# 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 that has spatial boundaries/polygon annotations and spatial features. Find overlapped feature information and convert to a matrix. Other examples can be found in the object creation tutorial.
# 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)
# feature metadata
fDataDT(g)
# cell metadata
pDataDT(g)
# get specific values from cell metadata
g$leiden_clus
# set values directly into cell metadata
# this is safest when directly using values from another metadata column
# since cell_IDs and their ordering may not as expected.
g$leiden_clus2 <- sprintf("clus_%s", g$leiden_clus)
force(g$leiden_clus2)
# annotate based on another column
g <- annotateGiotto(g,
name = "region",
cluster_column = "leiden_clus",
annotation_vector = c(
"1" = "a",
"2" = "b",
"3" = "c",
"4" = "a",
"5" = "d",
"6" = "b"
)
)
spatPlot2D(g, cell_color = "region")
# merge in table of values to metadata
ann <- data.frame(
cell_ID = spatIDs(g),
random = rnorm(ncol(g))
)
force(ann)
g <- addCellMetadata(g,
new_metadata = ann,
by_column = TRUE,
column_cell_ID = "cell_ID"
)
pDataDT(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"))For a more information, see the object splitting and joining tutorial
# split a Giotto object into a list of multiple smaller ones based on a cell metadata column
splitGiotto(g, "leiden_clus")
# slice the giotto object based on spatial unit and feature type
z0 <- sliceGiotto(g, spat_unit = "z0")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