Giotto is a technique-agnostic framework and toolbox for spatial-omic analysis. Its structure and classes are designed to be flexible, intuitive, and readable. The framework supports working with both aggregate (cell x count) and un-aggregated spatial data where the polygon annotations are separate from the spatial expression data.
Usage of the Giotto package revolves around the
giotto
object. This is an S4 class that holds spatial
expression data and facilitates its manipulation and visualization with
the Giotto package’s functions. Additional metadata and other
outputs generated from certain functions, which may be used in
downstream analyses, are also be stored within the giotto
object. Its self-contained nature provides a convenient representation
of the entire spatial experiment and is why most Giotto
functions take a given giotto
object as input and return a
giotto
object as output.
Data is organized within the giotto
object in defined
slots
as described in the diagram below.
Biology happens across multiple scales of size and types of modalities. While it is possible to simply generate a new object for each combination of the two, the fact that data from most spatial methods are both high resolution and spatially contiguous, requires a more flexible approach that permits the coexistence of multiple spatial units within the same object. This allows the user to define the spatial unit(s) of biology that are most relevant to the analysis and re-aggregate the feature information to those units.
With this organization it is convenient to compare expression across different spatial units. Additionally, by determining spatial overlaps between these spatial units, it becomes possible to represent the hierarchical organization of biological subunits and make queries using it.
To accommodate this complexity, information is subnested within many
of the giotto
object’s slots first by
spat_unit
(spatial unit) and then by feat_type
(feature type). This structurally separates each set of information
within Giotto’s framework so that there is minimal
ambiguity.
A summary of what information the object contains can be viewed by directly returning it.
library(Giotto)
library(GiottoData)
library(data.table)
vizmini = loadGiottoMini('vizgen')
vizmini
An object of class giotto
>Active spat_unit: z0
>Active feat_type: rna
[SUBCELLULAR INFO]
polygons : z0 z1 aggregate
features : rna
[AGGREGATE INFO]
expression -----------------------
[z0][rna] raw
[z1][rna] raw
[aggregate][rna] raw normalized scaled pearson
spatial locations ----------------
[z0] raw
[z1] raw
[aggregate] raw
spatial networks -----------------
[aggregate] Delaunay_network kNN_network
spatial enrichments --------------
[aggregate][rna] cluster_metagene
dim reduction --------------------
[aggregate][rna] pca umap tsne
nearest neighbor networks --------
[aggregate][rna] sNN.pca
attached images ------------------
giottoLargeImage : 4 items...
Use objHistory() to see steps and params used
Included below is a description of the giotto
object
subnesting for each data slot and also the accessor functions for
setting and getting information from them.
Slot | Nested | Example | Internal Accessors |
---|---|---|---|
@expression | spat_unit - | getExpression() | |
feat_type - | cell - rna - raw | setExpression() | |
name | |||
————————— | ———————— | ———————————- | ————————- |
@cell_metadata | spat_unit - | cell - rna | getCellMetadata() |
feat_type | setCellMetadata() | ||
————————— | ———————— | ———————————- | ————————- |
@feat_metadata | spat_unit - | cell - rna | getFeatMetadata() |
feat_type | setFeatMetadata() | ||
————————— | ———————— | ———————————- | ————————- |
