# Ensure Giotto Suite is installed.
if(!"Giotto" %in% installed.packages()) {
pak::pkg_install("drieslab/Giotto")
}
# Ensure the Python environment for Giotto has been installed.
genv_exists <- Giotto::checkGiottoEnvironment()
if(!genv_exists){
# The following command need only be run once to install the Giotto environment.
Giotto::installGiottoEnvironment()
}For use with raw subcellular data. Find overlapped feature information and convert to a matrix
# overlap of feature points with polygons
g <- calculateOverlap(g)
g <- overlapToMatrix(g)
# specifically use a polygon set & subset of points
g <- calculateOverlap(g,
spatial_info = "z0", # use polygons from z0
feat_subset_column = "global_z",
feat_subset_ids = 0
)
g <- overlapToMatrix(g)
# overlap of image intensity values with polygons
g <- calculateOverlap(g,
image_names = c("dapi_z0", "dapi_z1"),
name_overlap = "image" # default name would be "protein" for image overlaps
)
g <- overlapToMatrix(g,
type = "intensity",
feat_info = "image"
)
# combine overlaps data from multiple spatial units (intended for z stack data)
# also calculate z1
g <- calculateOverlap(g,
spatial_info = "z1", # use polygons from z1
feat_subset_column = "global_z",
feat_subset_ids = 1
)
g <- overlapToMatrix(g, poly_info = "z1")
g = aggregateStacks(g,
spat_units = c("z0", "z1"),
feat_type = "rna",
new_spat_unit = "aggregate",
values = "raw"
)
# setting active spatial unit and feature type
activeSpatUnit(g) <- "aggregate"
activeFeatType(g) <- "rna"
g <- addSpatialCentroidLocations(g, poly_info = "aggregate")For more detail, see the standard workflow vignette
g <- filterGiotto(g,
expression_threshold = 1,
feat_det_in_min_cells = 3,
min_det_feats_per_cell = 5
)
g <- normalizeGiotto(g)
g <- addStatistics(g)
g <- runPCA(g)
g <- runUMAP(g, dimensions_to_use = 1:20)
g <- createNearestNetwork(g, dimensions_to_use = 1:20)
g <- doLeidenCluster(g)
dimPlot2D(g, cell_color = "leiden_clus")Available with dev version 0.4.0 of GiottoClass
# Feature IDs
rownames(g)
featIDs(g)
# Spatial IDs (cells or spots)
colnames(g)
spatIDs(g)
# both sets of IDs
dimnames(g)
# Select specific sets of IDs
featIDs(g, subset = total_expr >= 500)
spatIDs(g, subset = leiden_clus %in% c(4, 5))
spatIDs(g, subset = leiden_clus %in% c(4, 5), negate = TRUE)
# number of cells and features
ncol(g)
nrow(g)
dim(g)Giotto subobjects carry their own metadata about where they should be in the object. There are several specific getter and setter functions. GiottoClass 0.4.0 introduces some common APIs
# also see ?subset_giotto_subobjects
# get any of: “spatial_info”, “spatial_locs”, “spatial_network”, “feat_info”,
# “expression”, “cell_metadata”, “feat_metadata”, “spatial_enrichment”,
# “nn_network”, “dimension_reduction”, “multiomics”, “images"
# Output is as a list of objects
g[["expression"]]
# be more specific with object name
a <- g[["expression", "raw"]] # (4 items)
# further filter by requested spatial unit. feat_type can also be used.
b <- g[["expression", "raw", spat_unit = "aggregate"]] # (only 1 item)
# any subobject can be set into the Giotto object using setGiotto
g <- setGiotto(g, b)
g <- setGiotto(g, a) # multiple objects can be set at once.
# Validity is only checked once all items are added.
# pull out all the subobjects of a giotto object
as.list(g)
# Giotto subobjects are S4 structures. They usually wrap a core data object
# This can be extracted using an empty call to `[`
a[[1]][]
# Accessing terra object spatial values can be done either through as.data.table()
gpoly <- g[["spatial_info", "aggregate"]][[1]]
gpoints <- g[["feat_info", "rna"]][[1]]
data.table::as.data.table(gpoly, geom = "XY")
data.table::as.data.table(gpoints, geom = "XY")
data.table::as.data.table(gpoly, geom = "WKT")
data.table::as.data.table(gpoly, geom = "HEX")
# or XY() (GiottoClass 0.4.0 or greater) which works for all spatial objects
# and retrieves xy(z) coordinates as `matrix` and sets them
sl <- g[["spatial_locs", "raw"]][[1]]
# get
m1 <- XY(sl)
m2 <- XY(gpoly)
m3 <- XY(gpoints)
XY(gpoly[1]) # vertices from first polygon
# set
XY(sl) <- m1
XY(gpoly) <- m2
XY(gpoints) <- m3
# See also ?spatValues
# spatValues can pull data from any of:
# cell expression, cell metadata, spatial locations, spatial enrichment,
# dimension reduction, polygon info
# The values will be provided as a data.table with the cell_IDs
spatValues(g, feats = c("leiden_clus", "nr_feats"))
