Importing Spatial Data
Working with VoltRon objects
In this tutorial, we will cover some of the fundemantel built-in functions of VoltRon to manage data, images and spatial information of assays. Here is the list of example datasets included in the Package.
data("visium_data")
data("melc_data")
data("xenium_data")Sample Metadata
The sample metadata is a summarized data frame which provides informations on assays, layers and sample (tissue blocks) included in the VoltRon object. You can use the SampleMetadata function to call this table. The row names are the unique assay IDs, following assay types (e.g. Visium), layer name and Sample (typicall tissue block with multiple layers and assays).
SampleMetadata(visium_data) Assay Layer Sample
Assay1 Visium Section1 Anterior1Metadata
The Metadata function calls the metadata table of the main assay type (see vrMainAssay).
Metadata(visium_data) Count Assay Layer Sample
AAAGGCTCTCGCGCCG-1_Assay1 39690 Visium Section1 Anterior1
AAATGGCCCGTGCCCT-1_Assay1 33516 Visium Section1 Anterior1
AAATTACACGACTCTG-1_Assay1 12250 Visium Section1 Anterior1
AAGACATACGTGGTTT-1_Assay1 31821 Visium Section1 Anterior1
ACCTACTATAAATCTA-1_Assay1 41387 Visium Section1 Anterior1
ACGCGGGCCAAGGACA-1_Assay1 48003 Visium Section1 Anterior1You can also specify the assay ID or the assay type to call metadata of a subset of spatial points.
Metadata(visium_data, assay = "Visium")
Metadata(visium_data, assay = "Assay1")The type argument returns all spatial points of a given spatial point type (ROI, spot, cell, molecule or tile).
Metadata(visium_data, type = "spot")If the type argument is specified as all. Then this would return the VoltRon Metadata Object (vrMetadata)
Metadata(visium_data, type = "all")VoltRon Metadata Object
This object includes:
100 spotsSpatial Points
In a VoltRon object, spatial points are spatially defined entities with coordinates, segments which are of any of five assay types (ROI, spot, cell, molecule and tile).
The vrSpatialPoints function return the IDs of these entities for further downstream operations, such as subsetting etc.
vrSpatialPoints(visium_data) [1] "AAAGGCTCTCGCGCCG-1_Assay1" "AAATGGCCCGTGCCCT-1_Assay1" "AAATTACACGACTCTG-1_Assay1" You can also specify the assay ID or the assay type to call metadata of a subset of spatial points.
vrSpatialPoints(visium_data, assay = "Visium")
vrSpatialPoints(visium_data, assay = "Assay1") [1] "AAAGGCTCTCGCGCCG-1_Assay1" "AAATGGCCCGTGCCCT-1_Assay1" "AAATTACACGACTCTG-1_Assay1" Assays
The default (or main) assay of the VoltRon object is typically shown when printed (next to the assay name says “main” in paranthesis)
melc_dataVoltRon Object
control_case_3:
Layers: Section1
Assays: MELC(Main) You can also call/get the name of the default (or main) using the vrMainAssay function
vrMainAssay(melc_data)[1] "MELC"You can also set the main assay yourself, but only the assay types given in the Assay column of SampleMetadata(object).
SampleMetadata(melc_data) Assay Layer Sample
Assay1 MELC Section1 control_case_3vrMainAssay(melc_data) <- "MELC"You can also get the assay IDs associated with the main assay using vrAssayNames function.
vrAssayNames(melc_data)[1] "Assay1"The assay type can be provided with the assay arguement to get type specific assay IDs
vrAssayNames(melc_data, assay = "MELC")[1] "Assay1"Coordinates and Segments
The vrCoordinates function is used to call the centroids of spots, cells and all other spatial points types with VoltRon objects.
vrCoordinates(visium_data) x y
AAAGGCTCTCGCGCCG-1_Assay1 271.9105 183.1497
AAATGGCCCGTGCCCT-1_Assay1 136.8330 220.2237
AAATTACACGACTCTG-1_Assay1 382.0482 436.6781
AAGACATACGTGGTTT-1_Assay1 481.5491 102.7539
ACCTACTATAAATCTA-1_Assay1 282.5473 102.7539
ACGCGGGCCAAGGACA-1_Assay1 296.7470 313.0120You can also specify the assay ID or the assay type to call coordinates of a subset of spatial points.