@spatial_grid | spat_unit - | grid- grid | getSpatialGrid() |
name | setSpatialGrid() | ||
————————— | ———————— | ———————————- | ————————- |
@dimension_reduction | approach - | ||
spat_unit - | getDimReduction() | ||
feat_type - | cells - cell - rna - pca - pca | setDimReduction() | |
method - | |||
name | |||
————————— | ———————— | ———————————- | ————————- |
@multiomics | spat_unit - | ||
feat_type - | cell - rna-protein - WNN - theta_weighted_matrix | getMultiomics() | |
method - | setMultiomics() | ||
name | |||
————————— | ———————— | ———————————- | ————————- |
@nn_network | spat_unit- | getNearestNetwork() | |
method - | cell - sNN - sNN_results1 | setNearestNetwork() | |
name | |||
————————— | ———————— | ———————————- | ————————- |
@spatial_enrichment | spat_unit - | getSpatialEnrichment() | |
feat_type - | cell - rna - results1 | setSpatialEnrichment() | |
name | |||
————————— | ———————— | ———————————- | ————————- |
@spatial_info | spat_unit | cell | getPolygonInfo() |
setPolygonInfo() | |||
————————— | ———————— | ———————————- | ————————- |
@spatial_locs | spat_unit - | cell - raw | getSpatialLocations() |
name | setSpatialLocations() | ||
————————— | ———————— | ———————————- | ————————- |
@spatial_network | spat_unit - | cell - Delaunay_network1 | getSpatialNetwork() |
name | setSpatialNetwork() | ||
————————— | ———————— | ———————————- | ————————- |
@feat_info | feat_type | rna | getFeatureInfo() |
setFeatureInfo() | |||
————————— | ———————— | ———————————- | ————————- |
@images | name | image | getGiottoImage() |
setGiottoImage() | |||
————————— | ———————— | ———————————- | ————————- |
@largeImages | name | image | getGiottoImage() |
setGiottoImage() | |||
————————— | ———————— | ———————————- | ————————- |
@instructions | instructions() | ||
————————— | ———————— | ———————————- | ————————- |
Show and list functions are also provided for determining what information exists within each of these slots and its nesting.
show
functions print a preview of all the data within
the slot, but do not return information
showGiottoSpatLocs(vizmini)
├──Spatial unit "z0"
│ └──S4 spatLocsObj "raw" coordinates: (498 rows)
│ An object of class spatLocsObj
│ provenance: z0
│ ------------------------
│ sdimx sdimy cell_ID
│ 1: 6405.067 -4780.499 40951783403982682273285375368232495429
│ 2: 6426.020 -4972.519 240649020551054330404932383065726870513
│ 3: 6428.456 -4799.158 274176126496863898679934791272921588227
│ 4: 6408.155 -4816.583 323754550002953984063006506310071917306
│
│ ranges:
│ sdimx sdimy
│ [1,] 6402.438 -5146.726
│ [2,] 6899.203 -4700.157
│
│
│
├──Spatial unit "z1"
│ └──S4 spatLocsObj "raw" coordinates: (504 rows)
│ An object of class spatLocsObj
│ provenance: z1
│ ------------------------
│ sdimx sdimy cell_ID
│ 1: 6404.014 -4779.625 40951783403982682273285375368232495429
│ 2: 6408.296 -4970.794 17685062374745280598492217386845129350
│ 3: 6401.148 -4991.061 223553142498364321238189328942498473503
│ 4: 6430.153 -4971.251 240649020551054330404932383065726870513
│
│ ranges:
│ sdimx sdimy
│ [1,] 6401.148 -5147.193
│ [2,] 6899.323 -4700.410
│
│
│
└──Spatial unit "aggregate"
└──S4 spatLocsObj "raw" coordinates: (461 rows)
An object of class spatLocsObj
provenance: z0 z1
------------------------
sdimx sdimy cell_ID
1: 6637.881 -5140.465 100210519278873141813371229408401071444
2: 6471.978 -4883.541 101161259912191124732236989250178928032
3: 6801.610 -4968.685 101488859781016188084173008420811094152
4: 6789.055 -5105.338 101523780333017320796881555775415156847
ranges:
sdimx sdimy
[1,] 6401.412 -5146.747
[2,] 6899.108 -4700.326
- `list` functions are (internal) functions that return a `data.table`
of the available information and nesting.
