LieberInstitute/spatialLIBD
spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

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In LieberInstitute/spatialLIBD: spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data 

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)

## For links
library("BiocStyle")

## Track time spent on making the vignette
startTime <- Sys.time()

## Bib setup
library("RefManageR")

## Write bibliography information
bib <- c(
    R = citation(),
    AnnotationHub = citation("AnnotationHub")[1],
    benchmarkme = citation("benchmarkme")[1],
    BiocFileCache = citation("BiocFileCache")[1],
    BiocGenerics = citation("BiocGenerics")[1],
    BiocStyle = citation("BiocStyle")[1],
    cowplot = citation("cowplot")[1],
    DT = citation("DT")[1],
    edgeR = citation("edgeR")[1],
    ExperimentHub = citation("ExperimentHub")[1],
    fields = citation("fields")[1],
    GenomicRanges = citation("GenomicRanges")[1],
    ggplot2 = citation("ggplot2")[1],
    golem = citation("golem")[1],
    IRanges = citation("IRanges")[1],
    knitr = citation("knitr")[3],
    limma = citation("limma")[1],
    magick = citation("magick")[1],
    Matrix = citation("Matrix")[1],
    paletteer = citation("paletteer")[1],
    plotly = citation("plotly")[1],
    RColorBrewer = citation("RColorBrewer")[1],
    RefManageR = citation("RefManageR")[1],
    rmarkdown = citation("rmarkdown")[1],
    rtracklayer = citation("rtracklayer")[1],
    S4Vectors = citation("S4Vectors")[1],
    scater = citation("scater")[1],
    scuttle = citation("scuttle")[1],
    sessioninfo = citation("sessioninfo")[1],
    SingleCellExperiment = citation("SingleCellExperiment")[1],
    shiny = citation("shiny")[1],
    SpatialExperiment = citation("SpatialExperiment")[1],
    spatialLIBD = citation("spatialLIBD")[1],
    spatialLIBDpaper = citation("spatialLIBD")[2],
    spatialDLPFC = citation("spatialLIBD")[3],
    VisiumSPGAD = citation("spatialLIBD")[4],
    statmod = citation("statmod")[1],
    SummarizedExperiment = citation("SummarizedExperiment")[1],
    testthat = citation("testthat")[1],
    viridisLite = citation("viridisLite")[1]
)

Welcome

Welcome to the spatialLIBD project! It is composed of:

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!

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).

Shiny website mirrors

Basics

Install spatialLIBD

R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. r Biocpkg('spatialLIBD') is a R package available via Bioconductor. R can be installed on any operating system from CRAN after which you can install r Biocpkg('spatialLIBD') by using the following commands in your R session:

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

BiocManager::install("spatialLIBD")

## Check that you have a valid Bioconductor installation
BiocManager::valid()

To run all the code in this vignette, you might need to install other R/Bioconductor packages, which you can do with:

BiocManager::install("spatialLIBD", dependencies = TRUE, force = TRUE)

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")

Required knowledge

r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) is based on many other packages and in particular in those that have implemented the infrastructure needed for dealing with single cell RNA sequencing data, visualization functions, and interactive data exploration. That is, packages like r Biocpkg('SingleCellExperiment') that allow you to store the data, r CRANpkg('ggplot2') and r CRANpkg('plotly') for visualizing the data, and r CRANpkg('shiny') for building an interactive interface. A r Biocpkg('spatialLIBD') user who only accesses the web application is not expected to deal with those packages directly. A r Biocpkg('spatialLIBD') user will need to be familiar with r Biocpkg('SingleCellExperiment') and r CRANpkg('ggplot2') to understand the data provided by r Biocpkg('spatialLIBD') or the graphical results r Biocpkg('spatialLIBD') provides. Furthermore, it'll be useful for the user to know about r CRANpkg('shiny') and r CRANpkg('plotly') if you wish to adapt the web application provided by r Biocpkg('spatialLIBD').

If you are asking yourself the question "Where do I start using Bioconductor?" you might be interested in this blog post.

