Using spatialLIBD with 10x Genomics public datasets
20 May 2021
Package
spatialLIBD 1.4.0
1 Basics
1.1 Install spatialLIBD
R is an open-source statistical environment which can be easily modified to enhance its functionality via packages. spatialLIBD (Pardo, Spangler, Weber, Hicks, Jaffe, Martinowich, Maynard, and Collado-Torres, 2021) is an R package available via the Bioconductor repository for packages. R can be installed on any operating system from CRAN after which you can install 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()1.2 Required knowledge
Please first check the Introduction to spatialLIBD vignette available through GitHub or Bioconductor.
1.3 Citing spatialLIBD
We hope that spatialLIBD will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("spatialLIBD")
#>
#> Pardo B, Spangler A, Weber LM, Hicks SC, Jaffe AE, Martinowich K,
#> Maynard KR, Collado-Torres L (2021). "spatialLIBD: an R/Bioconductor
#> package to visualize spatially-resolved transcriptomics data."
#> _bioRxiv_. doi: 10.1101/2021.04.29.440149 (URL:
#> https://doi.org/10.1101/2021.04.29.440149), <URL:
#> https://www.biorxiv.org/content/10.1101/2021.04.29.440149v1>.
#>
#> Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams
#> SR, II JLC, Tran MN, Besich Z, Tippani M, Chew J, Yin Y, Kleinman JE,
#> Hyde TM, Rao N, Hicks SC, Martinowich K, Jaffe AE (2021).
#> "Transcriptome-scale spatial gene expression in the human dorsolateral
#> prefrontal cortex." _Nature Neuroscience_. doi:
#> 10.1038/s41593-020-00787-0 (URL:
#> https://doi.org/10.1038/s41593-020-00787-0), <URL:
#> https://www.nature.com/articles/s41593-020-00787-0>.
#>
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.2 Download data from 10x Genomics
In this vignette we’ll show you how you can use spatialLIBD (Pardo, Spangler, Weber, et al., 2021) for exploring spatially resolved transcriptomics data from the Visium platform by 10x Genomics. That is, you will learn how to use spatialLIBD for data beyond the one it was initially developed for (Maynard, Collado-Torres, Weber, Uytingco, Barry, Williams, II, Tran, Besich, Tippani, Chew, Yin, Kleinman, Hyde, Rao, Hicks, Martinowich, and Jaffe, 2021). To illustrate these steps, we will use data that 10x Genomics made publicly available at https://support.10xgenomics.com/spatial-gene-expression/datasets. We will use files from the human lymph node example publicly available at https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Human_Lymph_Node.
2.1 Load packages
To get started, lets load the different packages we’ll need for this vignette. Here’s a brief summary of why we need these packages:
- BiocFileCache: for downloading and saving a local cache of the data
- SpatialExperiment: for reading the
spacerangerfiles provided by 10x Genomics - rtracklayer: for importing a gene annotation GTF file
- pryr: for checking how much memory our object is using
- spatialLIBD: for launching an interactive website to explore the data
## Load packages in the order we'll use them next
library("BiocFileCache")
library("SpatialExperiment")
library("rtracklayer")
library("pryr")
library("spatialLIBD")2.2 Download spaceranger output files
Next we download data from 10x Genomics available from the human lymph node example, available at https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Human_Lymph_Node. We don’t need to download all the files listed there since SpatialExperiment doesn’t need all of them for importing the data into R. These files are part of the output that gets generated by spaceranger which is the processing pipeline provided by 10x Genomics for Visium data.
We’ll use BiocFileCache to keep the data in a local cache in case we want to run this example again and don’t want to re-download the data from the web.
## Download and save a local cache of the data provided by 10x Genomics
bfc <- BiocFileCache::BiocFileCache()
#> Warning: DEPRECATION: As of BiocFileCache (>1.15.1), default caching location has changed.
