Introduction to visiumStitched

Nicholas J. Eagles1* and Leonardo Collado-Torres1,2,3**

1Lieber Institute for Brain Development
2Center for Computational Biology, Johns Hopkins University
3Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

*nickeagles77@gmail.com
**lcolladotor@gmail.com

Package

visiumStitched 0.99.15

1 Basics

1.1 Install visiumStitched

visiumStitched is a Bioconductor R package that can be installed with the following commands in your R session:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("visiumStitched")

1.2 Citing visiumStitched

We hope that visiumStitched will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!

## Citation info
citation("visiumStitched")
#> To cite package 'visiumStitched' in publications use:
#> 
#>   Eagles NJ, Collado-Torres L (2024). _Enable downstream analysis of
#>   Visium capture areas stitched together with Fiji_.
#>   doi:10.18129/B9.bioc.visiumStitched
#>   <https://doi.org/10.18129/B9.bioc.visiumStitched>,
#>   https://github.com/LieberInstitute/visiumStitched/visiumStitched - R
#>   package version 0.99.15,
#>   <http://www.bioconductor.org/packages/visiumStitched>.
#> 
#>   Eagles NJ, Bach S, Tippani M, Ravichandran P, Du Y, Miller RA, Hyde
#>   TM, Page SC, Martinowich K, Collado-Torres L (2024).
#>   "visiumStitched." _BMC Genomics_. doi:10.1186/s12864-024-10991-y
#>   <https://doi.org/10.1186/s12864-024-10991-y>,
#>   <doi.org/10.1186/s12864-024-10991-y>.
#> 
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.

1.3 Packages used in this vignette

Let’s load the packages we’ll use in this vignette.

library("SpatialExperiment")
library("visiumStitched")
library("dplyr")
library("spatialLIBD")
library("BiocFileCache")
library("ggplot2")

2 Preparing Experiment Information

Much of the visiumStitched package uses a tibble (or data.frame) defining information about the experiment. Most fundamentally, the group column allows you to line up which capture areas, in the capture_area column, are to be stitched together later. In our case, we have just one unique group, consisting of all three capture areas. Note multiple groups are supported. By the end of this demo, the SpatialExperiment will consist of just one sample composed of the three capture areas; in general, there will be one sample per group.

## Create initial sample_info
sample_info <- data.frame(
    group = "Br2719",
    capture_area = c("V13B23-283_A1", "V13B23-283_C1", "V13B23-283_D1")
)

## Initial sample_info
sample_info
#>    group  capture_area
#> 1 Br2719 V13B23-283_A1
#> 2 Br2719 V13B23-283_C1
#> 3 Br2719 V13B23-283_D1

Next, we’ll need the Spaceranger outputs for each capture area, which can be retrieved with spatialLIBD::fetch_data().

## Download example SpaceRanger output files
sr_dir <- tempdir()
temp <- unzip(spatialLIBD::fetch_data("visiumStitched_brain_spaceranger"),
    exdir = sr_dir
)
#> 2024-12-18 19:29:02.702686 loading file /home/biocbuild/.cache/R/BiocFileCache/37ddd515d59008_visiumStitched_brain_spaceranger.zip%3Frlkey%3Dbdgjc6mgy1ierdad6h6v5g29c%26dl%3D1
sample_info$spaceranger_dir <- file.path(
    sr_dir, sample_info$capture_area, "outs", "spatial"
)

## Sample_info with paths to SpaceRanger output directories
sample_info
#>    group  capture_area                            spaceranger_dir
#> 1 Br2719 V13B23-283_A1 /tmp/RtmpsS1gYF/V13B23-283_A1/outs/spatial
#> 2 Br2719 V13B23-283_C1 /tmp/RtmpsS1gYF/V13B23-283_C1/outs/spatial
#> 3 Br2719 V13B23-283_D1 /tmp/RtmpsS1gYF/V13B23-283_D1/outs/spatial

3 Preparing Inputs to Fiji

The visiumStitched workflow makes use of Fiji, a distribution of the ImageJ image-processing software, which includes an interface for aligning images on a shared coordinate system. Before aligning anything in Fiji, we need to ensure that images to align from all capture areas are on the same scale– that is, a pixel in each image represents the same distance. This is typically approximately true, but is not guaranteed to be exactly true, especially when the capture areas to align come from different Visium slides. rescale_fiji_inputs() reads in the high-resolution tissue images for each capture area, and uses info about their spot diameters in pixels and scale factors to rescale the images appropriately (even if they are from different Visium slides).

For demonstration purposes, we’ll set out_dir to a temporary location. Typically, it would really be any suitable directory to place the rescaled images for later input to Fiji.

