Importing Spatial Data

Importing Spatial Datasets

VoltRon is an end-to-end spatial omic analysis package that supports a large selection of spatial data resolutions. Currently, there exists a considerable amount of spatial omic technologies that generate datasets whose omic profiles are spatially resolved.

VoltRon objects are compatible with readouts of almost all of these technologies where we provide a selection of built-in functions to help users constructing VoltRon objects with ease. In this tutorial, we will review these spatial omic instruments and the functions available within the VoltRon package to import their readouts.

Visium (10x Genomics)

Image Credit: The Visium Spatial Gene Expression Slide (https://www.10xgenomics.com/)

10x Genomics Visium Spatial Gene Expression Platform incorporates in situ arrays (i.e. spots) to capture spatial localization of omic profiles where these spots are of 55 m in diameter and constitute a grid that covers a significant portion of a tissue section placed on the slide of the instrument.

We will use the readouts of Visium CytAssist platform that was derived from a single tissue section of a breast cancer sample. More information on the Visium CytAssist data and the study can be also be found on the BioArxiv preprint. You can download the data from the 10x Genomics website (specifically, import the Visium Spatial data).

We use the importVisium function to import the Visium readouts and create a VoltRon object. Here, we point to the folder of all the files with dir.path argument and also determine the name of this sample (sample_name).

library(VoltRon)
Vis_R1 <- importVisium(dir.path = "Visium/", sample_name = "VisiumR1")

VoltRon Object 
VisiumR1: 
  Layers: Section1 
Assays: Visium(Main) 

While importing the readouts, we can also determine the name of the assay as well as the name of the image. The SampleMetadata function summarizes the entire collection of assays, layers (sections) and samples (tissue blocks) within the R object.

Vis_R1 <- importVisium(dir.path = "Visium/", sample_name = "VisiumR1", 
                       assay_name = "Visium_assay", image_name = "H&E_stain")
SampleMetadata(Vis_R1)

              Assay    Layer   Sample
Assay1 Visium_assay Section1 VisiumR1

The current VoltRon object has only one assay associated with a single layer and a tissue block, and the image of this assay is currently the “H&E_stain”.

vrImageNames(Vis_R1)

[1] "H&E_stain"

Although by default the importVisium function selects the low resolution image, you can select the higher resolution one using resolution_level=“hires”

Vis_R1 <- importVisium(dir.path = "Visium/", sample_name = "VisiumR1", resolution_level="hires")

Xenium (10x Genomics)

Image Credit: https://www.biorxiv.org/content/10.1101/2022.10.06.510405v2

The 10x Genomics Xenium In Situ provides spatial localization of both (i) transcripts from a few hundred number of genes as well as (ii) the single cells with transcriptomic profiles.

We will use the readouts of a single Xenium platform replicate that was derived from a single tissue section of a breast cancer sample. More information on the Xenium data and the study can be also be found on the BioArxiv preprint. You can download the data from the 10x Genomics website (specifically, import the In Situ Replicate 1 data).

We use the importXenium function to import the Xenium readouts and create a VoltRon object. Here, we point to the folder of all the files with dir.path argument and also determine the name of this sample (sample_name).

library(VoltRon)
Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1")

VoltRon Object 
XeniumR1: 
  Layers: Section1 
Assays: Xenium(Main) 

You can use the import_molecules argument to import positions and features of the transcripts along with the single cell profiles.

Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1", import_molecules = TRUE)
Xen_R1

VoltRon Object 
XeniumR1: 
  Layers: Section1 
Assays: Xenium(Main) Xenium_mol 

The SampleMetadata function summarizes the entire collection of assays, layers (sections) and samples (tissue blocks) within the R object. In this case, the function will generate two assays in a single layer where one is a “cell” assay and the other is a “molecule assay”.

SampleMetadata(Xen_R1)

            Assay    Layer   Sample
Assay1     Xenium Section1 XeniumR1
Assay2 Xenium_mol Section1 XeniumR1

The Xenium in situ platform provides multiple resolution of the same Xenium slide which can be parsed from the OME.TIFF image file of DAPI stained tissue section (e.g. morphology_mip.ome.tif). The resolution_level argument determines the resolution of the DAPI image generated from the OME.TIFF file. More information on resolution levels can be found here.

Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1", import_molecules = TRUE, 
                       resolution_level = 4, overwrite_resolution = TRUE)
vrImages(Xen_R1, assay = "Xenium")

# A tibble: 1 × 7
  format width height colorspace matte filesize density
  <chr>  <int>  <int> <chr>      <lgl>    <int> <chr>  
1 PNG     4427   3222 Gray       FALSE        0 72x72  

Users can also decide to ignore OME.TIFF file and images, hence only cells and molecules would be imported.