Asking for help

As package developers, we try to explain clearly how to use our packages and in which order to use the functions. But R and Bioconductor have a steep learning curve so it is critical to learn where to ask for help. The blog post quoted above mentions some but we would like to highlight the Bioconductor support site as the main resource for getting help regarding Bioconductor. Other alternatives are available such as creating GitHub issues and tweeting. However, please note that if you want to receive help you should adhere to the posting guidelines. It is particularly critical that you provide a small reproducible example and your session information so package developers can track down the source of the error.

Citing spatialLIBD

We hope that r Biocpkg('spatialLIBD') will be useful for your research. Please use the following information to cite the package and the research article describing the data provided by r Biocpkg('spatialLIBD'). Thank you!

## Citation info
citation("spatialLIBD")

Overview

The r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) package was developed for analyzing the human dorsolateral prefrontal cortex (DLPFC) spatial transcriptomics data generated with the 10x Genomics Visium technology by researchers at the Lieber Institute for Brain Development (LIBD) r Citep(bib[['spatialLIBDpaper']]). An initial r CRANpkg('shiny') application was developed for interactively exploring this data and for assigning human brain layer labels to the each spot for each sample generated. While this was useful enough for our project, we made this Bioconductor package in case you want to:

In this vignette we'll showcase how you can access the Human DLPFC LIBD Visium dataset r Citep(bib[['spatialLIBDpaper']]), the R functions provided by r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]), and an overview of how you can re-shape your own Visium dataset to match the structure we used.

To get started, please load the r Biocpkg('spatialLIBD') package.

library("spatialLIBD")

Human DLPFC Visium dataset

The human DLPFC 10x Genomics Visium dataset analyzed by LIBD researchers and colleagues is described in detail by Maynard, Collado-Torres et al r Citep(bib[['spatialLIBDpaper']]). However, briefly, this dataset is composed of:

Data specifics

We combined all the Visium data into a single r Biocpkg('SpatialExperiment') r Citep(bib[['SpatialExperiment']]) object that we typically refer to as spe ^[Check this code for details on how we built the spe object. In particular check convert_sce.R and sce_scran.R.]. It has 33,538 genes (rows) and 47,681 spots (columns). This is the initial point for most of our analyses (code available on GitHub). Using r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) we manually assigned each spot across all 12 images to a layer (L1 through L6 or WM). We then compressed the spot-level data at the layer-level using a pseudo-bulking approach resulting in the r Biocpkg('SingleCellExperiment') object we typically refer to as sce_layer ^[Check this code for details on how we built the sce_layer object. In particular check spots_per_layer.R and layer_enrichment.R.]. We then computed for each gene t or F statistics assessing whether the gene had higher expression in a given layer compared to the rest (enrichment; t-stat), between one layer and another layer (pairwise; t-stat), or had any expression variability across all layers (anova; F-stat). The results from the models are stored in what we refer to as modeling_results ^[Check this code for details on how we built the modeling_results object. In particular check layer_specificity_fstats.R, layer_specificity.R, and misc_numbers.R.].

In summary,

Downloading the data with spatialLIBD

Using r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) you can download all of these R objects. They are hosted by Bioconductor's r Biocpkg('ExperimentHub') r Citep(bib[['ExperimentHub']]) resource and you can download them using spatialLIBD::fetch_data(). fetch_data() will query r Biocpkg('ExperimentHub') which in turn will download the data and cache it so you don't have to download it again. If r Biocpkg('ExperimentHub') is unavailable, then fetch_data() has a backup option that does not cache the files ^[You can change the destdir argument and specific a specific location that you will use and re-use. However the default value of destdir is a temporary directory that will be wiped out once you close your R session.] Below we obtain all of these objects.

## Connect to ExperimentHub
ehub <- ExperimentHub::ExperimentHub()

## Download the small example sce data
sce <- fetch_data(type = "sce_example", eh = ehub)

## Convert to a SpatialExperiment object
spe <- sce_to_spe(sce)

## If you want to download the full real data (about 2.1 GB in RAM) use:
if (FALSE) {
    if (!exists("spe")) spe <- fetch_data(type = "spe", eh = ehub)
}

## Query ExperimentHub and download the data
if (!exists("sce_layer")) sce_layer <- fetch_data(type = "sce_layer", eh = ehub)
modeling_results <- fetch_data("modeling_results", eh = ehub)

Once you have downloaded the objects, we can explore them a little bit

## spot-level data
spe

## layer-level data
sce_layer

## list of modeling result tables
sapply(modeling_results, class)
sapply(modeling_results, dim)
sapply(modeling_results, function(x) {
    head(colnames(x))
})

The modeling statistics are in wide format, which can make some visualizations complicated. The function sig_genes_extract_all() provides a way to convert them into long format and add some useful information. Let's do so below.