#> Problematic cache: /home/biocbuild/.cache/BiocFileCache
#> See https://www.bioconductor.org/packages/devel/bioc/vignettes/BiocFileCache/inst/doc/BiocFileCache.html#default-caching-location-update
lymph.url <-
paste0(
"https://cf.10xgenomics.com/samples/spatial-exp/",
"1.1.0/V1_Human_Lymph_Node/",
c(
"V1_Human_Lymph_Node_filtered_feature_bc_matrix.tar.gz",
"V1_Human_Lymph_Node_spatial.tar.gz"
)
)
lymph.data <- sapply(lymph.url, BiocFileCache::bfcrpath, x = bfc)10x Genomics provides the files in compressed tarballs (.tar.gz file extension). Which is why we’ll need to use utils::untar() to decompress the files. This will create new directories and we will use list.files() to see what files these directories contain.
## Extract the files to a temporary location
## (they'll be deleted once you close your R session)
sapply(lymph.data, utils::untar, exdir = tempdir())
#> https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Human_Lymph_Node/V1_Human_Lymph_Node_filtered_feature_bc_matrix.tar.gz.BFC34
#> 0
#> https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Human_Lymph_Node/V1_Human_Lymph_Node_spatial.tar.gz.BFC35
#> 0
## List the files we downloaded and extracted
## These files are typically SpaceRanger outputs
lymph.dirs <- file.path(
tempdir(),
c("filtered_feature_bc_matrix", "spatial", "raw_feature_bc_matrix")
)
list.files(lymph.dirs)
#> [1] "aligned_fiducials.jpg" "barcodes.tsv.gz"
#> [3] "detected_tissue_image.jpg" "features.tsv.gz"
#> [5] "matrix.mtx.gz" "scalefactors_json.json"
#> [7] "tissue_hires_image.png" "tissue_lowres_image.png"
#> [9] "tissue_positions_list.csv"Now that we have the files that we need, we can import the data into R using read10xVisium() from SpatialExperiment. We’ll import the low resolution histology images produced by spaceranger using the images = "lowres" and load = TRUE arguments. We’ll also load the filtered gene expression data using the data = "filtered" argument. The count matrix can still be quite large, which is why we’ll use the type = "sparse" argument to load the data into an R object that is memory-efficient for sparse data.
## Import the data as a SpatialExperiment object
spe <- SpatialExperiment::read10xVisium(
samples = tempdir(),
sample_id = "lymph",
type = "sparse", data = "filtered",
images = "lowres", load = TRUE
)
## Inspect the R object we just created: class, memory, and how it looks in
## general
class(spe)
#> [1] "SpatialExperiment"
#> attr(,"package")
#> [1] "SpatialExperiment"
pryr::object_size(spe)
#> 296 MB
spe
#> class: SpatialExperiment
#> dim: 36601 4035
#> metadata(0):
#> assays(1): counts
#> rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
#> ENSG00000277196
#> rowData names(1): symbol
#> colnames(4035): AAACAAGTATCTCCCA-1 AAACAATCTACTAGCA-1 ...
#> TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(1): sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialData names(3) : in_tissue array_row array_col
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
## The counts are saved in a sparse matrix R object
class(counts(spe))
#> [1] "dgCMatrix"
#> attr(,"package")
#> [1] "Matrix"3 Modify spe for spatialLIBD
Now that we have an SpatialExperiment R object (spe) with the data from 10x Genomics for the human lymph node example, we need to add a few features to the R object in order to create the interactive website using spatialLIBD::run_app(). These additional elements power features in the interactive website that you might be interested in.
First we start with adding a few variables to the sample information table (colData()) of our spe object. We add:
key: this labels each spot with a unique identifier. We combine the sample ID with the spot barcode ID to create this unique identifier.sum_umi: this continuous variable contains the total number of counts for each sample prior to filtering any genes.sum_gene: this continuous variable contains the number of genes that have at least 1 count.
## Add some information used by spatialLIBD
spe$key <- paste0(spe$sample_id, "_", colnames(spe))
spe$sum_umi <- colSums(counts(spe))
spe$sum_gene <- colSums(counts(spe) > 0)3.1 Add gene annotation information
The files SpatialExperiment::read10xVisium() uses to read in the spaceranger outputs into R do not include much information about the genes, such as their chromosomes, coordinates, and other gene annotation information. We thus recommend that you read in this information from a gene annotation file: typically a gtf file. For a real case scenario, you’ll mostly likely have access to the GTF file provided by 10x Genomics. However, we cannot download that file without downloading other files for this example. Thus we’ll show you the code you would use if you had access to the GTF file from 10x Genomics and also show a second approach that works for this vignette.