Xen_R1 <- importXenium("Xenium_R1/outs", sample_name = "XeniumR1", import_molecules = TRUE, 
                       use_image = FALSE)

GeoMx (NanoString)

Image Credit: https://www.biochain.com/nanostring-geomx-digital-spatial-profiling/

The Nanostring’s GeoMx Digital Spatial Profiler is a high-plex spatial profiling technology which produces segmentation-based protein and RNA assays. The instrument allows users to select regions of interest (ROIs) from fluorescent microscopy images that capture the morphological context of the tissue. These are ROIs are then used to generate transcriptomic or proteomic profiles.

We will import the ROI profiles generated from the GeoMx scan area where COVID-19 lung tissues were fitted into. See GSE190732 for more information on this study.

Here is the usage of importGeoMx function and necessary files for this example:

Argument	Description	Link
dcc.path	The path to DCC files directory	DDC files
pkc.file	GeoMx™ DSP configuration file	Human RNA Whole Transcriptomic Atlas for NGS
summarySegment	Segment summary table (.xls or .csv)	ROI Metadata file
image	The Morphology Image of the scan area	Image file
ome.tiff	The OME.TIFF Image of the scan area	OME.TIFF file
	The OME.TIFF Image XML file	OME.TIFF (XML) file

library(VoltRon)
GeoMxR1 <- importGeoMx(dcc.path = "DCC-20230427/", 
                       pkc.file = "Hs_R_NGS_WTA_v1.0.pkc",
                       summarySegment = "segmentSummary.csv",
                       image = "Lu1A1B5umtrueexp.tif",
                       ome.tiff = "Lu1A1B5umtrueexp.ome.tiff",
                       sample_name = "GeoMxR1")

The OME.TIFF file here provides the ROI coordinates within the embedded XML metadata. We can also incorporate the RBioFormats package to extract the XML metadata from the OME.TIFF file.

# fix java parameters
options(java.parameters = "-Xmx4g")
library(RBioFormats)

# alternatively you can use RBioFormats to create an xml file
ome.tiff.xml <- RBioFormats::read.omexml("data/GeoMx/Lu1A1B5umtrueexp.ome.tiff")
write(ome.tiff.xml, file = "data/GeoMx/Lu1A1B5umtrueexp.ome.tiff.xml")

The ome.tiff argument also accepts the path to this XML file.

GeoMxR1 <- importGeoMx(dcc.path = "DCC-20230427/",
                       pkc.file = "Hs_R_NGS_WTA_v1.0.pkc",
                       summarySegment = "segmentSummary.csv",
                       image = "Lu1A1B5umtrueexp.tif",
                       ome.tiff = "Lu1A1B5umtrueexp.ome.tiff.xml",
                       sample_name = "GeoMxR1")

CosMx (NanoString)

Image Credit: https://www.biorxiv.org/content/10.1101/2021.11.03.467020v1.full

The Nanostring’s CosMx Spatial Molecular Imaging platform is a high-plex spatial multiomics technology that captures the spatial localization of both (i) transcripts from thousands of genes as well as (ii) the single cells with transcriptomic and proteomic profiles.

We will use the readouts from two slides of a single CosMx experiment. You can download the data from the Nanostring website.

We use the importCosMx function to import the CosMx readouts and create a VoltRon object. Here, we point to the folder of the TileDB array that stores feature matrix as well as the transcript metadata.

CosMxR1 <- importCosMx(tiledbURI = "MuBrainDataRelease/")

VoltRon Object 
Slide1: 
  Layers: Section1 
Slide2: 
  Layers: Section1 
Assays: CosMx(Main) 

You can use the import_molecules argument to import positions and features of the transcripts along with the single cell profiles.

CosMxR1 <- importCosMx(tiledbURI = "MuBrainDataRelease/", import_molecules = TRUE)

STOmics (MGI)

Image Credit: https://bgi-australia.com.au/stomics

Before importing the STOmics data to VoltRon, we first convert STOmics readouts to an h5ad file using the stereopy Python module. For more information, visit https://stereopy.readthedocs.io/. See here for instructions on how to install stereopy.

import stereo as st
import warnings
warnings.filterwarnings('ignore')

# read the GEF file
data_path = './SS200000135TL_D1.tissue.gef'
data = st.io.read_gef(file_path=data_path, bin_size=50)
data.tl.raw_checkpoint()

# remember to set flavor as scanpy
adata = st.io.stereo_to_anndata(data, flavor='scanpy', output='sample.h5ad')

We use the importSTOmics function to import the STOmics readouts and create a VoltRon object. Here, we point to the folder an h5ad file generated using the stereo.io.stereo_to_anndata method previously.

vrdata <- importSTOmics(h5ad.path = "sample.h5ad")

VoltRon Object 
Sample1: 
  Layers: Section1 
Assays: STOmics(Main) 

GenePS (Spatial Genomics)