vrCoordinates(visium_data, assay = "Visium")
vrCoordinates(visium_data, assay = "Assay1")Each assay a VoltRon object may incorporate indefinite number of coordinate systems. One can look for these coordinate systems using the vrMainSpatial function, and select one of systems to call coordinates (see vrMainSpatial)
vrCoordinates(visium_data, spatial_name = "main")The reg option in the vrCoordinates function looks for a registered version of the main coordinate system and returns its coordinates (if there is any).
vrCoordinates(visium_data, spatial_name = "main", reg = TRUE) x y
AAAGGCTCTCGCGCCG-1_Assay1 271.9105 183.1497
AAATGGCCCGTGCCCT-1_Assay1 136.8330 220.2237
AAATTACACGACTCTG-1_Assay1 382.0482 436.6781
AAGACATACGTGGTTT-1_Assay1 481.5491 102.7539
ACCTACTATAAATCTA-1_Assay1 282.5473 102.7539
ACGCGGGCCAAGGACA-1_Assay1 296.7470 313.0120
Warning message:
In vrCoordinates.vrAssay(object[[assy]], spatial_name = spatial_name, :
There are no registered spatial systems with name main!The arguements of the vrSegments functions are idential to vrCoordinates and return a list of polygon corners associated with the coordinate system of the coordinates.
vrSegments(xenium_data)
vrSegments(xenium_data, assay = "Xenium")
vrSegments(xenium_data, assay = "Assay1")
vrSegments(xenium_data, spatial_name = "main")
vrSegments(xenium_data, spatial_name = "main", reg = TRUE)$`77197_Assay1`
# A tibble: 13 × 3
cell_id x y
<int> <dbl> <dbl>
1 77197 490. 379.
2 77197 487 378.
3 77197 486. 373.
4 77197 485. 366.
5 77197 492. 364.
6 77197 496. 360.
7 77197 504. 354.
8 77197 505. 354.
9 77197 505 356.
10 77197 500. 366.
11 77197 492. 374.
12 77197 490. 379.
13 77197 490. 379.
$`77200_Assay1`
# A tibble: 13 × 3
cell_id x y
<int> <dbl> <dbl>
1 77200 493. 383.
2 77200 490. 380.
3 77200 491. 376.
4 77200 493. 374.
5 77200 500. 366.
6 77200 505 356.
7 77200 510. 361.
8 77200 510. 366.
9 77200 509. 369.
10 77200 505. 372.
11 77200 502. 376.
12 77200 498 379.
13 77200 493. 383.Images and Spatial Systems
Below we review some of the essential built-in functions to manipulate and manage images of a VoltRon object.
Spatial system names
In a VoltRon object, each image has a unique ID which is also associated with a coordinate (or spatial) system. The names of these spatial systems can be called using the vrSpatialNames function.
vrSpatialNames(visium_data)[1] "main"You can also specify the assay ID or the assay type to call image names of a subset assays or any assay type (see Sample Metadata)
vrSpatialNames(visium_data, assay = "Visium")
vrSpatialNames(visium_data, assay = "Assay1")[1] "main"Channel names
Each spatial system object (vrSpatial) in VoltRon object can also include a indefinite number of channels which you can get (or request) a list of for further use.
vrImageChannelNames(melc_data)
vrImageChannelNames(melc_data, assay = "MELC")
vrImageChannelNames(melc_data, assay = "Assay1") Assay Spatial Channels
1 Assay1 MELC DAPI,CD45Default spatial systems
If you wanna see the list of all spatial systems associated with all assays of the main assay type, you can call the vrMainSpatial function.
vrMainSpatial(visium_data) Assay Spatial
1 Assay1 mainYou can also specify the assay ID or the assay type to call spatial system names of a subset assays or any assay type (see Sample Metadata)
vrMainSpatial(visium_data, assay = "Visium")
vrMainSpatial(visium_data, assay = "Assay1") Assay Spatial
1 Assay1 mainGet and set images
In VoltRon, images can be called specifically, or return as a list. The return image is of a magick image object (see magick package)
vrImages(visium_data)
vrImages(visium_data, assay = "Visium")
vrImages(visium_data, assay = "Assay1")
Once you know the name of a specific channel, you can the image of a specific channel by providing the name and the associated channel.
vrImages(melc_data, name = "MELC", channel = "DAPI")
You can set up the main channel as well as the main spatial system name for later use.