#   Generate rescaled approximately high-resolution images
sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())

## Sample_info with output directories
sample_info
#> # A tibble: 3 × 5
#>   group  capture_area  spaceranger_dir     intra_group_scalar group_hires_scalef
#>   <chr>  <chr>         <chr>                            <dbl>              <dbl>
#> 1 Br2719 V13B23-283_A1 /tmp/RtmpsS1gYF/V1…               1.00             0.0825
#> 2 Br2719 V13B23-283_C1 /tmp/RtmpsS1gYF/V1…               1.00             0.0825
#> 3 Br2719 V13B23-283_D1 /tmp/RtmpsS1gYF/V1…               1                0.0825

4 Building a SpatialExperiment

4.1 Stitching Images with Fiji

Before building a SpatialExperiment for a stitched dataset, we must align the images for each group in Fiji. Check out this video for a guide through this process with the example data.

4.2 Creating Group-Level Samples

From the Fiji alignment, two output files will be produced: an XML file specifying rigid affine transformations for each capture area, and the stitched approximately high-resolution image. These files for this dataset are available through spatialLIBD::fetch_data(). We’ll need to add the paths to the XML and PNG files to the fiji_xml_path and fiji_image_path columns of sample_info, respectively.

fiji_dir <- tempdir()
temp <- unzip(fetch_data("visiumStitched_brain_Fiji_out"), exdir = fiji_dir)
#> 2024-12-18 19:29:16.151889 loading file /home/biocbuild/.cache/R/BiocFileCache/37ddd56d56041e_visiumStitched_brain_fiji_out.zip%3Frlkey%3Dptwal8f5zxakzejwd0oqw0lhj%26dl%3D1
sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
sample_info$fiji_image_path <- temp[grep("png$", temp)]

We now have every column present in sample_info that will be necessary for any visiumStitched function.

## Complete sample_info
sample_info
#> # A tibble: 3 × 7
#>   group  capture_area  spaceranger_dir     intra_group_scalar group_hires_scalef
#>   <chr>  <chr>         <chr>                            <dbl>              <dbl>
#> 1 Br2719 V13B23-283_A1 /tmp/RtmpsS1gYF/V1…               1.00             0.0825
#> 2 Br2719 V13B23-283_C1 /tmp/RtmpsS1gYF/V1…               1.00             0.0825
#> 3 Br2719 V13B23-283_D1 /tmp/RtmpsS1gYF/V1…               1                0.0825
#> # ℹ 2 more variables: fiji_xml_path <chr>, fiji_image_path <chr>

Before building the SpatialExperiment, the idea is to create a directory structure very similar to Spaceranger’s spatial outputs for each group, as opposed to the capture-area-level directories we already have. We’ll place this directory in a temporary location that will later be read in to produce the final SpatialExperiment.

First, prep_fiji_coords() will apply the rigid affine transformations specified by Fiji’s output XML file to the spatial coordinates, ultimately producing a group-level tissue_positions.csv file. Next, prep_fiji_image() will rescale the stitched image to have a default of 1,200 pixels in the longest dimension. The idea is that in an experiment with multiple groups, the images stored in the SpatialExperiment for any group will be similarly scaled and occupy similar memory footprints.

## Prepare the Fiji coordinates and images.
## These functions return the file paths to the newly-created files that follow
## the standard directory structure from SpaceRanger (10x Genomics)
spe_input_dir <- tempdir()
prep_fiji_coords(sample_info, out_dir = spe_input_dir)
#> [1] "/tmp/RtmpsS1gYF/Br2719/tissue_positions.csv"
prep_fiji_image(sample_info, out_dir = spe_input_dir)
#> [1] "/tmp/RtmpsS1gYF/Br2719/tissue_lowres_image.png"
#> [2] "/tmp/RtmpsS1gYF/Br2719/scalefactors_json.json"

4.3 Constructing the Object

We now have all the pieces to create the SpatialExperiment object. After constructing the base object, information related to how spots may overlap between capture areas in each group is added. The sum_umi metric will by default determine which spots in overlapping regions to exclude in plots. In particular, at regions of overlap, spots from capture areas with higher average UMI (unique molecular identifier) counts will be plotted, while any other spots will not be shown using spatialLIBD::vis_clus(), spatialLIBD::vis_gene(), and related visualization functions. We’ll also mirror the image and gene-expression data to match the orientation specified at the wet bench. More info about performing geometric transformations is here.