Image Credit: https://spatialgenomics.com/applications/

We use the importGenePS function to import the GenePS (Spatial Genomics) readouts and create a VoltRon object. You can use the import_molecules argument to import positions and features of the transcripts along with the single cell profiles. The resolution_level argument determines the resolution of the DAPI image generated from the TIFF file.

vrdata <- importGenePS(dir.path = "out/", import_molecules = TRUE, resolution_level = 7)

VoltRon Object 
Sample1: 
  Layers: Section1 
Assays: GenePS(Main) GenePS_mol

PhenoCycler (Akoya)

Image Credit: https://tep.cancer.illinois.edu/phenocycler-fusion-system/

We use the importPhenoCycler function to import the PhenoCycler (Akoya Biosciences) readouts and create a VoltRon object. The function supports multiple readouts types depending on how the readouts were generated, this is controlled by the type arguement. For more information on all arguements of the function, see help(importPhenoCycler).

We used the Human FFPE tonsil tissue example with 83000 cells which could be found here. You have to download the contents and dir.path arguement should be set to the location of the Example-dataset-for-MAV folder.

vr_pheno <- importPhenoCycler(dir.path = "Example-dataset-for-MAV/", type = "processor", 
                              sample_name = "Tonsil18AB")

VoltRon Object 
Tonsil18AB: 
  Layers: Section1 
Assays: PhenoCycler(Main) 

OpenST

Image Credit: https://www.cell.com/cell/fulltext/S0092-8674(24)00636-6

We use the importOpenST function to import the OpenST https://rajewsky-lab.github.io/openst/latest/ readouts and create a VoltRon object. The function will parse each section from the output h5ad file, define it as an independent assay in a single layer where these layers are ordered in a single tissue block.

We use the metastatic lymph node example that is deposited to NCBI/GEO (GSE251926). Please use the GSE251926_metastatic_lymph_node_3d.h5ad.gz file and unzip it to use the importOpenST function below.

vr_openst <- importOpenST(h5ad.path = "GSE251926_metastatic_lymph_node_3d.h5ad", 
                          sample_name = "MLN_3D")

VoltRon Object 
MLN_3D: 
  Layers: Section1 Section2 Section3 Section4 Section5 Section6 Section7 Section8 Section9 Section10 Section11 Section12 Section13 Section14 Section15 Section16 Section17 Section18 Section19 
Assays: OpenST(Main) 

DBIT-Seq

Image Credit: https://www.cell.com/cell/fulltext/S0092-8674(20)31390-8

We use the importDBITSeq function to import the DBIT-Seq https://www.cell.com/cell/fulltext/S0092-8674(20)31390-8 readouts and create a VoltRon object. The default path to the rna count matrix is accompanied by the path to the protein count matrix, which is optional. The size parameter here determines the size of each square pixel on the DBIT-Seq slide (default is 10\(\mu\)m).

We use the example with developing eye field in a E10 mouse embryo using 10-μm microfluidic (sample id: 0719cL for RNA, and 0719aL for Protein) channels that is deposited to NCBI/GEO (GSE137986).

vr_dbit <- importDBITSeq(path.rna = "GSM4189615_0719cL.tsv", 
                         path.prot = "GSM4202309_0719aL.tsv", 
                         size = 10, sample_name = "E10_Eye_2", image_name = "main")

VoltRon Object 
E10_Eye_2: 
  Layers: Section1 
Assays: DBIT-Seq-RNA(Main) DBIT-Seq-Prot 

ST data

Image Credit: Ståhl, et. al (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 353(6294), 78-82.

We demonstrate importing the original Spatial Transcriptomics (ST) datasets by formulating custom spot-level spatial transcriptomics datasets. We will use the formVoltRon function directly. We use the example provided by the https://doi.org/10.5281/zenodo.4751624. For more information you can also check the paper here.

We first import and manipulate count matrices and spot coordinates from the provided output files. Here we only demonstrate building a custom spot-level VoltRon object for the section “A1”, however the remaining sections can be imported using the same method.

# count matrix
raw.data <- read.table("A1.tsv", header = TRUE, sep = "\t")
spatialentities <- raw.data$X
raw.data <- raw.data[,-1]
raw.data <- t(as.matrix(raw.data))

# coords 
coords <- read.table("A1_selection.tsv", header = TRUE, sep = "\t")
rownames(coords) <- paste(coords$x, coords$y, sep = "x")
coords <- coords[entities,]
coords <- coords[,c("pixel_x", "pixel_y")]
colnames(coords) <- c("x", "y")

The image can be imported using the magick package.

library(magick)
img <- magick::image_read("HE/A1.jpg")
img_info <- magick::image_info(img)

Before we form the VoltRon object, we should define the parameters for spot-level datasets. Here, we provide

• the radius of a spot in the physical space (i.e. spot.radius)
• the radius of a spot for visualization (i.e. vis.spot.radius)
• The distance to the nearest spot, to be used for spatial neighborhood calculation (i.e. nearestpost.distance)