## Initially we don't have much information about the genes
rowRanges(spe)
#> GRangesList object of length 36601:
#> $ENSG00000243485
#> GRanges object with 0 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> -------
#> seqinfo: no sequences
#>
#> $ENSG00000237613
#> GRanges object with 0 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> -------
#> seqinfo: no sequences
#>
#> $ENSG00000186092
#> GRanges object with 0 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> -------
#> seqinfo: no sequences
#>
#> ...
#> <36598 more elements>3.1.1 From 10x
Depending on the version of spaceranger you used, you might have used different GTF files 10x Genomics has made available at https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest and described at https://support.10xgenomics.com/single-cell-gene-expression/software/release-notes/build. These files are too big though and we won’t download them in this example. For instance, References - 2020-A (July 7, 2020) for Human reference (GRCh38) is 11 GB in size and contains files we do not need for this vignette. If you did have the file locally, you could use the following code to read in the GTF file prepared by 10x Genomics and add the information into your spe object that SpatialExperiment::read10xVisium() does not include.
For example, in our computing cluster this GTF file is located at the following path and is 1.4 GB in size:
$ cd /dcl02/lieber/ajaffe/SpatialTranscriptomics/
$ du -sh --apparent-size refdata-gex-GRCh38-2020-A/genes/genes.gtf
1.4G /dcl02/lieber/ajaffe/SpatialTranscriptomics/refdata-gex-GRCh38-2020-A/genes/genes.gtfIf you have the GTF file from 10x Genomics, we show next how you can read the information into R, match it appropriately with the information in the spe object and add it back into the spe object.
## You could:
## * download the 11 GB file from
## https://cf.10xgenomics.com/supp/cell-exp/refdata-gex-GRCh38-2020-A.tar.gz
## * decompress it
## Read in the gene information from the annotation GTF file provided by 10x
gtf <-
rtracklayer::import(
"/path/to/refdata-gex-GRCh38-2020-A/genes/genes.gtf"
)
## Subject to genes only
gtf <- gtf[gtf$type == "gene"]
## Set the names to be the gene IDs
names(gtf) <- gtf$gene_id
## Match the genes
match_genes <- match(rownames(spe), gtf$gene_id)
## They should all be present if you are using the correct GTF file from 10x
stopifnot(all(!is.na(match_genes)))
## Keep only some columns from the gtf (you could keep all of them if you want)
mcols(gtf) <-
mcols(gtf)[, c(
"source",
"type",
"gene_id",
"gene_version",
"gene_name",
"gene_type"
)]
## Add the gene info to our SPE object
rowRanges(spe) <- gtf[match_genes]
## Inspect the gene annotation data we added
rowRanges(spe)3.1.2 From Gencode
In this vignette, we’ll use the GTF file from Gencode v32. That’s because the build notes from References - 2020-A (July 7, 2020) and Human reference, GRCh38 (GENCODE v32/Ensembl 98) at https://support.10xgenomics.com/single-cell-gene-expression/software/release-notes/build#GRCh38_2020A show that 10x Genomics used Gencode v32. They also used Ensembl version 98 which is why a few genes we have in our object are going to be missing. We show next how you can read the information into R, match it appropriately with the information in the spe object and add it back into the spe object.
## Download the Gencode v32 GTF file and cache it
gtf_cache <- BiocFileCache::bfcrpath(
bfc,
paste0(
"ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/",
"release_32/gencode.v32.annotation.gtf.gz"
)
)
## Show the GTF cache location
gtf_cache
#> BFC43
#> "~/.cache/BiocFileCache/176999676d2003_gencode.v32.annotation.gtf.gz"
## Import into R (takes ~1 min)
gtf <- rtracklayer::import(gtf_cache)
## Subset to genes only
gtf <- gtf[gtf$type == "gene"]
## Remove the .x part of the gene IDs
gtf$gene_id <- gsub("\\..*", "", gtf$gene_id)
## Set the names to be the gene IDs
names(gtf) <- gtf$gene_id
## Match the genes
match_genes <- match(rownames(spe), gtf$gene_id)
table(is.na(match_genes))
#>
#> FALSE TRUE
#> 36572 29
## Drop the few genes for which we don't have information
spe <- spe[!is.na(match_genes), ]
match_genes <- match_genes[!is.na(match_genes)]
## Keep only some columns from the gtf
mcols(gtf) <- mcols(gtf)[, c("source", "type", "gene_id", "gene_name", "gene_type")]
## Add the gene info to our SPE object
rowRanges(spe) <- gtf[match_genes]
## Inspect the gene annotation data we added
rowRanges(spe)
#> GRanges object with 36572 ranges and 5 metadata columns:
#> seqnames ranges strand | source type
#> <Rle> <IRanges> <Rle> | <factor> <factor>
#> ENSG00000243485 chr1 29554-31109 + | HAVANA gene
#> ENSG00000237613 chr1 34554-36081 - | HAVANA gene
#> ENSG00000186092 chr1 65419-71585 + | HAVANA gene
#> ENSG00000238009 chr1 89295-133723 - | HAVANA gene
#> ENSG00000239945 chr1 89551-91105 - | HAVANA gene
#> ... ... ... ... . ... ...