## Download the Gencode v32 GTF file which is the closest one to the one
## that was used with SpaceRanger. Note that SpaceRanger GTFs are available at
## https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-GRCh38-2024-A.tar.gz
## but is too large for us to download here since it includes many other files
## we don't need right now.
## However, ideally you would adapt this code and use the actual GTF file you
## used when running SpaceRanger.
bfc <- BiocFileCache::BiocFileCache()
gtf_cache <- bfcrpath(
    bfc,
    paste0(
        "ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/",
        "release_32/gencode.v32.annotation.gtf.gz"
    )
)

## Now we can build the SpatialExperiment object. We'll later explore error
## metrics related to computing new array coordinates, and thus specify
## 'calc_error_metrics = TRUE'.
spe <- build_SpatialExperiment(
    sample_info,
    coords_dir = spe_input_dir, reference_gtf = gtf_cache,
    calc_error_metrics = TRUE
)
#> Building SpatialExperiment using capture area as sample ID
#> 2024-12-18 19:29:21.31045 SpatialExperiment::read10xVisium: reading basic data from SpaceRanger
#> 2024-12-18 19:29:32.607145 read10xVisiumAnalysis: reading analysis output from SpaceRanger
#> 2024-12-18 19:29:33.236353 add10xVisiumAnalysis: adding analysis output from SpaceRanger
#> 2024-12-18 19:29:33.656953 rtracklayer::import: reading the reference GTF file
#> 2024-12-18 19:30:14.322575 adding gene information to the SPE object
#> Warning: Gene IDs did not match. This typically happens when you are not using
#> the same GTF file as the one that was used by SpaceRanger. For example, one
#> file uses GENCODE IDs and the other one ENSEMBL IDs. read10xVisiumWrapper()
#> will try to convert them to ENSEMBL IDs.
#> Warning: Dropping 2226 out of 38606 genes for which we don't have information
#> on the reference GTF file. This typically happens when you are not using the
#> same GTF file as the one that was used by SpaceRanger.
#> 2024-12-18 19:30:16.020891 adding information used by spatialLIBD
#> Overwriting imgData(spe) with merged images (one per group)
#> Adding array coordinates with error metrics and adding overlap info

## The images in this example data have to be mirrored across the horizontal axis.
spe <- SpatialExperiment::mirrorObject(spe, axis = "h")

## Explore stitched spe object
spe
#> class: SpatialExperiment 
#> dim: 36380 14976 
#> metadata(0):
#> assays(1): counts
#> rownames(36380): ENSG00000243485.5 ENSG00000237613.2 ...
#>   ENSG00000198695.2 ENSG00000198727.2
#> rowData names(6): source type ... gene_type gene_search
#> colnames(14976): AAACAACGAATAGTTC-1_V13B23-283_A1
#>   AAACAAGTATCTCCCA-1_V13B23-283_A1 ... TTGTTTGTATTACACG-1_V13B23-283_D1
#>   TTGTTTGTGTAAATTC-1_V13B23-283_D1
#> colData names(33): sample_id in_tissue ... overlap_key
#>   exclude_overlapping
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor

The colData(spe)$exclude_overlapping column controls which spots to drop for visualization purposes. Note also that the overlap_key column was added, which gives a comma-separated string of spot keys overlapping each given spot, or the empty string otherwise. After spatial clustering, the overlap_key information can be useful to check how frequently overlapping spots are assigned the same cluster.

## Examine spots to exclude for plotting
table(spe$exclude_overlapping)
#> 
#> FALSE  TRUE 
#> 13426  1550

5 Examining the stitched data

5.1 Stitched plotting

To demonstrate that we’ve stitched both the gene expression and image data successfully, we’ll use spatialLIBD::vis_gene(is_stitched = TRUE) (version 1.17.8 or newer) to plot the distribution of white matter spatially. For more context on human brain white matter spatial marker genes, check our previous work on this subject.

## Show combined raw expression of white-matter marker genes
wm_genes <- rownames(spe)[
    match(c("MBP", "GFAP", "PLP1", "AQP4"), rowData(spe)$gene_name)
]
vis_gene(spe, geneid = wm_genes, assayname = "counts", is_stitched = TRUE)

Note that we’re plotting raw counts; prior to normalization, library-size variation across spots can bias the apparent distribution. Later, we’ll show that normalization is critical to producing a visually seamless transition between overlapping capture areas.

5.2 Defining Array Coordinates

Given that the stitched data is larger than a default Visium capture area, add_array_coords() (which is used internally by build_SpatialExperiment()) recomputed the array coordinates (i.e. spe$array_row and spe$array_col) to more sensibly index the stitched data.