## Convert to a long format the modeling results
## This takes a few seconds to run
system.time(
    sig_genes <-
        sig_genes_extract_all(
            n = nrow(sce_layer),
            modeling_results = modeling_results,
            sce_layer = sce_layer
        )
)

## Explore the result
class(sig_genes)
dim(sig_genes)

Interactively explore the spatialLIBD data

Now that you have downloaded the data, you can interactively explore the data using a r CRANpkg('shiny') r Citep(bib[['shiny']]) web application contained within r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]). To do so, use the run_app() function as shown below:

if (interactive()) {
    run_app(
        spe = spe,
        sce_layer = sce_layer,
        modeling_results = modeling_results,
        sig_genes = sig_genes
    )
}

The r Biocpkg('spatialLIBD') r CRANpkg('shiny') application allows you to browse the spot-level data and interactively label spots, as well as explore the layer-level results. Once you launch it, check the Documentation tab for each view for more details. In order to avoid duplicating the documentation, we provide all the details on the r CRANpkg('shiny') application itself.

Though overall, this application allows you to export all static visualizations as PDF files or all interactive visualizations as PNG files, as well as all result table as CSV files. This is what produces the version you can access without any R use from your side at spatial.libd.org/spatialLIBD/.

spatialLIBD functions

We already covered fetch_data() which allows you to download the Human DLPFC Visium data from LIBD researchers and colleagues r Citep(bib[['spatialLIBDpaper']]).

Spot-level clusters and discrete variables

With the spe object that contains the spot-level data, we can visualize any discrete variable such as the layers using vis_clus() and related functions. These functions know where to extract and how to visualize the histology information.

## View our LIBD layers for one sample
vis_clus(
    spe = spe,
    clustervar = "layer_guess_reordered",
    sampleid = "151673",
    colors = libd_layer_colors,
    ... = " LIBD Layers"
)

Most of the variables stored in spe are discrete variables and as such, you can visualize them using vis_clus() and vis_grid_clus() for one or more than one sample respectively.

## This is not fully precise, but gives you a rough idea
## Some integer columns are actually continuous variables
table(sapply(colData(spe), class) %in% c("factor", "integer"))

## This is more precise (one cluster has 28 unique values)
table(sapply(colData(spe), function(x) length(unique(x))) < 29)

Notably, vis_clus() has a spatial logical(1) argument which is useful if you want to visualize the data without the histology information provided by geom_spatial() (a custom ggplot2::layer()). In particular, this is useful if you then want to use plotly::ggplotly() or other similar functions on the resulting ggplot2::ggplot() object.

## View our LIBD layers for one sample
## without spatial information
vis_clus(
    spe = spe,
    clustervar = "layer_guess_reordered",
    sampleid = "151673",
    colors = libd_layer_colors,
    ... = " LIBD Layers",
    spatial = FALSE
)

Some helper functions include get_colors() and sort_clusters() as well as the libd_layer_colors object included in r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]).

## Color palette designed by Lukas M. Weber with feedback from the team.
libd_layer_colors

Spot-level genes and continuous variables

Similar to vis_clus(), the vis_gene() family of functions use the spe spot-level object to visualize the gene expression or any continuous variable such as the number of cells per spots. That is, vis_gene() can visualize any of the assays(spe) or any of the continuous variables stored in colData(spe). If you want to visualize more than one sample at a time, use vis_grid_gene() instead. And just like vis_clus(), vis_gene() has a spatial logical(1) argument to turn off the custom spatial ggplot2::layer() produced by geom_spatial().