Giottolist_expression(vizmini)
spat_unit feat_type name
1: z0 rna raw
2: z1 rna raw
3: aggregate rna raw
4: aggregate rna normalized
5: aggregate rna scaled
6: aggregate rna pearson
# Find specific spat_unit objects #
Giottolist_expression(vizmini, spat_unit = 'z0')
list names
(internal) functions return a
vector
of object names at the specified nesting
Giottolist_expression_names(vizmini, spat_unit = 'z1', feat_type = 'rna')
Going further, sometimes different sources of information can be used when aggregating to a particular spatial unit. This is most easily shown with the subcellular datasets from the Vizgen MERSCOPE platform which provide both feature polygon information for multiple confocal planes within a tissue. The aggregated information produced then could be drawn from different z-planes or combinations thereof. Giotto tracks this provenance information for each set of aggregated data.
expr_mat = getExpression(vizmini, spat_unit = 'aggregate')
prov(expr_mat)
Giotto 3.0 update introduced S4 subobjects that are used
within the giotto
object and its processing. These
subobjects provide more formalized definitions for what information and
formatting is needed in each of the giotto
object slots in
order for it to be functional. These objects are standalone and
extensible and commonly used spatial manipulation and plotting methods
are being implemented for them.
In addition, these subobjects carry several pieces of metadata in
additional slots alongside the main information (e.g. also slots for
spat_unit
and feat_type
alongside the
exprDT
slot for the exprObj
S4). This makes it
so that nesting information is retained when they are taken out of the
giotto
object and that nesting information does not need to
be supplied anymore when interacting with the setter
functions.
getter
functions now have an output
param
that defaults to extracting the information from the
giotto
object as the S4 subobject. When extracting
information that will be modified and then returned to the
giotto
object, it is preferred that the information is
extracted as the S4 both so that tagged information is not lost, and
because it is convenient to work with the S4’s main data slot through
the [
and [<-
generics (see Section
3.5).
For directly creating a subobject, constructor functions can be used.
constructors
createExprObj()
createCellMetaObj()
createFeatMetaObj()
createDimObj()
createNearestNetObj()
createSpatLocsObj()
createSpatNetObj()
createSpatEnrObj()
createSpatialGrid()
createGiottoPoints()
createGiottoPolygonsFromDfr()
createGiottoPolygonsFromMask()
createGiottoImage()
createGiottoLargeImage()
coords = data.table(
sdimx = c(1,2,3),
sdimy = c(1,2,3),
cell_ID = c('A','B','C')
)
st = createSpatLocsObj(name = 'test',
spat_unit = 'cell',
coordinates = coords,
provenance = 'cell')
There are non numeric or integer columns for the spatial location input at column position(s): 3 The first non-numeric column will be considered as a cell ID to test for consistency with the expression matrix. Other non numeric columns will be removed
print(st)
Alternatively, read functions can be used to take named nested lists of raw data input and convert them to lists of subobjects which are directly usable by the setter functions.
readers
readPolygonData()
readFeatData()
readExprData()
readCellMetadata()
readFeatMetadata()
readSpatLocsData()
readSpatNetData()
readSpatEnrichData()
readDimReducData()
readNearestNetData()
st2 = readSpatLocsData(list(cell2 = list(test1 = coords,
test2 = coords)))
There are non numeric or integer columns for the spatial location input at column position(s): 3. The first non-numeric column will be considered as a cell ID to test for consistency with the expression matrix. Other non numeric columns will be removed
There are non numeric or integer columns for the spatial location input at column position(s): 3. The first non-numeric column will be considered as a cell ID to test for consistency with the expression matrix. Other non numeric columns will be removed
print(st2)
[[1]]
An object of class spatLocsObj : "test1"
spat_unit : "cell2"
provenance: cell2
------------------------
preview:
sdimx sdimy cell_ID
1: 1 1 A
2: 2 2 B
3: 3 3 C
ranges:
sdimx sdimy
[1,] 1 1
[2,] 3 3
[[2]]
An object of class spatLocsObj : "test2"
spat_unit : "cell2"
provenance: cell2
------------------------
preview:
sdimx sdimy cell_ID
1: 1 1 A
2: 2 2 B
3: 3 3 C
ranges:
sdimx sdimy
[1,] 1 1
[2,] 3 3
Giotto provides getter
and setter
functions for manually accessing the information contained within the
giotto
object. Note that the setters
require
that the data be provided as compatible S4 subobjects or lists thereof.
