Bioconductor package to visualize spatially-resolved transcriptomics data

knitr::opts_chunk$set(
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spatialLIBD

Welcome to the spatialLIBD project! It is composed of:

a shiny web application that we are hosting at spatial.libd.org/spatialLIBD/ that can handle a limited set of concurrent users,
a Bioconductor package at bioconductor.org/packages/spatialLIBD (or from here) that lets you analyze the data and run a local version of our web application (with our data or yours),
and a research article with the scientific knowledge we drew from this dataset. The analysis code for our project is available here and the high quality figures for the manuscript are available through Figshare.

The web application allows you to browse the LIBD human dorsolateral pre-frontal cortex (DLPFC) spatial transcriptomics data generated with the 10x Genomics Visium platform. Through the R/Bioconductor package you can also download the data as well as visualize your own datasets using this web application. Please check the manuscript or bioRxiv pre-print for more details about this project.

If you tweet about this website, the data or the R package please use the #spatialLIBD hashtag. You can find previous tweets that way as shown here. Thank you! Tweet #spatialLIBD

Study design

As a quick overview, the data presented here is from portion of the DLPFC that spans six neuronal layers plus white matter (A) for a total of three subjects with two pairs of spatially adjacent replicates (B). Each dissection of DLPFC was designed to span all six layers plus white matter (C). Using this web application you can explore the expression of known genes such as SNAP25 (D, a neuronal gene), MOBP (E, an oligodendrocyte gene), and known layer markers from mouse studies such as PCP4 (F, a known layer 5 marker gene).

This web application was built such that we could annotate the spots to layers as you can see under the spot-level data tab. Once we annotated each spot to a layer, we compressed the information by a pseudo-bulking approach into layer-level data. We then analyzed the expression through a set of models whose results you can also explore through this web application. Finally, you can upload your own gene sets of interest as well as layer enrichment statistics and compare them with our LIBD Human DLPFC Visium dataset.

If you are interested in running this web application locally, you can do so thanks to the spatialLIBD R/Bioconductor package that powers this web application as shown below.

## Run this web application locally
spatialLIBD::run_app()

## You will have more control about the length of the
## session and memory usage.

## You could also use this function to visualize your
## own data given some requirements described
## in detail in the package vignette documentation
## at http://research.libd.org/spatialLIBD/.

Shiny website mirrors

Main shiny application website (note that the link must have a trailing slash / for it to work)
Shinyapps This version has less RAM memory but is typically deployed using the latest version of spatialLIBD.

R/Bioconductor package

The spatialLIBD package contains functions for:

Accessing the spatial transcriptomics data from the LIBD Human Pilot project (code on GitHub) generated with the Visium platform from 10x Genomics. The data is retrieved from Bioconductor's ExperimentHub.
Visualizing the spot-level spatial gene expression data and clusters.
Inspecting the data interactively either on your computer or through spatial.libd.org/spatialLIBD/.

For more details, please check the documentation website or the Bioconductor package landing page here.

Installation instructions

Get the latest stable R release from CRAN. Then install spatialLIBD from Bioconductor using the following code:

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

BiocManager::install("spatialLIBD")

If you want to use the development version of spatialLIBD, you will need to use the R version corresponding to the current Bioconductor-devel branch as described in more detail on the Bioconductor website. Then you can install spatialLIBD from GitHub using the following command.

BiocManager::install("LieberInstitute/spatialLIBD")

Access the data

Through the spatialLIBD package you can access the processed data in it's final R format. However, we also provide a table of links so you can download the raw data we received from 10x Genomics.

Processed data

Using spatialLIBD you can access the Human DLPFC spatial transcriptomics data from the 10x Genomics Visium platform. For example, this is the code you can use to access the layer-level data. For more details, check the help file for fetch_data().

## Load the package
library("spatialLIBD")

## Download the spot-level data
spe <- fetch_data(type = "spe")

## This is a SpatialExperiment object
spe

## Note the memory size
lobstr::obj_size(spe)

## Remake the logo image with histology information
vis_clus(
    spe = spe,
    clustervar = "spatialLIBD",
    sampleid = "151673",
    colors = libd_layer_colors,
    ... = " DLPFC Human Brain Layers\nMade with research.libd.org/spatialLIBD/"
)

Raw data

You can access all the raw data through Globus (jhpce#HumanPilot10x). Furthermore, below you can find the links to the raw data we received from 10x Genomics.

## Read in the table of links from the HumanPilot repository
## Since this depends on another repo, I set eval to FALSE.
aws_links <-
    read.table(
        "../HumanPilot/AWS_File_locations.tsv",
        header = TRUE,
        stringsAsFactors = FALSE
    )
## Format into markdown links
for (i in seq_len(ncol(aws_links))[-1]) {
    aws_links[[i]] <- paste0("[AWS](", aws_links[[i]], ")")
}
aws_links$`HTML_report` <- paste0("[GitHub](https://github.com/LieberInstitute/HumanPilot/blob/master/10X/", aws_links$SampleID, "/", aws_links$SampleID, "_web_summary.html)")

## Print the table
knitr::kable(aws_links, caption = "Links to the Human DLPFC Visium raw data files", format = "markdown")

| SampleID|h5_filtered |h5_raw |image_full |image_hi |image_lo |loupe |HTML_report | |--------:|:-----------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | 151507|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151508|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151509|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151510|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151669|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151670|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151671|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151672|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151673|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151674|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151675|AWS |AWS |AWS |AWS |AWS |AWS |GitHub | | 151676|AWS |AWS |AWS |AWS |AWS |AWS |GitHub |

Citation

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.

print(citation("spatialLIBD"), bibtex = TRUE)

Please note that the spatialLIBD 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 spatialLIBD project is released with a Contributor 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 through r BiocStyle::CRANpkg('usethis'), r BiocStyle::CRANpkg('remotes'), r BiocStyle::Githubpkg('r-hub/sysreqs') and r BiocStyle::CRANpkg('rcmdcheck') customized to use Bioconductor's docker containers and r BiocStyle::Biocpkg('BiocCheck').
Code coverage assessment is possible thanks to codecov and r BiocStyle::CRANpkg('covr').
The documentation website 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').