spatialLIBD

Lifecycle:
maturing Travis build
status BioC
status DOI Codecov test
coverage

Welcome to the spatialLIBD project! It is composed of:

This 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 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!

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

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 using from Bioconductor the following code:

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

BiocManager::install("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
sce <- fetch_data(type = 'sce')
#> Loading objects:
#>   sce

## This is a SingleCellExperiment object
sce
#> class: SingleCellExperiment 
#> dim: 33538 47681 
#> metadata(1): image
#> assays(2): counts logcounts
#> rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
#>   ENSG00000268674
#> rowData names(9): source type ... gene_search is_top_hvg
#> colnames(47681): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
#>   TTGTTTCCATACAACT-1 TTGTTTGTGTAAATTC-1
#> colData names(73): barcode sample_name ... pseudobulk_UMAP_spatial
#>   markers_UMAP_spatial
#> reducedDimNames(6): PCA TSNE_perplexity50 ... TSNE_perplexity80
#>   UMAP_neighbors15
#> spikeNames(0):
#> altExpNames(0):

## Note the memory size
pryr::object_size(sce)
#> 2.08 GB

## Remake the logo image with histology information
sce_image_clus(
    sce = sce,
    clustervar = 'layer_guess_reordered',
    sampleid = '151673',
    colors = libd_layer_colors,
    ... = ' DLPFC Human Brain Layers\nMade with github.com/LieberInstitute/spatialLIBD'
)

Raw data

Below you can find the links to the raw data we received from 10x Genomics.

SampleID h5_filtered h5_raw image_hi image_lo loupe
151507 AWS AWS AWS AWS AWS
151508 AWS AWS AWS AWS AWS
151509 AWS AWS AWS AWS AWS
151510 AWS AWS AWS AWS AWS
151669 AWS AWS AWS AWS AWS
151670 AWS AWS AWS AWS AWS
151671 AWS AWS AWS AWS AWS
151672 AWS AWS AWS AWS AWS
151673 AWS AWS AWS AWS AWS
151674 AWS AWS AWS AWS AWS
151675 AWS AWS AWS AWS AWS
151676 AWS AWS AWS AWS AWS

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.

citation('spatialLIBD')
#> 
#> Collado-Torres L, Maynard KR, Jaffe AE (2020). _LIBD Visium spatial
#> transcriptomics human pilot data inspector_. doi:
#> 10.18129/B9.bioc.spatialLIBD (URL:
#> https://doi.org/10.18129/B9.bioc.spatialLIBD),
#> https://github.com/LieberInstitute/spatialLIBD - R package version
#> 0.99.5, <URL: http://www.bioconductor.org/packages/spatialLIBD>.
#> 
#> Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Williams SR, II
#> JLC, Barry BK, Tran MN, Besich Z, Tippani M, Chew J, Yin Y, Hyde TM,
#> Rao N, Hicks SC, Martinowich K, Jaffe AE (2020). "Transcriptome-scale
#> spatial gene expression in the human dorsolateral prefrontal cortex."
#> _bioRxiv_. doi: 10.1101/xxxyyy (URL: https://doi.org/10.1101/xxxyyy),
#> <URL: https://doi.org/10.1101/xxxyyy>.
#> 
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.

Please note that the spatialLIBD was only made possible thanks to many other R and bioinformatics software authors. We have cited their work either in the pre-print or the vignette of the R package.