External data can read into the appropriate formats using the above
reader
functions. The getter
functions return
S4 subobjects by default.
getters
getExpression()
getCellMetadata()
getFeatMetadata()
getSpatialLocations()
getDimReduction()
getNearestNetwork()
getSpatialNetwork()
getPolygonInfo()
getFeatureInfo()
getSpatialEnrichment()
getGiottoImage()
setters
setExpression()
setCellMetadata()
setFeatureMetadata()
setSpatialLocations()
setDimReduction()
setNearestNetwork()
setSpatialNetwork()
setPolygonInfo()
setFeatureInfo()
setSpatialEnrichment()
setGiottoImage()
expval = getExpression(vizmini)
print(expval)
An object of class exprObj : "raw"
spat_unit : "z0"
feat_type : "rna"
provenance: z0
contains:
336 x 498 sparse Matrix of class "dgCMatrix"
Adora1 . . . . . . . . . . 1 . . ......
Adgrb1 . . . . 1 . . . . . . . . ......
Adgrb3 . . . . . . . . . . 1 3 . ......
........suppressing 485 columns and 330 rows
Blank-128 . . . . . . . . . . . . . ......
Blank-145 . . . . . . . . . . . . . ......
Gpr101 . . . . . . . . . . . . . ......
First four colnames:
40951783403982682273285375368232495429
240649020551054330404932383065726870513
274176126496863898679934791272921588227
323754550002953984063006506310071917306
spatUnit()
, featType()
, and
prov()
are replacement functions for tagged spatial unit,
feature type, and provenance information respectively.
The spat_unit
, feat_type
, and
name
params no longer need to be given when setting an S4
subobject with tagged information into a giottoObject
.
However, if input is given to the set
function parameters
then it is prioritized over the tagged information and the tagged
information is updated.
# set exprObj to tagged nesting location
vizmini <- setExpression(vizmini, expval)
Giottolist_expression(vizmini)
Giotto’s S4 subobjects each wrap one main data object. The empty
[]
and []<-
operators are defined as
shorthand for directly accessing this slot that contains the data. For
example, with a spatLocsObj
:
class(spatLocsObj[])
is equivalent to
class(spatLocsObj@coordinates)
In this way, the S4 subobjects can be used in contexts that the wrapped objects could be.
st = getSpatialLocations(vizmini)
class(st)
# With empty brackets
class(st[])
Setting information
print(st)
An object of class spatLocsObj : "raw"
spat_unit : "z0"
provenance: z0
------------------------
preview:
sdimx sdimy cell_ID
1: 6405.067 -4780.499 40951783403982682273285375368232495429
2: 6426.020 -4972.519 240649020551054330404932383065726870513
3: 6428.456 -4799.158 274176126496863898679934791272921588227
4: 6408.155 -4816.583 323754550002953984063006506310071917306
5: 6425.894 -4862.808 87260224659312905497866017323180367450
---
494: 6863.376 -4764.372 264234489423886906860498828392801290668
495: 6833.515 -4724.922 328891726607418454659643302361160567789
496: 6829.474 -4755.392 6380671372744430258754116433861320161
