There’s a lot of single-cell omics file/object formats out there, and
not all tools support all of these formats. scKirby aims to make
switching between these formats much easier by running several steps
within a single function: ingest_data(). Alternatively, users can run
any of these steps separately using the designated sub-functions.
- Read: Automatically infers the file/object type and loads it
(sub-function:
read_data()). - Convert: Converts it to the desired file/object type
(sub-function:
to_<format>). - Save: Saves the converted file/object (sub-function:
save_data()).
- SingleCellExperiment
- SummarizedExperiment
- HDF5SummarizedExperiment
- Seurat
- H5Seurat
- anndata
- loom
- loomR
- CellDataSet/monocle
- ExpressionSet
- list
- EWCE
- matrix
- sparseMatrix (dgTMatrix/dgCMatrix)
- DelayedArray
Notes:
- For exporting to additional formats, see these following packages:
- Currently, some (but not all) conversions carry over:
- Multiple assays per experiment.
- Additional objects like dimensionality reduction projections (e.g. PCA, tSNE, UMAP) or graphs (e.g. K-nearest neighbors). This feature will be added in the future.
if(!"remotes" %in% rownames(install.packages())){install.packages("remotes")}
remotes::install_github("bschilder/scKirby")Here are several quick examples of how one can use scKirby. For a
complete list of examples please see the documentation
website.
library(scKirby)scKirby can ingest a named list (i.e. list(exp=..., annot=...)) with
the following items:
-
exp: Expression matrix with rows/genes x cols/cells. Can be a variety of matrix classes, including dense or sparse. -
annot: Cell annotationdata.framewith one cell per row.rownames(annot)should be the same ascolnames(exp).
This happens to be the format that the example data in
EWCE uses, but any
user-supplied data will work.
data("example_EWCElist")
sce <- ingest_data(obj=example_EWCElist)## + Returning object directly...
## Converting formats:
## + 10 core(s) assigned as workers (2 reserved).
## + EWCElist ==> SingleCellExperiment
## [1] "+ Checking SCE rownames."
## + Saving SingleCellExperiment: /tmp/RtmpQGwA8v/scKirby_output.rds
seurat <- ingest_data(obj=example_EWCElist,
output_type = "Seurat")## + Returning object directly...
## Converting formats:
## + 10 core(s) assigned as workers (2 reserved).
## + EWCElist ==> Seurat
## Warning: Feature names cannot have underscores ('_'), replacing with dashes
## ('-')
## + Saving Seurat: /tmp/RtmpQGwA8v/scKirby_output.rds
In-memory
data("example_seurat")
sce <- ingest_data(obj=example_seurat)## + Returning object directly...
## Converting formats:
## + 10 core(s) assigned as workers (2 reserved).
## + Seurat ==> SingleCellExperiment
## [1] "+ Checking SCE rownames."
## + Saving SingleCellExperiment: /tmp/RtmpQGwA8v/scKirby_output.rds
Seurat’s UpdateSeuratObject() can only update objects from the version
immediately previous to the version of Seurat you currently have
installed (e.g. Seurat v2 –> v3). This means you can’t import an
object created in Seurat v1 and directly upgrade it to Seurat v3. We
have provided yaml files when can be used to create separate envs for
each version of Seurat
here.
For more details, see the scKirby conda env tutorial.