## Available gene expression assays
assayNames(spe)

## Not all of these make sense to visualize
## In particular, the key is not useful to visualize.
colnames(colData(spe))

## Visualize a gene
vis_gene(
    spe = spe,
    sampleid = "151673",
    viridis = FALSE
)

## Visualize the estimated number of cells per spot
vis_gene(
    spe = spe,
    sampleid = "151673",
    geneid = "cell_count"
)

## Visualize the fraction of chrM expression per spot
## without the spatial layer
vis_gene(
    spe = spe,
    sampleid = "151673",
    geneid = "expr_chrM_ratio",
    spatial = FALSE
)

As for the color palette, you can either use the color blind friendly palette when viridis = TRUE or a custom palette we determined. Note that if you design your own palette, you have to take into account that values it can be hard to distinguish some colors from the histology set of purple tones that are noticeable when the continuous variable is below or equal to the minCount (in our palettes such points have a 'transparent' color). For more details, check the internal code of vis_gene_p().

Extract significant genes

Earlier we also ran sig_genes_extract_all() in order to run the r CRANpkg('shiny') web application. However, we didn't explain the output. If you explore it, you'll notice that it's a very long table with several columns.

head(sig_genes)

The output of sig_genes_extract_all() contains the following columns:

sig_genes_extract_all() uses sig_genes_extract() as it's workhorse and as such has a very similar output.

Visualize modeling results

After extracting the table of modeling results in long format with sig_genes_extract_all(), we can then use layer_boxplot() to visualize any gene of interest for any of the model types and tests we performed. Below we explore the first one (by default). We also show the color palettes used in the r CRANpkg('shiny') application provided by r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]). The last example has the long title version that uses more information from the sig_genes object we created earlier.

## Note that we recommend setting the random seed so the jittering of the
## points will be reproducible. Given the requirements by BiocCheck this
## cannot be done inside the layer_boxplot() function.

## Create a boxplot of the first gene in `sig_genes`.
set.seed(20200206)
layer_boxplot(sig_genes = sig_genes, sce_layer = sce_layer)

## Viridis colors displayed in the shiny app
## showing the first pairwise model result
## (which illustrates the background colors used for the layers not
## involved in the pairwise comparison)
set.seed(20200206)
layer_boxplot(
    i = which(sig_genes$model_type == "pairwise")[1],
    sig_genes = sig_genes,
    sce_layer = sce_layer,
    col_low_box = viridisLite::viridis(4)[2],
    col_low_point = viridisLite::viridis(4)[1],
    col_high_box = viridisLite::viridis(4)[3],
    col_high_point = viridisLite::viridis(4)[4]
)

## Paper colors displayed in the shiny app
set.seed(20200206)
layer_boxplot(
    sig_genes = sig_genes,
    sce_layer = sce_layer,
    short_title = FALSE,
    col_low_box = "palegreen3",
    col_low_point = "springgreen2",
    col_high_box = "darkorange2",
    col_high_point = "orange1"
)

Correlation of layer-level statistics

Just like we compressed our spe object into sce_layer by pseudo-bulking ^[For more details, check this script.], we can do the same for other single nucleus or single cell RNA sequencing datasets (snRNA-seq, scRNA-seq) and then compute enrichment t-statistics (one group vs the rest; could be one cell type vs the rest or one cluster of cells vs the rest). In our analysis r Citep(bib[['spatialLIBDpaper']]), we did this for several datasets including one of our LIBD Human DLPFC snRNA-seq data generated by Matthew N Tran et al ^[For more details, check this script.]. In r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) we include a small set of these statistics for the 31 cell clusters identified by Matthew N Tran et al.

## Explore the enrichment t-statistics derived from Tran et al's snRNA-seq
## DLPFC data
dim(tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer)
tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer[seq_len(3), seq_len(6)]

The function layer_stat_cor() will take as input one such matrix of statistics and correlate them against our layer enrichment results (or other model types) using the subset of Ensembl gene IDs that are observed in both tables.

## Compute the correlation matrix of enrichment t-statistics between our data
## and Tran et al's snRNA-seq data
cor_stats_layer <- layer_stat_cor(
    tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer,
    modeling_results,
    "enrichment"
)

## Explore the correlation matrix
head(cor_stats_layer[, seq_len(3)])

Once we have computed this correlation matrix, we can then visualize it using layer_stat_cor_plot() as shown below.