#> ENSG00000212907 chrM 10470-10766 + | ENSEMBL gene
#> ENSG00000198886 chrM 10760-12137 + | ENSEMBL gene
#> ENSG00000198786 chrM 12337-14148 + | ENSEMBL gene
#> ENSG00000198695 chrM 14149-14673 - | ENSEMBL gene
#> ENSG00000198727 chrM 14747-15887 + | ENSEMBL gene
#> gene_id gene_name gene_type
#> <character> <character> <character>
#> ENSG00000243485 ENSG00000243485 MIR1302-2HG lncRNA
#> ENSG00000237613 ENSG00000237613 FAM138A lncRNA
#> ENSG00000186092 ENSG00000186092 OR4F5 protein_coding
#> ENSG00000238009 ENSG00000238009 AL627309.1 lncRNA
#> ENSG00000239945 ENSG00000239945 AL627309.3 lncRNA
#> ... ... ... ...
#> ENSG00000212907 ENSG00000212907 MT-ND4L protein_coding
#> ENSG00000198886 ENSG00000198886 MT-ND4 protein_coding
#> ENSG00000198786 ENSG00000198786 MT-ND5 protein_coding
#> ENSG00000198695 ENSG00000198695 MT-ND6 protein_coding
#> ENSG00000198727 ENSG00000198727 MT-CYB protein_coding
#> -------
#> seqinfo: 25 sequences from an unspecified genome; no seqlengths3.1.3 Enable a friendlier gene search
Regardless of which method you used to obtain the gene annotation information, we can now proceed by adding the gene symbol and gene ID information that helps users search for genes in the shiny app produced by spatialLIBD. This will enable users to search genes by gene symbol or gene ID. If you didn’t do this, users would only be able to search genes by gene ID which makes the web application harder to use.
We also compute the total expression for the mitochondrial chromosome (chrM) as well as the ratio of chrM expression. Both of these continuous variables are interesting to explore and in some situations could be useful for biological interpretations. For instance, in our pilot data (Maynard, Collado-Torres, Weber, et al., 2021), we noticed that the expr_chrM_ratio was associated to DLPFC layers. That is, spots with high expr_chrM_ratio were not randomly located in our Visium slides.
## Add information used by spatialLIBD
rowData(spe)$gene_search <- paste0(
rowData(spe)$gene_name, "; ", rowData(spe)$gene_id
)
## Compute chrM expression and chrM expression ratio
is_mito <- which(seqnames(spe) == "chrM")
spe$expr_chrM <- colSums(counts(spe)[is_mito, , drop = FALSE])
spe$expr_chrM_ratio <- spe$expr_chrM / spe$sum_umi3.2 Filter the spe object
We can now continue with some filtering steps since this can help reduce the object size in memory as well as make it ready to use for downstream processing tools such as those from the scran and scuttle packages. Though these steps are not absolutely necessary.