# The first object found will be selected by default.
# It is possible to be more specific by mentioning the name of the object to check
spatValues(g, feats = c("Pdgfra", "Mlc1"), expression_values = "normalized")
# Pulling values from multiple spatial units
spatValues(g, feats = c("Pdgfra", "Mlc1"), spat_unit = c("aggregate", "z0", "z1"))GiottoClass 0.4.0 introduces [ and subset generic subsetting
# also see `subset_giotto`
# subset by i (features) and j (observations)
g[1:200, 1:300]
subset(g, subset = leiden_clus %in% c(4, 5))
subset(g, subset = leiden_clus %in% c(4, 5), negate = TRUE)
# The older subsetGiotto() can also be used.
subsetGiotto(g, cell_ids = spatIDs(g)[1:300], feat_ids = featIDs(g)[1:200])
# subset by spatial locations centroids
subsetGiottoLocs(g, y_max = -4900)
# subset subcellular (used when no aggregate information has been calculated yet)
subsetGiottoLocsSubcellular(g, y_max = -4900) # will not work now
# join objects (without integration) into a single object so that they can be
# analyzed in a common spatial and expression space.
# This updates the cell_IDs and image names, and adds a new `list_ID` column in cell metadata
# dry run can be turned on to preview the spatial positioning of the objects after joining.
joinGiottoObjects(list(g, g), gobject_names = c("a", "b"), dry_run = TRUE)
j <- joinGiottoObjects(list(g, g), gobject_names = c("a", "b"))R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.4
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_4.1.3 GiottoClass_0.4.0
loaded via a namespace (and not attached):
[1] colorRamp2_0.1.0 DBI_1.2.3 deldir_2.0-4
[4] rlang_1.1.4 magrittr_2.0.3 GiottoUtils_0.2.0
[7] matrixStats_1.4.1 e1071_1.7-14 compiler_4.4.1
[10] png_0.1-8 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_2.8.4 XVector_0.44.0
[19] labeling_0.4.3 utf8_1.2.4 rmarkdown_2.28
[22] UCSC.utils_1.0.0 purrr_1.0.2 xfun_0.47
[25] beachmat_2.20.0 zlibbioc_1.50.0 GenomeInfoDb_1.40.0
[28] jsonlite_1.8.9 DelayedArray_0.30.0 BiocParallel_1.38.0
[31] terra_1.7-78 irlba_2.3.5.1 parallel_4.4.1
[34] R6_2.5.1 RColorBrewer_1.1-3 reticulate_1.39.0
[37] GenomicRanges_1.56.0 scattermore_1.2 Rcpp_1.0.13
[40] SummarizedExperiment_1.34.0 knitr_1.48 R.utils_2.12.3
[43] FNN_1.1.4.1 IRanges_2.38.0 Matrix_1.7-0
[46] igraph_2.0.3 tidyselect_1.2.1 rstudioapi_0.16.0
[49] abind_1.4-8 codetools_0.2-20 lattice_0.22-6
[52] tibble_3.2.1 Biobase_2.64.0 withr_3.0.1
[55] evaluate_1.0.0 sf_1.0-16 units_0.8-5
[58] proxy_0.4-27 exactextractr_0.10.0 pillar_1.9.0
[61] MatrixGenerics_1.16.0 KernSmooth_2.23-24 checkmate_2.3.2
[64] stats4_4.4.1 plotly_4.10.4 generics_0.1.3
[67] dbscan_1.2-0 sp_2.1-4 S4Vectors_0.42.0
[70] ggplot2_3.5.1 munsell_0.5.1 scales_1.3.0
[73] GiottoData_0.2.15 gtools_3.9.5 class_7.3-22
[76] glue_1.8.0 lazyeval_0.2.2 tools_4.4.1
[79] GiottoVisuals_0.2.5 data.table_1.16.0 ScaledMatrix_1.12.0
[82] cowplot_1.1.3 grid_4.4.1 tidyr_1.3.1
[85] colorspace_2.1-1 SingleCellExperiment_1.26.0 GenomeInfoDbData_1.2.12
[88] raster_3.6-26 BiocSingular_1.20.0 rsvd_1.0.5
[91] cli_3.6.3 fansi_1.0.6 S4Arrays_1.4.0
[94] viridisLite_0.4.2 dplyr_1.1.4 uwot_0.2.2
[97] gtable_0.3.5 R.methodsS3_1.8.2 digest_0.6.37
[100] BiocGenerics_0.50.0 classInt_0.4-10 SparseArray_1.4.1
[103] ggrepel_0.9.6 farver_2.1.2 rjson_0.2.21
[106] htmlwidgets_1.6.4 htmltools_0.5.8.1 R.oo_1.26.0
[109] lifecycle_1.0.4 httr_1.4.7