## Visualize the correlation matrix
layer_stat_cor_plot(cor_stats_layer, max = max(cor_stats_layer))

In order to fully interpret the resulting heatmap you need to know what each of the cell clusters labels mean. In this case, the syntax is xx (Y) where xx is the cluster number and Y is:

  1. Excit: excitatory neurons.
  2. Inhib: inhibitory neurons.
  3. Oligo: oligodendrocytes.
  4. Astro: astrocytes.
  5. OPC: oligodendrocyte progenitor cells.
  6. Drop: an ambiguous cluster of cells that potentially should be dropped.

You can find the version with the full names here if you are interested in it.

The r CRANpkg('shiny') application provided by r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) allows users to upload CSV file with these t-statistics, view the correlation heatmaps, download them, and download the correlation matrix. An example CSV file is provided here.

Gene set enrichment

Many researchers have identified lists of genes that increase the risk of a given disorder or disease, are differentially expressed in a given experiment or set of conditions, have been described in a several research papers, among other collections. We can ask for any of our modeling results whether a list of genes is enriched among our significant results.

For illustration purposes, we included in r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) a set of genes from the Simons Foundation Autism Research Initiative SFARI. In our analysis r Citep(bib[['spatialLIBDpaper']]) we used more gene lists. Below, we read in the data and create a list() object with the Ensembl gene IDs that SFARI has provided that are related to autism.

## Read in the SFARI gene sets included in the package
asd_sfari <- utils::read.csv(
    system.file(
        "extdata",
        "SFARI-Gene_genes_01-03-2020release_02-04-2020export.csv",
        package = "spatialLIBD"
    ),
    as.is = TRUE
)

## Format them appropriately
asd_sfari_geneList <- list(
    Gene_SFARI_all = asd_sfari$ensembl.id,
    Gene_SFARI_high = asd_sfari$ensembl.id[asd_sfari$gene.score < 3],
    Gene_SFARI_syndromic = asd_sfari$ensembl.id[asd_sfari$syndromic == 1]
)

After reading the list of genes, we can then compute the enrichment odds ratios and p-values for a given FDR threshold in our statistics. 

## Compute the gene set enrichment results
asd_sfari_enrichment <- gene_set_enrichment(
    gene_list = asd_sfari_geneList,
    modeling_results = modeling_results,
    model_type = "enrichment"
)

## Explore the results
head(asd_sfari_enrichment)

Using the above enrichment table, we can then visualize the odds ratios on a heatmap as shown below. Note that we use the thresholded p-values at -log10(p) = 12 for visualization purposes and only show the odds ratios for -log10(p) > 3 by default.

## Visualize gene set enrichment results
gene_set_enrichment_plot(
    asd_sfari_enrichment,
    xlabs = gsub(".*_", "", unique(asd_sfari_enrichment$ID)),
    layerHeights = c(0, 40, 55, 75, 85, 110, 120, 135)
)

The r CRANpkg('shiny') application provided by r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]) allows users to upload CSV file their gene lists, compute the enrichment statistics, visualize them, download the PDF, and download the enrichment table. An example CSV file is provided here.

Re-shaping your data to our structure

This section gets into the details of how we generated the data r Citep(bib[['spatialLIBDpaper']]) behind r Biocpkg('spatialLIBD') r Citep(bib[['spatialLIBD']]). This could be useful if you are a Bioconductor developer or a user very familiar with packages such as r Biocpkg('SingleCellExperiment').

SpatialExperiment support

As of version 1.3.3, r Biocpkg('spatialLIBD') supports the SpatialExperiment class from r Biocpkg('SpatialExperiment'). The functions vis_gene_p(), vis_gene(), vis_grid_clus(), vis_grid_gene(), vis_clus(), vis_clus_p(), geom_spatial() now work with SpatialExperiment objects thanks to the updates in r Biocpkg('SpatialExperiment'). If you have spot-level data formatted in the older SingleCellExperiment objects that were heavily modified for spatialLIBD, you can use sce_to_spe() to convert the objects.

This work was done by Brenda Pardo and Leonardo.