## Remove genes with no data
no_expr <- which(rowSums(counts(spe)) == 0)
## Number of genes with no counts
length(no_expr)
#> [1] 11397
## Compute the percent of genes with no counts
length(no_expr) / nrow(spe) * 100
#> [1] 31.16318
spe <- spe[-no_expr, , drop = FALSE]
## Remove spots without counts
summary(spe$sum_umi)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 23 15917 20239 20738 25252 54931
## If we had spots with no counts, we would remove them
if (any(spe$sum_umi == 0)) {
spots_no_counts <- which(spe$sum_umi == 0)
## Number of spots with no counts
print(length(spots_no_counts))
## Percent of spots with no counts
print(length(spots_no_counts) / ncol(spe) * 100)
spe <- spe[, -spots_no_counts, drop = FALSE]
}3.3 Check object
Next, we add the ManualAnnotation variable to the sample information table (colData()) with "NA". That variable is used by the interactive website to store any manual annotations.
## Add a variable for saving the manual annotations
spe$ManualAnnotation <- "NA"Finally, we can now check the final object using spatialLIBD::check_spe(). This is a helper function that will warn us if some important element is missing in spe that we use later for the interactive website. If it all goes well, it will return the original spe object.
## Check the final dimensions and object size
dim(spe)
#> [1] 25175 4035
pryr::object_size(spe)
#> 298 MB
## Run check_spe() function
check_spe(spe)
#> class: SpatialExperiment
#> dim: 25175 4035
#> metadata(0):
#> assays(1): counts
#> rownames(25175): ENSG00000238009 ENSG00000241860 ... ENSG00000198695
#> ENSG00000198727
#> rowData names(6): source type ... gene_type gene_search
#> colnames(4035): AAACAAGTATCTCCCA-1 AAACAATCTACTAGCA-1 ...
#> TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(25): sample_id key ... array_row array_col
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialData names(3) : in_tissue array_row array_col
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor4 Explore the data
With our complete spe object, we can now use spatialLIBD for visualizing our data. We can do so using functions such as vis_gene() and vis_clus() that are described in more detail at the Introduction to spatialLIBD vignette available through GitHub or Bioconductor.
## Example visualizations. Let's start with a continuous variable.
spatialLIBD::vis_gene(
spe = spe,
sampleid = "lymph",
geneid = "sum_umi",
assayname = "counts"
)
## We next create a random cluster label to visualize
set.seed(20210428)
spe$random_cluster <- sample(1:7, ncol(spe), replace = TRUE)
## Next we visualize that random cluster
spatialLIBD::vis_clus(
spe = spe,
sampleid = "lymph",
clustervar = "random_cluster"
)
4.1 Run the interactive website
We are now ready to create our interactive website for the human lymph node data. The interactive website is a shiny web application that uses plotly to power several of the interactive features. We can create the interactive website using the spatialLIBD::run_app() function. The default arguments of that function are customized for the data from our initial study (Maynard, Collado-Torres, Weber, et al., 2021), so we will need to make some adjustments:
sce_layer,modeling_resultsandsig_geneswill be set toNULLsince do not have any pseudo-bulk results for this example data.title: we will use a custom title that reflect our dataspe_discreate_vars: we don’t have really any discrete variables to show beyondManualAnnotationwhich is used for the manual annotations andrandom_clusterthat we created in the previous section.spe_continous_vars: we have computed several continuous variables while adapting ourspeobject for spatialLIBD, so we’ll list these variables below in order to visually inspect them.default_cluster: this is used for indicating the default discrete variable and for now we’ll set it to ourrandom_cluster.
## Run our shiny app
if (interactive()) {
run_app(
spe,
sce_layer = NULL,
modeling_results = NULL,
sig_genes = NULL,
title = "spatialLIBD: human lymph node by 10x Genomics",
spe_discrete_vars = c("random_cluster", "ManualAnnotation"),
spe_continuous_vars = c("sum_umi", "sum_gene", "expr_chrM", "expr_chrM_ratio"),
default_cluster = "random_cluster"
)
}We also recommend creating custom website documentation files as described in the documentation of spatialLIBD::run_app(). Those documentation files will help you describe your project to your users in a more personalized way. The easiest way to start is to copy our documentation files to a new location and adapt them. You can located them at the following path.
## Locate our documentation files
docs_path <- system.file("app", "www", package = "spatialLIBD")
docs_path
#> [1] "/tmp/RtmpL81vCz/Rinst2744db1c864f6c/spatialLIBD/app/www"
list.files(docs_path)
#> [1] "README.md" "documentation_sce_layer.md"
#> [3] "documentation_spe.md" "favicon.ico"
#> [5] "footer.html"5 Limitations
spatialLIBD::run_app() has limitations that are inherent to the methods used to implement it, such as:
- the memory per user required by a server for hosting the web application,
- response speeds for the interactive views due to the number of spots,
- the resolution of the images displayed limiting the usefulness to magnify specific spots,
- and customization of the web application by the end user.