Let’s explain this in more detail. By definition, these array coordinates (see documentation from 10X) are integer indices of each spot on a Visium capture area, numbering the typically 78 and 128 rows and columns, respectively, for a 6.5mm capture area. The build_SpatialExperiment() function retains each capture area’s original array coordinates, spe$array_row_original and spe$array_col_original, but these are typically not useful to represent our group-level, stitched data. In fact, each stitched capture area has the same exact array coordinates, despite having different spatial positions after stitching. We’ll take in-tissue spots only and use transparency to emphasize the overlap among capture areas:

## Plot positions of default array coordinates, before overwriting with more
## meaningful values. Use custom colors for each capture area
ca_colors <- c("#A33B20", "#e7bb41", "#3d3b8e")
names(ca_colors) <- c("V13B23-283_C1", "V13B23-283_D1", "V13B23-283_A1")

colData(spe) |>
    as_tibble() |>
    filter(in_tissue) |>
    ggplot(
        mapping = aes(
            x = array_row_original, y = array_col_original, color = capture_area
        )
    ) +
    geom_point(alpha = 0.3) +
    scale_color_manual(values = ca_colors)

Let’s contrast this with the array coordinates recomputed by visiumStitched. Briefly, visiumStitched forms a new hexagonal, Visium-like grid spanning the space occupied by all capture areas after stitching. Then, the true spot positions are fit to the nearest new spot positions, in terms of Euclidean distance. Finally, array coordinates are re-indexed according to the new spot assignments, resulting in spatially meaningful values that apply at the group level for stitched data.

## Plot positions of redefined array coordinates
colData(spe) |>
    as_tibble() |>
    filter(in_tissue) |>
    ggplot(
        mapping = aes(
            x = array_row, y = array_col, color = capture_area
        )
    ) +
    geom_point(alpha = 0.3) +
    scale_color_manual(values = ca_colors)

An important downstream application of these array coordinates, is that it enables methods that rely on the hexagonal grid structure of Visium to find more than the original six neighboring spots. This enables clustering with BayesSpace or PRECAST, to treat each group as a spatially continuous sample. We can see here how BayesSpace:::.find_neighbors() version 1.11.0 uses the hexagonal Visium grid properties to find the spot neighbors. See also BayesSpace Figure 1b for an illustration of this process.

Yet, it doesn’t matter if there are actually two or more spots on each of those six neighbor positions. visiumStitched takes advantage of this property to enable BayesSpace and other spatially-aware clustering methods to use data from overlapping spots when performing spatial clustering. You can then use colData(spe)$overlap_key to inspect whether overlapping spots were assigned to the same spatial cluster.

5.2.1 Error metrics

No algorithm can fit a set of capture areas’ spots onto a single hexagonal grid without some error. Here, we define a spot’s error in being assigned new array coordinates with two independent metrics, which are stored in spe$euclidean_error and spe$shared_neighbors if the user opts to compute them with build_SpatialExperiment(calc_error_metrics = TRUE). The latter metric can take a couple minutes to compute, and thus by default the metrics are not computed.

The first metric is the Euclidean distance, in multiples of 100 microns (the distance between spots on a Visium capture area), between a spot’s original position and the position of its assigned array coordinates.

#   Explore the distribution of Euclidean error
colData(spe) |>
    as_tibble() |>
    ggplot(mapping = aes(x = 0, y = euclidean_error)) +
    geom_boxplot()

The other metric, spe$shared_neighbors, measures the fraction of original neighbors (from a same capture area) that are retained after mapping to the new array coordinates. Thus, a value of 1 is ideal.

#   Explore the distribution of Euclidean error
colData(spe) |>
    as_tibble() |>
    ggplot(mapping = aes(x = 0, y = shared_neighbors)) +
    geom_boxplot()

In theory, error as measured through these metrics could have a very slight impact on the quality of clustering results downstream. We envision interested users in checking these metrics when interpreting specific spots’ cluster assignments downstream.

6 Downstream applications

One common area of analysis in spatial transcriptomics involves clustering– in particular, spatially-aware clustering. Many spatially-aware clustering algorithms check the array coordinates to determine neighboring spots and ultimately produce spatially smooth clusters. As we have previously explained, visiumStitched re-computes array coordinates in a meaningful way, such that software like BayesSpace and PRECAST work out-of-the-box with stitched data, treating each group as a single continuous sample.