Using spatialLIBD with your own data

Please check the second vignette on how to use r Biocpkg('spatialLIBD') with your own data as exemplified with a public 10x Genomics dataset or go directly to the read10xVisiumWrapper() documentation.

Expected characteristics of the data

If you want to check the key characteristics required by r Biocpkg('spatialLIBD')'s functions or the r CRANpkg('shiny') application, use the check_* family of functions.

check_spe(spe)
check_sce_layer(sce_layer)
## The output here is too long to print
xx <- check_modeling_results(modeling_results)
identical(xx, modeling_results)

Generating our data (legacy)

If you are interested in reshaping your data to fit our structure, we do not provide a quick function to do so. That is intentional given the active development by the Bioconductor community for determining the best way to deal with spatial transcriptomics data in general and the 10x Visium data in particular. Having said that, we do have all the steps and reproducibility information documented across several of our analysis r Citep(bib[['spatialLIBDpaper']]) scripts.

You don't necessarily need to do all of this to use the functions provided by r Biocpkg('spatialLIBD'). Note that external to the R objects, for the r CRANpkg('shiny') application provided by r Biocpkg('spatialLIBD') you will need to have the tissue_lowres_image.png image files in a directory structure by sample as shown here in order for the interactive visualizations made with r CRANpkg('plotly') to work.

More spatially-resolved LIBD datasets

Over time spatialLIBD::fetch_data() has been expanded to provide access to other datasets generated by our teams at the Lieber Institute for Brain Development (LIBD) that have also been analyzed with spatialLIBD.

spatialDLPFC

Through spatialLIBD::fetch_data() you can also download the data from the Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex project, also known as spatialDLPFC r Citep(bib[['spatialDLPFC']]). See http://research.libd.org/spatialDLPFC/ for more information about this project.

See the Twitter thread 🧵 below for a brief overview of the #spatialDLPFC project.

Visium_SPG_AD

Through spatialLIBD::fetch_data() you can also download the data from the _Influence of Alzheimer’s disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex _ project, also known as Visium_SPG_AD r Citep(bib[['VisiumSPGAD']]). See http://research.libd.org/Visium_SPG_AD/ for more information about this project.

See the Twitter thread 🧵 below for a brief overview of the #Visium_SPG_AD project.

TODO

LIBD data outside spatialLIBD

Sometimes our collaborators have shared data through other venues. So not all LIBD spatially-resolved transcriptomics data from the Keri Martinowich, Kristen Maynard, and Leonardo Collado-Torres teams has been released through spatialLIBD. However, it is very much compatible with spatialLIBD and can be analyzed or visualized with spatialLIBD functions.

locus-c

The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics, also known as locus-c, is not available through spatialLIBD, but you might be interested in checking out the excellent r Biocpkg("WeberDivechaLCdata") package for more details. See https://github.com/lmweber/locus-c for more details about the locus-c project.

See the Twitter thread 🧵 below for a brief overview of the locus-c project.

Reproducibility

The r Biocpkg('spatialLIBD') package r Citep(bib[['spatialLIBD']]) was made possible thanks to:

Code for creating the vignette

## Create the vignette
library("rmarkdown")
system.time(render("spatialLIBD.Rmd"))

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

Date the vignette was generated.

## Date the vignette was generated
Sys.time()

Wallclock time spent generating the vignette.

## Processing time in seconds
totalTime <- diff(c(startTime, Sys.time()))
round(totalTime, digits = 3)

R session information.

## Session info
library("sessioninfo")
options(width = 120)
session_info()

Bibliography

This vignette was generated using r Biocpkg('BiocStyle') r Citep(bib[['BiocStyle']]), r CRANpkg('knitr') r Citep(bib[['knitr']]) and r CRANpkg('rmarkdown') r Citep(bib[['rmarkdown']]) running behind the scenes.

Citations made with r CRANpkg('RefManageR') r Citep(bib[['RefManageR']]).

## Print bibliography
PrintBibliography(bib, .opts = list(hyperlink = "to.doc", style = "html"))


LieberInstitute/spatialLIBD documentation built on Nov. 4, 2024, 11:57 a.m.

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