5.1 Memory
Regarding the memory limitation, you can estimate how much memory you need per user by considering the memory required for the spe and sce_layer objects.
pryr::object_size(spe)
#> 298 MBIn our pilot data (Maynard, Collado-Torres, Weber, et al., 2021) our object uses about 2.1 GB of RAM since it contains the data for 12 Visium slides and we considered using about 3 GB of RAM per user. You could filter the genes more aggressively to drop lowly expressed genes or if you have many Visium slides, you could consider making multiple websites for different sets of slides. You could also have multiple mirrors to support several users, though in that case, we recommend linking users to a stable website instead of one that might not be available if you have too many users: for us our stable website is http://research.libd.org/spatialLIBD/ which includes the links to all the mirrors.
Given these memory limitations, we chose to deploy our main web application at http://spatial.libd.org/spatialLIBD/ using an Amazon EC2 instance: an ‘r5.4xlarge’ EC2 instance with 16 vCPUs, 128 GB DRAM, 10 Gb max network, 1.008 USD/Hour. We also have deployed mirrors at https://www.shinyapps.io/ using the “3X-Large (8 GB)” instances they provide.
5.2 Response speeds
This limitation is mostly due to the number of spots shown under the “clusters (interactive)” section of the interactive website powered by plotly. Each spot is shown four times which is about 16 thousand spots for one Visium slide (depending on any filter steps you applied). The response time will depend on your own computer RAM memory, that is, the client side. This limitation might be more noticeable if you have a computer with 8GB of RAM or lower, as well as if you have other high-memory software open. Furthermore, if you are running web application locally through spatialLIBD::run_app() then you also need to consider the required memory for the R objects. That is, the server side memory use.
Thanks to Jesús Vélez Santiago, the app is more responsive as of version 1.3.15 by using plotly::toWebGL().
5.3 Image resolution
When you construct the SpatialExperiment spe object with SpatialExperiment, you can read in higher resolution images. However, the benefit of loading the raw histology images (500 MB to 20 GB per image) is likely non-existent in this web application. The memory required would likely become prohibitive. Other solutions load these raw histology images in chunks and display the chunks necessary for a given visualization area. We thus recommend using other software if you want to zoom in at the spot and/or cell resolution.
5.4 Customization
While the documentation, title, icon and HTML footer are all customizable at spatialLIBD::run_app(), ultimately the panels shown are not unless you fork and adapt the internal code of this package. Thus, the interactive web applications powered by spatialLIBD are not as easy to customize as say iSEE web applications are. We think of our web application as a good enough prototype that can be useful for initial explorations of 10x Genomics Visium data. We welcome additions to our code, though we recognize that you might want to build your own production-level solution.
6 Reproducibility
The spatialLIBD package (Pardo, Spangler, Weber, et al., 2021) was made possible thanks to:
- R (R Core Team, 2021)
- BiocFileCache (Shepherd and Morgan, 2021)
- BiocStyle (Oleś, 2021)
- knitr (Xie, 2021)
- pryr (Wickham, 2018)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2021)
- rtracklayer (Lawrence, Gentleman, and Carey, 2009)
- sessioninfo (Csárdi, core, Wickham, Chang, Flight, Müller, and Hester, 2018)
- SpatialExperiment (Righelli, Risso, Crowell, and Weber, 2021)
- testthat (Wickham, 2011)
This package was developed using biocthis.
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("TenX_data_download.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("TenX_data_download.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2021-05-20 14:49:13 EDT"Wallclock time spent generating the vignette.
#> Time difference of 1.526 minsR session information.
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#>
#> [1] /tmp/RtmpL81vCz/Rinst2744db1c864f6c
#> [2] /home/biocbuild/bbs-3.13-bioc/R/library7 Bibliography
This vignette was generated using BiocStyle (Oleś, 2021) with knitr (Xie, 2021) and rmarkdown (Allaire, Xie, McPherson, et al., 2021) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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