We’ve already run PRECAST, and can visualize the results here, where we see a fairly seamless transition of cluster assignments across capture-area boundaries. First, let’s examine k = 2:

## Grab SpatialExperiment with normalized counts
spe_norm <- fetch_data(type = "visiumStitched_brain_spe")
#> 2024-12-18 19:32:14.353405 loading file /home/biocbuild/.cache/R/BiocFileCache/37ddd53a31d910_visiumStitched_brain_spe.rds%3Frlkey%3Dnq6a82u23xuu9hohr86oodwdi%26dl%3D1
assayNames(spe_norm)
#> [1] "counts"    "logcounts"

## PRECAST k = 2 clusters with our manually chosen colors
vis_clus(
    spe_norm,
    clustervar = "precast_k2_stitched",
    is_stitched = TRUE,
    colors = c(
        "1" = "gold",
        "2" = "darkblue",
        "NA" = "white"
    ),
    spatial = FALSE
)

We can see that these two spatial clusters are differentiating the white vs the gray matter based on the white matter marker genes we previously visualized.

In the example data, k = 4 and k = 8 have also been computed. Let’s visualize the k = 4 results.

## PRECAST results already available in this example data
vars <- colnames(colData(spe_norm))
vars[grep("precast", vars)]
#>  [1] "precast_k2_stitched"    "precast_k4_stitched"    "precast_k8_stitched"   
#>  [4] "precast_k16_stitched"   "precast_k24_stitched"   "precast_k2_unstitched" 
#>  [7] "precast_k4_unstitched"  "precast_k8_unstitched"  "precast_k16_unstitched"
#> [10] "precast_k24_unstitched"

## PRECAST k = 4 clusters with default cluster colors
vis_clus(
    spe_norm,
    clustervar = "precast_k4_stitched",
    is_stitched = TRUE,
    spatial = FALSE
)

The biological interpretation of these spatial clusters would need further work, using methods such as:

• spatial registration of reference sc/snRNA-seq or spatial data,
• visualization of known marker genes for the tissue of interest,
• or identification of data driven marker genes using spatialLIBD::registration_wrapper(), DeconvoBuddies::findMarkers_1vAll(), DeconvoBuiddies::get_mean_ratio() or other tools. See Pullin and McCarthy, Genome Biol., 2024 for a list of marker gene selection methods.

7 Conclusion

visiumStitched provides a set of helper functions, in conjunction with ImageJ/Fiji, intended to simplify the stitching of Visium data into a spatially integrated SpatialExperiment object ready for analysis. We hope you find it useful for your research!

8 Reproducibility

The visiumStitched package (Eagles and Collado-Torres, 2024) was made possible thanks to:

• R (R Core Team, 2024)
• BiocFileCache (Shepherd and Morgan, 2024)
• BiocStyle (Oleś, 2024)
• dplyr (Wickham, François, Henry, Müller, and Vaughan, 2023)
• DropletUtils (Lun, Riesenfeld, Andrews, Dao, Gomes, participants in the 1st Human Cell Atlas Jamboree, and Marioni, 2019)
• ggplot2 (Wickham, 2016)
• imager (Barthelme, 2024)
• knitr (Xie, 2024)
• pkgcond (Redd and R Documentation Task Force, 2021)
• RefManageR (McLean, 2017)
• rjson (Couture-Beil, 2024)
• rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2024)
• S4Vectors (Pagès, Lawrence, and Aboyoun, 2024)
• sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 2021)
• Seurat (Hao, Stuart, Kowalski, Choudhary, Hoffman, Hartman, Srivastava, Molla, Madad, Fernandez-Granda, and Satija, 2023)
• SpatialExperiment (Righelli, Weber, Crowell, Pardo, Collado-Torres, Ghazanfar, Lun, Hicks, and Risso, 2022)
• spatialLIBD (Pardo, Spangler, Weber, Hicks, Jaffe, Martinowich, Maynard, and Collado-Torres, 2022)
• stringr (Wickham, 2023)
• SummarizedExperiment (Morgan, Obenchain, Hester, and Pagès, 2024)
• testthat (Wickham, 2011)
• xml2 (Wickham, Hester, and Ooms, 2023)

This package was developed using biocthis.

Code for creating the vignette

## Create the vignette
library("rmarkdown")
system.time(render("visiumStitched.Rmd", "BiocStyle::html_document"))

## Extract the R code
library("knitr")
knit("visiumStitched.Rmd", tangle = TRUE)

R session information.

#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R Under development (unstable) (2024-10-21 r87258)
#>  os       Ubuntu 24.04.1 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  C
#>  ctype    en_US.UTF-8
#>  tz       America/New_York
#>  date     2024-12-18
#>  pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
#>  package              * version   date (UTC) lib source
#>  abind                  1.4-8     2024-09-12 [2] CRAN (R 4.5.0)
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9 Bibliography

This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

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