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README.Rmd
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---
title: visiumStitched_brain
output:
github_document:
html_preview: true
html_document:
toc: true
toc_flot: true
includes:
in_header: header.html
after_body: footer.html
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r 'setup', include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "img/",
out.width = "100%"
)
```
[](https://zenodo.org/doi/10.5281/zenodo.13014172)
## Overview
This repository contains the data used to demonstrate the utility of [`visiumStitched`](https://github.com/LieberInstitute/visiumStitched). Three [Visium](https://www.10xgenomics.com/products/spatial-gene-expression)
capture areas were collected from the postmortem human brain from one donor. We used
[Fiji](https://imagej.net/software/fiji/) and `visiumStitched` to stitch the gene-expression and imaging
data into one sample for downstream analysis.
If you tweet about this website, the data or the R package please use
the <code>\#visiumStitched</code> hashtag. You can find previous tweets
that way as shown
<a href="https://twitter.com/search?q=%23visiumStitched&src=typed_query">here</a>.
Thank you for your interest in our work!
## Study Design
<img src="img/data_generation.png" align="center" width="1000px" />
**Figure 1. Experimental design to generate spatially-resolved transcriptomics (SRT) data from postmortem human brain across three Visium capture areas.**
**(A)** Schematics illustrating a coronal human brain hemisphere at the level of the anterior striatum. Inset showing an illustration of the lateral side of the brain with a vertical red line depicting the location of the coronal slab. CC – cerebral cortex, C – caudate nucleus, P – putamen, WM – white matter.
**(B)** Raw coronal brain slab, dissected tissue block with an indicated score line, and H&E staining of the 10 µm section taken from the same block.
**(C)** A diagram depicting how tissue sections were arranged on the Visium slide with capture arrays A1 (blue) and D1 (yellow) arranged partially overlapping, while capture arrays D1 (yellow) and C1 (red) arranged adjacent to each other across a score line.
## Introductory Material
Here is a short overview video of the stitching steps performed with [`Fiji`](https://imagej.net/software/fiji/).
<iframe width="560" height="315" src="https://www.youtube.com/embed/kFLtpK3qbSY?si=u6bIKY5U9yccx62R" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen data-external="1"></iframe>
Check out [this guide](https://research.libd.org/visiumStitched/articles/visiumStitched.html) describing how to use
`visiumStitched` with the outputs of `Fiji`, which enabled the integration of all three capture areas for this project. This resulted in a [`SpatialExperiment`](https://doi.org/10.1093/bioinformatics/btac299) object that can be used seamlessly for downstream analyses steps, such as performing spatially-aware clustering with tools such as [`BayesSpace`](https://doi.org/10.1038/s41587-021-00935-2) and [`PRECAST`](https://doi.org/10.1038/s41467-023-35947-w).
## Interactive Websites
Explore the stitched example data using the [interactive website](https://libd.shinyapps.io/visiumStitched_brain), powered by [spatialLIBD](https://doi.org/10.1186/s12864-022-08601-w).
Alternatively, you may choose to run the interactive website locally with [`spatialLIBD::run_app()`](http://research.libd.org/spatialLIBD/reference/run_app.html),
which gives more freedom regarding memory usage and session length. See [this example](https://github.com/LieberInstitute/visiumStitched_brain/blob/devel/code/05_shiny/app.R)
for invoking `spatialLIBD::run_app()` with an appropriate set of arguments.
# Data Access
## R Objects
For this human brain dataset composed of 3 Visium capture areas, the transcriptomics data, [`SpaceRanger`](https://www.10xgenomics.com/support/software/space-ranger/latest/analysis/running-pipelines/space-ranger-count) outputs, and [`Fiji`](https://imagej.net/software/fiji/) outputs are all available through `spatialLIBD::fetch_data()` (version 1.17.8 or newer). A comprehensive vignette using these objects is written [here](https://research.libd.org/visiumStitched/articles/visiumStitched.html).
```{r "pull_data", fig.height = 8, fig.width = 9, out.width="800px", out.extra="align='center'"}
## 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")
spe
## Show clustering results from PRECAST at k = 2
spatialLIBD::vis_clus(
spe,
clustervar = 'precast_k2_stitched',
is_stitched = TRUE,
colors = c(
"1" = "gold",
"2" = "darkblue",
"NA" = "white"
)
)
```
## Raw data
The source data described in this manuscript are available from [Globus](http://research.libd.org/globus/) (`jhpce#visiumStitched_brain`). This includes all the input FASTQ files as well as the outputs from [`SpaceRanger`](https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger).
# Citing Our Work
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_visiumStitched', eval = requireNamespace('visiumStitched')}
print(citation("visiumStitched")[2], bibtex = TRUE)
```
Please note that `visiumStiched` 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 the
package.
### Cite `spatialLIBD`
Below is the citation output from using `citation('spatialLIBD')` in R. Please run this yourself
to check for any updates on how to cite __spatialLIBD__.
```{r 'citation_spatialLIBD'}
print(citation("spatialLIBD")[1], bibtex = TRUE)
```
## Internal
* JHPCE location: `/dcs05/lieber/lcolladotor/visiumStitched_LIBD1070/visiumStitched_brain`.
The code in this repository follows the organization from https://github.com/LieberInstitute/template_project following the guidelines from [organizing-your-work](https://lcolladotor.github.io/bioc_team_ds/organizing-your-work.html#.YzL43uzMKX0).