Author: Brian M. Schilder
Most recent update: Jun-30-2021
Automated ingestion and conversion of various single-cell data formats.
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()).
i/o formats
Planned output formats
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).
Installation
if(!"remotes" %in% rownames(install.packages())){install.packages("remotes")}
remotes::install_github("bschilder/scKirby")Quick examples
Here are several quick examples of how one can use scKirby. For a complete list of examples please see the documentation website.
Ingest list
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.
As SingleCellExperiment
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: /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmplQASgm/scKirby_output.rds
As Seurat
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: /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmplQASgm/scKirby_output.rdsIngest Seurat
As SingleCellExperiment
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: /var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmplQASgm/scKirby_output.rdsConda environments
Updating Seurat objects
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.
Session Info
utils::sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## 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_0.32.0
## [9] GenomicRanges_1.44.0 gld_2.6.2
## [11] spatstat.data_2.1-0 rstudioapi_0.13
## [13] proxy_0.4-26 leiden_0.3.8
## [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_1.8.2.1 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
## [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_1.0.6
## [51] scattermore_0.7 Biobase_2.52.0
## [53] vctrs_0.3.8 nlme_3.1-152
## [55] lmtest_0.9-38 xfun_0.24
## [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_1.38.0 MASS_7.3-54
## [67] zoo_1.8-9 scales_1.1.1
## [69] spatstat.core_2.2-0 spatstat.utils_2.2-0
## [71] promises_1.2.0.1 MatrixGenerics_1.4.0
## [73] parallel_4.1.0 SummarizedExperiment_1.22.0
## [75] expm_0.999-6 RColorBrewer_1.1-2
## [77] SingleCellExperiment_1.14.1 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_1.6.2 S4Vectors_0.30.0
## [87] e1071_1.7-7 BiocGenerics_0.38.0
## [89] boot_1.3-28 BiocParallel_1.26.0
## [91] GenomeInfoDb_1.28.0 rlang_0.4.11
## [93] pkgconfig_2.0.3 matrixStats_0.59.0
## [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_1.26.0 RcppAnnoy_0.0.18
## [107] plyr_1.8.6 magrittr_2.0.1
## [109] R6_2.5.0 IRanges_2.26.0
## [111] DescTools_0.99.42 generics_0.1.0
## [113] DelayedArray_0.18.0 DBI_1.1.1
## [115] mgcv_1.8-36 pillar_1.6.1
## [117] fitdistrplus_1.1-5 abind_1.4-5
## [119] survival_3.2-11 RCurl_1.98-1.3
## [121] tibble_3.1.2 future.apply_1.7.0
## [123] crayon_1.4.1 KernSmooth_2.23-20
## [125] utf8_1.2.1 spatstat.geom_2.2-0
## [127] plotly_4.9.4.1 rmarkdown_2.9
## [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