The scaling parameter (scale_param) is required to overlay localization and distances of spots to the coordinate system of the imported image. Here,

• the width of a ST slide is of 6200\(\mu\)m,
• the diameter of a spot is 100\(\mu\)m (hence a radius of 50\(\mu\)m) and
• the center-to-center distance between two spots is 200\(\mu\)m.
• Each spot has at most 8 neighboring spots including vertically, horizontally and diagonally adjacent spots.

scale_param <- img_info$width/6200
params <- list(
  spot.radius = 50*(scale_param),
  vis.spot.radius = 100*(scale_param),
  nearestpost.distance = (200*sqrt(2) + 50)*scale_param
)

Now we can combine all components into a VoltRon object. We should also flip the coordinates of spots vertically after creating the object.

# make voltron object
stdata <- formVoltRon(data = datax, image = img, coords = coords, assay.type = "spot", 
                      params = params, sample_name = "A1")
stdata <- flipCoordinates(stdata)

We can now visualize spots and the adjacency between these spots simultaneously.

stdata <- getSpatialNeighbors(stdata, method = "radius")
vrSpatialPlot(stdata, graph.name = "radius", crop = T)

Custom VoltRon objects

VoltRon incorporates the formVoltRon function to assemble each component of a spatial omic assay into a VoltRon object. Here:

• the feature matrix: the pxn feature to point matrix for raw counts and omic profiles
• metadata: the metadata table
• image: An image or a list of images with names associated to channel
• coordinates: xy-Coordinates of spatial points
• segments: the list of xy-Coordinates of each spatial point

can individually be prepared before executing formVoltRon.

We will use a single image based proteomic assay to demonstrate building custom VoltRon objects. Specifically, we use cells characterized by multi-epitope ligand cartography (MELC) with a panel of 44 parameters. We use the already segmented cells on which expression of 43 protein features (excluding DAPI) were mapped to these cells.

VoltRon also provides support for imaging based proteomics assays. In this next use case, we analyze cells characterized by multi-epitope ligand cartography (MELC) with a panel of 44 parameters. We use the already segmented cells on which expression of 43 protein features (excluding DAPI) were mapped to these cells. You can download the files below here.

library(magick)

# feature x cell matrix
intensity_data <- read.table("intensities.tsv", sep = "\t")
intensity_data <- as.matrix(intensity_data)

# metadata
metadata <- read.table("metadata.tsv", sep = "\t")

# coordinates
coordinates <- read.table("coordinates.tsv", sep = "\t")
coordinates <- as.matrix(coordinates)

# image
library(magick)
image <- image_read("DAPI.tif")

# create VoltRon object
vr_object<- formVoltRon(data = intensity_data,
                        metadata = metadata,
                        image = image,
                        coords = coordinates,
                        main.assay = "MELC",
                        assay.type = "cell",
                        sample_name = "control_case_3",
                        image_name = "DAPI")
vr_object

VoltRon Object 
control_case_3: 
  Layers: Section1 
Assays: MELC(Main) 

VoltRon can store multiple images (or channels) associated with a single coordinate system.

library(magick)
image <- list(DAPI = image_read("DAPI.tif"),
              CD45 = image_read("CD45.tif"))

vr_object<- formVoltRon(data = intensity_data,
                        metadata = metadata,
                        image = image,
                        coords = coordinates,
                        main.assay = "MELC",
                        assay.type = "cell",
                        sample_name = "control_case_3",
                        image_name = "MELC")

These channels then can be interrogated and used as background images for spatial plots and spatial feature plots as well.

vrImageChannelNames(vr_object)

   Assay Image  Channels
1 Assay1  MELC DAPI,CD45

You can extract each of these channels individually.

vrImages(vr_object, name = "MELC", channel = "DAPI")
vrImages(vr_object, name = "MELC", channel = "CD45")

Image-only VoltRon objects

The formVoltRon function can also be used to build VoltRon objects where pixels (or tiles) are defined as spatial points. These information are derived from images only which then can be used for multiple downstream analysis purposes.

For this we incorporate importImageData function and only supply an image object. We will use the H&E image derived from a tissue section that was first analyzed by The 10x Genomics Xenium In Situ platform.

More information on the Xenium and the study can be also be found on the BioArxiv preprint. You can download the H&E image from the 10x Genomics website as well (specifically, import the Post-Xenium H&E image (TIFF) data).

Xen_R1_image <- importImageData("Xenium_FFPE_Human_Breast_Cancer_Rep1_he_image.tif",
                                sample_name = "XeniumR1image", 
                                image_name = "H&E")
Xen_R1_image

VoltRon Object 
XeniumR1image: 
  Layers: Section1 
Assays: ImageData(Main) 

vrImages(Xen_R1_image)