vrMainSpatial(melc_data, assay = "Assay1") <- c("MELC", "CD45")
vrImages(melc_data)
You can also resize the images as they are being returned. This is usually used for visualization purposes and helps speeding up visualization for large images. This is accomplished with scale.perc arguement.
vrImages(melc_data, scale.perc = 25)Combining image channels
VoltRon even allows manipulation of channel images if you also provide an associate list of colors.
melc_data <- combineChannels(melc_data,
channels = c("DAPI", "CD45"), colors = c("grey", "green"),
channel_key = "combined")These new images can be stored as new channels within the same image object, and called later again
vrImageChannelNames(melc_data) Assay Spatial Channels
1 Assay1 MELC DAPI,CD45,combinedvrImages(melc_data, channel = "combined")Feature Matrix (Data)
vrData(visium_data)[3:8,3:5] AAATTACACGACTCTG-1_Assay1 AAGACATACGTGGTTT-1_Assay1 ACCTACTATAAATCTA-1_Assay1
Gm19938 0 0 0
Gm37381 0 0 0
Rp1 0 0 0
Sox17 0 1 1
Gm37587 0 0 0
Gm37323 0 0 0vrData(visium_data, norm = TRUE)[3:8,3:5] AAATTACACGACTCTG-1_Assay1 AAGACATACGTGGTTT-1_Assay1 ACCTACTATAAATCTA-1_Assay1
Gm19938 0 0.0000000 0.0000000
Gm37381 0 0.0000000 0.0000000
Rp1 0 0.0000000 0.0000000
Sox17 0 0.2732722 0.2164184
Gm37587 0 0.0000000 0.0000000
Gm37323 0 0.0000000 0.0000000Embeddings
You can parse and even set individual embedding elements in a VoltRon object.
vrEmbeddingNames(xenium_data)[1] "pca" "umap"You can use these names of get the associated embedding dataset from the object.
vrEmbeddings(xenium_data, type = "umap") x y
171_Assay1 -7.253258 0.3016544
180_Assay1 5.242596 4.3041073
182_Assay1 3.705179 4.8671747
185_Assay1 5.239327 4.2822548
197_Assay1 -4.665270 2.8520897
206_Assay1 -1.887516 4.0298878You can also set and create new embedding elements in the voltron object. In this case, you have to make sure that the row names should match with the targeted spatial points.
new_umap_data <- vrEmbeddings(xenium_data, type = "umap")
vrEmbeddings(xenium_data, type = "new_umap") <- new_umap_data*2Now we can observe changes to the new embedding data.
vrEmbeddings(xenium_data, type = "new_umap") x y
171_Assay1 -14.506517 0.6033088
180_Assay1 10.485192 8.6082145
182_Assay1 7.410358 9.7343494
185_Assay1 10.478653 8.5645096
197_Assay1 -9.330539 5.7041794
206_Assay1 -3.775032 8.0597755
[1] "pca" "umap" "new_umap"You can choose invidiual assay names or assay classes.
vrEmbeddings(xenium_data, type = "umap", assay = "Xenium")
vrEmbeddings(xenium_data, type = "pca", assay = "Assay1")Subsetting VoltRon objects
sample/assay
VoltRon object can be subsetted in a variety of ways using assay names, sample names, spatial point names, features (e.g. gene), image coordinates (crop boxes or bounding boxes) as well as interactively.
visium_data <- subset(visium_data, samples = "Anterior1")
visium_data <- subset(visium_data, assays = "Assay1")VoltRon Object
Anterior1:
Layers: Section1
Assays: Visium(Main) spatial points
You can use a list of spatial points (typically using vrSpatialPoints function, see vrSpatialPoints)
selected_points <- vrSpatialPoints(visium_data)
selected_points[1:20] [1] "AAAGGCTCTCGCGCCG-1_Assay1" "AAATGGCCCGTGCCCT-1_Assay1" "AAATTACACGACTCTG-1_Assay1" visium_data <- subset(visium_data, spatialpoints = selected_points[1:20])VoltRon Object
Anterior1:
Layers: Section1
Assays: Visium(Main) features
You can select a few number of features and subset the features given this list. However, it would only subset the main assay (see vrMainAssay)
selected_features <- vrFeatures(visium_data)
selected_features[1:20] [1] "Xkr4" "Gm1992" "Gm19938" "Gm37381" "Rp1" "Sox17" "Gm37587" "Gm37323" "Mrpl15" "Lypla1"
[11] "Tcea1" "Rgs20" "Gm16041" "Atp6v1h" "Oprk1" "Npbwr1" "Rb1cc1" "4732440D04Rik" "Alkal1" "St18"visium_data <- subset(visium_data, features = selected_features[1:20])
vrFeatures(visium_data) [1] "Xkr4" "Gm1992" "Gm19938" "Gm37381" "Rp1" "Sox17" "Gm37587" "Gm37323" "Mrpl15" "Lypla1"
[11] "Tcea1" "Rgs20" "Gm16041" "Atp6v1h" "Oprk1" "Npbwr1" "Rb1cc1" "4732440D04Rik" "Alkal1" "St18"Interactive subsetting
VoltRon allows interactively subsetting spatial data. Using the arguement interactive = TRUE, a mini Shiny app is triggered where users can select a bounding box to crop the spatial data.
