_0.1.1

[7] pbmc3k.SeuratData_3.1.4 panc8.SeuratData_3.0.2

[9] ifnb.SeuratData_3.1.0 hcabm40k.SeuratData_3.0.0

[11] bmcite.SeuratData_0.3.0 SeuratData_0.2.1

[13] SeuratDisk_0.0.0.9019 SeuratObject_4.0.2

[15] Seurat_4.0.4 scater_1.20.1

[17] ggplot2_3.3.5 scuttle_1.2.1

[19] SingleCellExperiment_1.14.1 SummarizedExperiment_1.22.0

[21] Biobase_2.52.0 GenomicRanges_1.44.0

[23] GenomeInfoDb_1.28.1 IRanges_2.26.0

[25] S4Vectors_0.30.0 BiocGenerics_0.38.0

[27] MatrixGenerics_1.4.2 matrixStats_0.60.0

loaded via a namespace (and not attached):

[1] utf8_1.2.2 reticulate_1.20

[3] tidyselect_1.1.1 htmlwidgets_1.5.3

[5] grid_4.1.0 BiocParallel_1.26.1

[7] Rtsne_0.15 munsell_0.5.0

[9] ScaledMatrix_1.0.0 codetools_0.2-18

[11] ragg_1.1.3 ica_1.0-2

[13] future_1.21.0 miniUI_0.1.1.1

[15] withr_2.4.2 colorspace_2.0-2

[17] highr_0.9 knitr_1.33

[19] ROCR_1.0-11 tensor_1.5

[21] listenv_0.8.0 labeling_0.4.2

[23] GenomeInfoDbData_1.2.6 polyclip_1.10-0

[25] farver_2.1.0 bit64_4.0.5

[27] rprojroot_2.0.2 parallelly_1.26.0

[29] vctrs_0.3.8 generics_0.1.0

[31] xfun_0.25 R6_2.5.0

[33] ggbeeswarm_0.6.0 rsvd_1.0.5

[35] hdf5r_1.3.3 bitops_1.0-7

[37] spatstat.utils_2.1-0 cachem_1.0.5

[39] DelayedArray_0.18.0 assertthat_0.2.1

[41] promises_1.2.0.1 scales_1.1.1

[43] beeswarm_0.4.0 gtable_0.3.0

[45] beachmat_2.8.0 globals_0.14.0

[47] goftest_1.2-2 rlang_0.4.11

[49] systemfonts_1.0.2 splines_4.1.0

[51] lazyeval_0.2.2 spatstat.geom_2.1-0

[53] yaml_2.2.1 reshape2_1.4.4

[55] abind_1.4-5 httpuv_1.6.1

[57] tools_4.1.0 ellipsis_0.3.2

[59] spatstat.core_2.1-2 jquerylib_0.1.4

[61] RColorBrewer_1.1-2 ggridges_0.5.3

[63] Rcpp_1.0.7 plyr_1.8.6

[65] sparseMatrixStats_1.4.2 zlibbioc_1.38.0

[67] purrr_0.3.4 RCurl_1.98-1.3

[69] rpart_4.1-15 deldir_0.2-10

[71] pbapply_1.4-3 viridis_0.6.1

[73] cowplot_1.1.1 zoo_1.8-9

[75] ggrepel_0.9.1 cluster_2.1.2

[77] fs_1.5.0 magrittr_2.0.1

[79] data.table_1.14.0 scattermore_0.7

[81] lmtest_0.9-38 RANN_2.6.1

[83] fitdistrplus_1.1-5 mime_0.11

[85] evaluate_0.14 xtable_1.8-4

[87] gridExtra_2.3 compiler_4.1.0

[89] tibble_3.1.3 KernSmooth_2.23-20

[91] crayon_1.4.1 htmltools_0.5.1.1

[93] mgcv_1.8-35 later_1.2.0

[95] tidyr_1.1.3 DBI_1.1.1

[97] formatR_1.11 MASS_7.3-54

[99] rappdirs_0.3.3 Matrix_1.3-3

[101] cli_3.0.1 igraph_1.2.6

[103] pkgconfig_2.0.3 pkgdown_1.6.1

[105] plotly_4.9.4 spatstat.sparse_2.0-0

[107] vipor_0.4.5 bslib_0.2.5.1

[109] XVector_0.32.0 stringr_1.4.0

[111] digest_0.6.27 sctransform_0.3.2

[113] RcppAnnoy_0.0.18 spatstat.data_2.1-0

[115] rmarkdown_2.10 leiden_0.3.8

[117] uwot_0.1.10 DelayedMatrixStats_1.14.2

[119] shiny_1.6.0 lifecycle_1.0.0

[121] nlme_3.1-152 jsonlite_1.7.2

[123] BiocNeighbors_1.10.0 desc_1.3.0

[125] viridisLite_0.4.0 fansi_0.5.0

[127] pillar_1.6.2 lattice_0.20-44

[129] fastmap_1.1.0 httr_1.4.2

[131] survival_3.2-11 glue_1.4.2

[133] png_0.1-7 bit_4.0.4

[135] stringi_1.7.3 sass_0.4.0

[137] textshaping_0.3.5 BiocSingular_1.8.1

[139] memoise_2.0.0 dplyr_1.0.7

[141] irlba_2.3.3 future.apply_1.7.0

Contents
Converting to/from SingleCellExperiment
Converting to/from loom
Converting to/from AnnData
Acknowledgments
Developed by Paul Hoffman, Satija Lab and Collaborators.

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