utils::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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scKirby_0.1.0
##
## loaded via a namespace (and not attached):
## [1] Seurat_4.0.3 Rtsne_0.15
## [3] colorspace_2.0-2 deldir_0.2-10
## [5] ellipsis_0.3.2 class_7.3-19
## [7] ggridges_0.5.3 XVector_0.33.0
## [9] GenomicRanges_1.45.0 gld_2.6.2
## [11] spatstat.data_2.1-0 rstudioapi_0.13
## [13] proxy_0.4-26 leiden_0.3.9
## [15] listenv_0.8.0 ggrepel_0.9.1
## [17] fansi_0.5.0 mvtnorm_1.1-2
## [19] codetools_0.2-18 splines_4.1.0
## [21] rootSolve_1.8.2.2 knitr_1.33
## [23] polyclip_1.10-0 jsonlite_1.7.2
## [25] ica_1.0-2 cluster_2.1.2
## [27] png_0.1-7 uwot_0.1.10.9000
## [29] spatstat.sparse_2.0-0 sctransform_0.3.2
## [31] shiny_1.6.0 compiler_4.1.0
## [33] httr_1.4.2 lazyeval_0.2.2
## [35] assertthat_0.2.1 SeuratObject_4.0.2
## [37] Matrix_1.3-4 fastmap_1.1.0
## [39] later_1.2.0 htmltools_0.5.1.1
## [41] tools_4.1.0 igraph_1.2.6
## [43] gtable_0.3.0 glue_1.4.2
## [45] lmom_2.8 GenomeInfoDbData_1.2.6
## [47] reshape2_1.4.4 RANN_2.6.1
## [49] dplyr_1.0.7 Rcpp_1.0.7
## [51] scattermore_0.7 Biobase_2.53.0
## [53] vctrs_0.3.8 nlme_3.1-152
## [55] lmtest_0.9-38 xfun_0.25
## [57] stringr_1.4.0 globals_0.14.0
## [59] mime_0.11 miniUI_0.1.1.1
## [61] lifecycle_1.0.0 irlba_2.3.3
## [63] goftest_1.2-2 future_1.21.0
## [65] zlibbioc_1.39.0 MASS_7.3-54
## [67] zoo_1.8-9 scales_1.1.1
## [69] spatstat.core_2.3-0 spatstat.utils_2.2-0
## [71] promises_1.2.0.1 MatrixGenerics_1.5.3
## [73] parallel_4.1.0 SummarizedExperiment_1.23.1
## [75] expm_0.999-6 RColorBrewer_1.1-2
## [77] SingleCellExperiment_1.15.1 yaml_2.2.1
## [79] Exact_2.1 gridExtra_2.3
## [81] pbapply_1.4-3 reticulate_1.20
## [83] ggplot2_3.3.5 rpart_4.1-15
## [85] stringi_1.7.4 S4Vectors_0.31.0
## [87] e1071_1.7-8 BiocGenerics_0.39.1
## [89] boot_1.3-28 BiocParallel_1.27.3
## [91] GenomeInfoDb_1.29.3 rlang_0.4.11
## [93] pkgconfig_2.0.3 matrixStats_0.60.0
## [95] bitops_1.0-7 evaluate_0.14
## [97] lattice_0.20-44 tensor_1.5
## [99] ROCR_1.0-11 purrr_0.3.4
## [101] htmlwidgets_1.5.3 patchwork_1.1.1
## [103] cowplot_1.1.1 tidyselect_1.1.1
## [105] parallelly_1.27.0 RcppAnnoy_0.0.19
## [107] plyr_1.8.6 magrittr_2.0.1
## [109] R6_2.5.1 IRanges_2.27.0
## [111] DescTools_0.99.42 generics_0.1.0
## [113] DelayedArray_0.19.1 DBI_1.1.1
## [115] mgcv_1.8-36 pillar_1.6.2
## [117] fitdistrplus_1.1-5 abind_1.4-5
## [119] survival_3.2-12 RCurl_1.98-1.4
## [121] tibble_3.1.4 future.apply_1.8.1
## [123] crayon_1.4.1 KernSmooth_2.23-20
## [125] utf8_1.2.2 spatstat.geom_2.2-2
## [127] plotly_4.9.4.9000 rmarkdown_2.10
## [129] grid_4.1.0 data.table_1.14.0
## [131] digest_0.6.27 xtable_1.8-4
## [133] tidyr_1.1.3 httpuv_1.6.1
## [135] stats4_4.1.0 munsell_0.5.0
## [137] viridisLite_0.4.0