visium_data_subset_info <- subset(visium_data, interactive = TRUE)
visium_data_subset <- visium_data_subset_info$subsets[[1]]VoltRon Object
Anterior1_subset:
Layers: Section1
Assays: Visium(Main) vrImages(visium_data_subset)Visualization
VoltRon provides visualization utilities for both spatial and embedding level visualizations.
Spatial Plots
vrSpatialPlot is the main function for visualize labels and identities of cells. This information is parsed from the Metadata of the main assay (see vrMainAssay). The users can also specify the assay.
vrSpatialPlot(xenium_data, group.by = "clusters")
You can also visualize the segments of spatial points and even get the segments transparent
vrSpatialPlot(xenium_data, group.by = "clusters", plot.segments = TRUE, alpha = 0.6)
The background information can be set to either a “white” or “black” background.
vrSpatialPlot(xenium_data, group.by = "clusters", plot.segments = TRUE,
background = "white")
In cases where there are multiple coordinate systems, you can get the background set to the name of the image (or coordinate system). You can get the name of these images from vrSpatialNames.
If also want to select a channel from the same coordinate system, you can set background arguement as a vector of 2 where first is the name of the image (coordinate system) and the other would be channel name. You can get the name of these channels from vrImageChannelNames.
vrSpatialPlot(xenium_data, group.by = "clusters", plot.segments = TRUE,
background = c("main"))
vrSpatialPlot(xenium_data, group.by = "clusters", plot.segments = TRUE,
background = c("main", "DAPI"))If the visualized assay is of a type “spot”, you can crop the image to encapsulate the smallest subset that include all spots.
vrSpatialPlot(visium_data, group.by = "Sample", crop = TRUE)
The vrSpatialFeaturePlot functions the same way as vrSpatialPlot but requires extra arguements such as features for selecting features, norm for normalized expression (default), and log for log transformed counts.
vrSpatialFeaturePlot(visium_data, features = "Count")
vrSpatialFeaturePlot(visium_data, features = "Stat1", norm = TRUE, log = TRUE)
For all variations of vrSpatialPlot and vrSpatialFeaturePlot functions above, you can specifiy the assay names or assay class.
vrSpatialPlot(visium_data, assay = "Visium", plot.segments = TRUE)
vrSpatialPlot(visium_data, assay = "Assay1", plot.segments = TRUE)
vrSpatialFeaturePlot(visium_data, assay = "Visium", features = "Count")
vrSpatialFeaturePlot(visium_data, assay = "Assay1", features = "Count")Embedding Plots
Dimensional reduction and embedding features are also possible for the VoltRon objects.
vrEmbeddingPlot(xenium_data, embedding = "umap", group.by = "clusters", label = T)
The vrEmbeddingFeaturePlot functions the same way as vrEmbeddingPlot but requires extra arguements such as features for selecting features, norm for normalized expression (default), and log for log transformed counts.
vrEmbeddingFeaturePlot(xenium_data, embedding = "umap", features = "Count")
vrEmbeddingFeaturePlot(xenium_data, embedding = "umap", features = "KRT5", norm = FALSE)
For all variations of vrEmbeddingPlot and vrEmbeddingFeaturePlot functions above, you can specifiy the assay names or assay class.
vrEmbeddingPlot(xenium_data, assay = "Xenium", embedding = "umap")
vrEmbeddingPlot(xenium_data, assay = "Assay1", embedding = "umap")
vrEmbeddingFeaturePlot(xenium_data, assay = "Xenium", embedding = "umap", features = "Count")
vrEmbeddingFeaturePlot(xenium_data, assay = "Assay1", embedding = "umap", features = "Count")