Automated ingestion and conversion of various single-cell data formats

Authors: Brian Schilder

Most recent update: Sep-13-2023

Intro

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.

1. Read: Automatically infers the file/object type and loads it (sub-function: read_data()).
2. Convert: Converts it to the desired file/object type (sub-function: to_<format>).
3. Save: Saves the converted file/object (sub-function: save_data()).

Documentation website

Vignette: data ingestion

Vignette: conda environments

i/o formats

Supported input formats

• SingleCellExperiment
• SummarizedExperiment
• HDF5SummarizedExperiment
• Seurat
• H5Seurat
• anndata
• loom
• loomR
• CellDataSet/monocle
• ExpressionSet
• list
• EWCE
• matrix
• sparseMatrix (dgTMatrix/dgCMatrix)
• DelayedArray

Supported output formats

• SingleCellExperiment
• HDF5SummarizedExperiment
• Seurat
• H5Seurat
• anndata
• list

Planned output formats

• loom
• CellDataSet/monocle

Notes:

• For exporting to additional formats, see these following packages:
  • sceasy
  • zellkonverter
• 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.

Installation

if(!require("remotes")) install.packages("remotes")

remotes::install_github("neurogenomics/scKirby")

Conda 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 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] pillar_1.9.0        compiler_4.2.1      RColorBrewer_1.1-3 
##  [4] BiocManager_1.30.20 yulab.utils_0.0.6   tools_4.2.1        
##  [7] digest_0.6.31       jsonlite_1.8.4      evaluate_0.21      
## [10] lifecycle_1.0.3     tibble_3.2.1        gtable_0.3.3       
## [13] pkgconfig_2.0.3     rlang_1.1.1         cli_3.6.1          
## [16] rstudioapi_0.14     rvcheck_0.2.1       yaml_2.3.7         
## [19] xfun_0.40           fastmap_1.1.1       dplyr_1.1.2        
## [22] knitr_1.44          generics_0.1.3      desc_1.4.2         
## [25] vctrs_0.6.3         dlstats_0.1.7       rprojroot_2.0.3    
## [28] grid_4.2.1          tidyselect_1.2.0    here_1.0.1         
## [31] data.table_1.14.8   glue_1.6.2          R6_2.5.1           
## [34] fansi_1.0.4         rmarkdown_2.22      ggplot2_3.4.2      
## [37] badger_0.2.3        magrittr_2.0.3      scales_1.2.1       
## [40] htmltools_0.5.5     rworkflows_0.99.13  colorspace_2.1-0   
## [43] renv_0.17.3         utf8_1.2.3          munsell_0.5.0

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