Interoperability: AnnData#
- Date:
4/14/23
1 Converting to and from AnnData (squidpy-flavor)#
This tutorial details how to use the conversion functions
anndataToGiotto() and giottoToAnnData(). A mini Giotto object
will be used for minimal computational requirements. Please note that
these functions are inherently in active development, since changes to
either squidpy or anndata are possible.
2 Start Giotto#
# Ensure Giotto Suite is installed
if(!"Giotto" %in% installed.packages()) {
devtools::install_github("drieslab/Giotto@suite")
}
library(Giotto)
# Ensure Giotto Data is installed
if(!"GiottoData" %in% installed.packages()) {
devtools::install_github("drieslab/GiottoData")
}
library(GiottoData)
# Ensure the Python environment for Giotto has been installed
genv_exists = checkGiottoEnvironment()
if(!genv_exists){
# The following command need only be run once to install the Giotto environment
installGiottoEnvironment()
}
3 Create a Giotto object#
# Specify path to which results may be saved
results_directory = paste0(getwd(),'/giotto_anndata_conversion/')
# Optional: Specify a path to a Python executable within a conda or miniconda
# environment. If set to NULL (default), the Python executable within the previously
# installed Giotto environment will be used.
my_python_path = NULL # alternatively, "/local/python/path/python" if desired.
mini_gobject = loadGiottoMini(dataset = 'vizgen',
python_path = my_python_path)
instrs = showGiottoInstructions(mini_gobject)
instrs$save_dir = results_directory
mini_gobject = replaceGiottoInstructions(gobject = mini_gobject,
instructions = instrs)
4 Convert Giotto to AnnData#
Since Giotto is structured hierarchally, converting Giotto to AnnData
will result in multiple .h5ad files. Each file will correspond to a
Giotto spat_unit feat_type pair. Furthermore, each expression
slot will be treated as a layer in the resulting AnnData slot.
Squidpy anndata objects take different defaults for various operations compared to Giotto. For instance, the default nearest neighbor network is a kNN for squidpy, while the default for Giotto is a sNN. We’ll create a kNN in addition to the sNN within this object already to show how it they are handled.
mini_gobject = createNearestNetwork(gobject = mini_gobject,
spat_unit = "aggregate",
feat_type = "rna",
type = "kNN",
dim_reduction_to_use = "umap",
dim_reduction_name = "umap",
k = 15,
name = "kNN.umap")
The above cell creates a nearest network with almost all default parameters. We’ll change some and make a new network to show how the converter handles this.
mini_gobject = createNearestNetwork(gobject = mini_gobject,
spat_unit = "aggregate",
feat_type = "rna",
type = "kNN",
dim_reduction_to_use = "umap",
dim_reduction_name = "umap",
k = 6,
name = "new_network")
Since we have multiple spat_unit feat_type pairs, there will be
multiple files created by this function. The names of the .h5ad
files will be returned. In the case of a non-anndata-default nearest
network or spatial network name, the key_added terms will be recorded
and saved in .txt files named accordingly. Please see the
documentation for further details.
anndata_conversions = giottoToAnnData(gobject = mini_gobject,
save_directory = results_directory,
python_path = my_python_path)
5 AnnData to Giotto#
To convert an AnnData Object back into a Giotto object, it must first be
saved as a .h5ad file. The name of said file may then be provided to
anndataToGiotto() for conversion.
If a nearest neighbor network or spatial netowkr was created using the
key_added argument, they may be provided to arguments n_key_added
and/or spatial_n_key_added, respectively. If converting an anndata
object that was originally a giotto object, the .txt files generated
by giottoToAnnData() may be provided to these arguments as well.
z0_rna_gobject <- anndataToGiotto(anndata_path = "./giotto_anndata_conversion/z0_rna_converted_gobject.h5ad",
python_path = my_python_path)
z1_rna_gobject <- anndataToGiotto(anndata_path = "./giotto_anndata_conversion/z1_rna_converted_gobject.h5ad",
python_path = my_python_path)
aggregate_rna_gobject <- anndataToGiotto(anndata_path = "./giotto_anndata_conversion/aggregate_rna_converted_gobject.h5ad",
python_path = my_python_path,
n_key_added = list("sNN.pca","new_network"),
spatial_n_key_added = "aggregate_rna_spatial_network_keys_added.txt")
6 Session Info#
sessionInfo()
R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22621)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] GiottoData_0.2.1 Giotto_3.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 pillar_1.9.0 compiler_4.2.2 tools_4.2.2
[5] digest_0.6.30 jsonlite_1.8.3 evaluate_0.20 lifecycle_1.0.3
[9] tibble_3.2.1 gtable_0.3.3 lattice_0.20-45 png_0.1-7
[13] pkgconfig_2.0.3 rlang_1.1.0 igraph_1.4.1 Matrix_1.5-1
[17] cli_3.4.1 rstudioapi_0.14 parallel_4.2.2 yaml_2.3.7
[21] xfun_0.38 fastmap_1.1.0 terra_1.7-18 dplyr_1.1.1
[25] knitr_1.42 rappdirs_0.3.3 generics_0.1.3 vctrs_0.6.1
[29] rprojroot_2.0.3 grid_4.2.2 tidyselect_1.2.0 here_1.0.1
[33] reticulate_1.26 glue_1.6.2 data.table_1.14.6 R6_2.5.1
[37] fansi_1.0.4 rmarkdown_2.21 ggplot2_3.4.1 magrittr_2.0.3
[41] scales_1.2.1 codetools_0.2-18 htmltools_0.5.4 colorspace_2.1-0
[45] utf8_1.2.3 munsell_0.5.0 dbscan_1.1-11