https://github.com/bioconductor-source/visiumstitched

https://github.com/bioconductor-source/visiumstitched

Repository

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "man/figures/README-",
    out.width = "100%"
)
```

# visiumStitched 


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`visiumStitched` provides helper functions for working with	multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. `visiumStitched` prepares `SpaceRanger` (10x Genomics) output files so you can stitch the images from groups of capture areas together with `Fiji`. Then `visiumStitched` builds a `SpatialExperiment` object with the stitched data and makes an artificial hexogonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as `PRECAST` and `BayesSpace`. The `SpatialExperiment` objects created by `visiumStitched` are compatible with `spatialLIBD`, which can be used to build interactive websites for stitched `SpatialExperiment` objects (check the [example human brain data](https://libd.shinyapps.io/visiumStitched_brain)). `visiumStitched` also enables casting `SpatialExperiment` objects as `Seurat` objects.

For details, check the [documentation site](http://research.libd.org/visiumStitched/).

## Example data processed with visiumStitched

Here is example human brain data from [LieberInstitute/visiumStitched_brain](https://github.com/LieberInstitute/visiumStitched_brain) that was stitched together using `visiumStitched`. The resulting data can be accessed and visualized with [`spatialLIBD`](https://research.libd.org/spatialLIBD/) version 1.17.8 or newer.

```{r "example_visiumStitched_brain"}
## Check that you have a recent version of spatialLIBD installed
stopifnot(packageVersion("spatialLIBD") >= "1.17.8")

## Download the spot-level data, which is a SpatialExperiment object
spe <- spatialLIBD::fetch_data(type = "visiumStitched_brain_spe")

## Explore the stitched data
spe

## Show clustering results from PRECAST at k = 8
spatialLIBD::vis_clus(
    spe,
    clustervar = "precast_k8_stitched",
    is_stitched = TRUE
)
```


## Installation instructions

Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `visiumStitched` from [GitHub](https://github.com/LieberInstitute/visiumStitched) using the following code:

```{r 'install', eval = FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("LieberInstitute/visiumStitched")
```

## Citation

Below is the citation output from using `citation('visiumStitched')` in R. Please
run this yourself to check for any updates on how to cite __visiumStitched__.

```{r 'citation', eval = requireNamespace('visiumStitched')}
print(citation("visiumStitched"), bibtex = TRUE)
```

Please note that the `visiumStitched` was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.

## Code of Conduct

Please note that the `visiumStitched` project is released with a [Contributor Code of Conduct](http://bioconductor.org/about/code-of-conduct/). By contributing to this project, you agree to abide by its terms.

## Development tools

* Continuous code testing is possible thanks to [GitHub actions](https://www.tidyverse.org/blog/2020/04/usethis-1-6-0/)  through `r BiocStyle::CRANpkg('usethis')`, `r BiocStyle::CRANpkg('remotes')`, and `r BiocStyle::CRANpkg('rcmdcheck')` customized to use [Bioconductor's docker containers](https://www.bioconductor.org/help/docker/) and `r BiocStyle::Biocpkg('BiocCheck')`.
* Code coverage assessment is possible thanks to [codecov](https://codecov.io/gh) and `r BiocStyle::CRANpkg('covr')`.
* The [documentation website](http://LieberInstitute.github.io/visiumStitched) is automatically updated thanks to `r BiocStyle::CRANpkg('pkgdown')`.
* The code is styled automatically thanks to `r BiocStyle::CRANpkg('styler')`.
* The documentation is formatted thanks to `r BiocStyle::CRANpkg('devtools')` and `r BiocStyle::CRANpkg('roxygen2')`.

For more details, check the `dev` directory.

This package was developed using `r BiocStyle::Biocpkg('biocthis')`.

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