# Ensure Giotto Suite is installed
if(!"Giotto" %in% installed.packages()) {
pak::pkg_install("drieslab/Giotto")
}
library(Giotto)Many {Giotto} analyses start with manually defining a set of
instructions to use. {Giotto} instructions are used to apply settings to
giotto object behavior at the project level. Each
giotto object needs a set of instructions information that
can either be manually set or generated with defaults when the
giotto object is first created via either
createGiotto*() helpers or the S4 class constructor
giotto(). Once added, these instructions are stored in the
giotto object @instructions slot.
These instruction sets can be created via either the constructor
or the generic accessor/replacement function
instructions() when no giotto object is
provided.
# named params can be provided (same as those in createGiottoInstructions())
instructions()These instruction sets are of S3 class
giottoInstructions, extending list.
Specific information about all behaviors that can be set in these
instruction sets can be found in the
?GiottoClass::createGiottoInstructions documentation.
The most commonly used settings are:
save_dir - directory to save plot results tosave_dir
show_plot - logical. Whether plotting functions should
display the plotreturn_plot - logical. Whether a plot object (for
example a {ggplot2} gg object) should be returned from the
plotting function.python_path - filepath to a python binary to use with
{Giotto}.A commonly used set of instructions:
results_folder <- "path/to/results"
python_path <- NULL # NULL will let {Giotto} autodetect
intrs <- createGiottoInstructions(
save_dir = results_folder,
save_plot = TRUE,
show_plot = FALSE,
return_plot = FALSE,
python_path = python_path
)This establishes the location defined by results_folder
as a common location to export all plots created from that
giotto object as saved images. This can be helpful for
setting up analysis workflows.
save_plot is set to TRUE, while
show_plot is set to FALSE. This ensures that
plots are saved but not plotted directly to the viewer. Outputting plots
in this manner can save time since directly plotting can be slow.
return_plot is set as FALSE since it is not
necessary in most cases. It additionally prevents double plotting form
occurring when the plotting function output is not assigned into a
variable.
A python path to a specified python binary is also specified here,
although it is usually preferable to set the following global option in
the ~/.Rprofile when the same particular path is needed
every time, and then allow autodetection to find it. (see next
section)
options("giotto.py_path" = "???")The python environment to use via {reticulate} can be set with the
python_path param. Providing NULL will have
{Giotto} autodetect for a python environment to use (usually the
giotto_env).
?GiottoClass::giotto_python for more info on how to
set up a {Giotto} miniconda python environment and how autodetection of
an environment to use works.Creating a set of instructions will call
GiottoClass::set_giotto_python_path() under the hood and
activate a python environment.
Critically, only one {reticulate} python environment can be
activated per session. If you would like to avoid activating a
python environment, you can set the following global option before
running a giotto or giottoInstructions object
creation function:
options("giotto.use_conda" = FALSE)giotto Object Creation
Giotto instructions can be set or retrieved from a
giotto object at any time. They can be initially applied
directly at the giotto object creation step. Here we add in
the giottoInstructions that we created in the previous
section.
createGiottoObject(instructions = instrs) # object creation helper
# or
giotto(instructions = instrs) # class constructorOf course the actual data can also be added via other params
during these giotto object creation steps, they are just
omitted for this demonstration
You can extract the full set of instructions in a giotto
object using the instructions() generic.
g <- giotto() # will generate and attach a default set of instructions
i <- instructions(g)
force(i)You can also extract specific parameters of that instruction set
instructions(g, "dpi")This will also work directly on the giottoInstructions
object
instructions(i, "dpi")Setting of giottoInstructions is also done with the
instructions() generic.
instructions(g) <- instrs # `instrs` had `return_plot = FALSE`
instructions(g, "return_plot") # this is now FALSEYou can also update one or more specific instructions settings at
once either directly on the giottoInstructions object or to
the giotto object.
# on the giottoInstructions object
instructions(i, "dpi") <- 600
force(i)
# on the giotto object
instructions(g, "dpi") <- 600
force(instructions(g))Multiple value replacement currently only works with the
giotto object.
instructions(g, c("plot_format", "height")) <- list("tiff", 15)
force(instructions(g))R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 15.3.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Giotto_4.2.1 GiottoClass_0.4.7
loaded via a namespace (and not attached):
[1] colorRamp2_0.1.0 gridExtra_2.3 remotes_2.5.0
[4] rlang_1.1.4 magrittr_2.0.3 GiottoUtils_0.2.4
[7] matrixStats_1.4.1 compiler_4.4.1 png_0.1-8
[10] callr_3.7.6 vctrs_0.6.5 pkgconfig_2.0.3
[13] SpatialExperiment_1.14.0 crayon_1.5.3 fastmap_1.2.0
[16] backports_1.5.0 magick_2.8.5 XVector_0.44.0
[19] ggraph_2.2.1 utf8_1.2.4 rmarkdown_2.29
[22] UCSC.utils_1.0.0 ps_1.7.6 purrr_1.0.2
[25] xfun_0.49 zlibbioc_1.50.0 cachem_1.1.0
[28] GenomeInfoDb_1.40.0 jsonlite_1.8.9 DelayedArray_0.30.0
[31] tweenr_2.0.3 terra_1.8-29 parallel_4.4.1
[34] R6_2.5.1 reticulate_1.39.0 pkgload_1.3.4
[37] GenomicRanges_1.56.0 scattermore_1.2 Rcpp_1.0.13-1
[40] SummarizedExperiment_1.34.0 knitr_1.49 IRanges_2.38.0
[43] Matrix_1.7-0 igraph_2.1.1 tidyselect_1.2.1
[46] rstudioapi_0.16.0 abind_1.4-8 yaml_2.3.10
[49] viridis_0.6.5 codetools_0.2-20 curl_5.2.3
[52] processx_3.8.4 pkgbuild_1.4.4 lattice_0.22-6
[55] tibble_3.2.1 Biobase_2.64.0 withr_3.0.2
[58] evaluate_1.0.1 desc_1.4.3 polyclip_1.10-7
[61] pillar_1.9.0 MatrixGenerics_1.16.0 checkmate_2.3.2
[64] stats4_4.4.1 plotly_4.10.4 generics_0.1.3
[67] S4Vectors_0.42.0 ggplot2_3.5.1 munsell_0.5.1
[70] scales_1.3.0 GiottoData_0.2.15 gtools_3.9.5
[73] glue_1.8.0 lazyeval_0.2.2 tools_4.4.1
[76] GiottoVisuals_0.2.12 data.table_1.16.2 graphlayouts_1.1.1
[79] tidygraph_1.3.1 cowplot_1.1.3 grid_4.4.1
[82] tidyr_1.3.1 colorspace_2.1-1 SingleCellExperiment_1.26.0
[85] GenomeInfoDbData_1.2.12 ggforce_0.4.2 cli_3.6.3
[88] dbMatrix_0.0.0.9023 fansi_1.0.6 S4Arrays_1.4.0
[91] viridisLite_0.4.2 dplyr_1.1.4 gtable_0.3.6
[94] digest_0.6.37 BiocGenerics_0.50.0 SparseArray_1.4.1
[97] ggrepel_0.9.6 rjson_0.2.21 htmlwidgets_1.6.4
[100] farver_2.1.2 memoise_2.0.1 htmltools_0.5.8.1
[103] lifecycle_1.0.4 httr_1.4.7 MASS_7.3-60.2