497: 6823.512 -4713.632 75286702783716447443887872812098770697
498: 6842.534 -4717.261 9677424102111816817518421117250891895
ranges:
sdimx sdimy
[1,] 6402.438 -5146.726
[2,] 6899.203 -4700.157
st[] = coords
print(st)
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.2.1
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
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/Mexico_City
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] data.table_1.14.10 GiottoData_0.2.6.1 GiottoUtils_0.1.2
[4] Giotto_4.0.2 GiottoClass_0.1.2
loaded via a namespace (and not attached):
[1] rstudioapi_0.15.0 jsonlite_1.8.8
[3] magrittr_2.0.3 magick_2.8.2
[5] rmarkdown_2.25 fs_1.6.3
[7] zlibbioc_1.48.0 vctrs_0.6.5
[9] memoise_2.0.1 RCurl_1.98-1.13
[11] terra_1.7-65 usethis_2.2.2
[13] htmltools_0.5.7 S4Arrays_1.2.0
[15] progress_1.2.3 curl_5.2.0
[17] Rhdf5lib_1.24.1 SparseArray_1.2.3
[19] rhdf5_2.46.1 KernSmooth_2.23-22
[21] htmlwidgets_1.6.4 cachem_1.0.8
[23] igraph_1.6.0 mime_0.12
[25] lifecycle_1.0.4 pkgconfig_2.0.3
[27] Matrix_1.6-4 R6_2.5.1
[29] fastmap_1.1.1 GenomeInfoDbData_1.2.11
[31] MatrixGenerics_1.14.0 shiny_1.8.0
[33] digest_0.6.33 colorspace_2.1-0
[35] AnnotationDbi_1.64.1 S4Vectors_0.40.2
[37] pkgload_1.3.3 GenomicRanges_1.54.1
[39] RSQLite_2.3.4 filelock_1.0.3
[41] progressr_0.14.0 fansi_1.0.6
[43] httr_1.4.7 abind_1.4-5
[45] compiler_4.3.2 proxy_0.4-27
[47] remotes_2.4.2.1 bit64_4.0.5
[49] withr_2.5.2 backports_1.4.1
[51] DBI_1.2.0 pkgbuild_1.4.3
[53] biomaRt_2.58.0 sessioninfo_1.2.2
[55] rappdirs_0.3.3 DelayedArray_0.28.0
[57] rjson_0.2.21 classInt_0.4-10
[59] GiottoVisuals_0.1.1 tools_4.3.2
[61] units_0.8-5 httpuv_1.6.13
[63] glue_1.6.2 rhdf5filters_1.14.1
[65] promises_1.2.1 grid_4.3.2
[67] sf_1.0-15 checkmate_2.3.1
[69] generics_0.1.3 gtable_0.3.4
[71] class_7.3-22 hms_1.1.3
[73] xml2_1.3.6 utf8_1.2.4
[75] XVector_0.42.0 BiocGenerics_0.48.1
[77] pillar_1.9.0 stringr_1.5.1
[79] later_1.3.2 dplyr_1.1.4
[81] BiocFileCache_2.10.1 lattice_0.21-9
[83] gmp_0.7-3 bit_4.0.5
[85] tidyselect_1.2.0 SingleCellExperiment_1.24.0
[87] Biostrings_2.70.1 miniUI_0.1.1.1
[89] knitr_1.45 IRanges_2.36.0
[91] SummarizedExperiment_1.32.0 stats4_4.3.2
[93] xfun_0.41 Biobase_2.62.0
[95] devtools_2.4.5 matrixStats_1.2.0
[97] stringi_1.8.3 yaml_2.3.8
[99] evaluate_0.23 codetools_0.2-19
[101] tibble_3.2.1 colorRamp2_0.1.0
[103] cli_3.6.2 arrow_14.0.0.2
[105] xtable_1.8-4 reticulate_1.34.0
[107] munsell_0.5.0 Rcpp_1.0.11
[109] GenomeInfoDb_1.38.5 dbplyr_2.4.0
[111] png_0.1-8 XML_3.99-0.16
[113] parallel_4.3.2 ellipsis_0.3.2
[115] ggplot2_3.4.4 assertthat_0.2.1
[117] blob_1.2.4 prettyunits_1.2.0
[119] ClusterR_1.3.2 profvis_0.3.8
[121] urlchecker_1.0.1 bitops_1.0-7
[123] SpatialExperiment_1.12.0 scales_1.3.0
[125] e1071_1.7-14 purrr_1.0.2
[127] crayon_1.5.2 rlang_1.1.2
[129] cowplot_1.1.2 KEGGREST_1.42.0