HCATonsilData is an R/ExperimentHub package that provides easy access to single-cell RNA-seq (scRNA-seq), single-cell ATAC-seq (scATAC-seq), 10X Multiome, CITE-seq and spatial transcriptomics data (Visium) derived from the tonsil cell atlas project.
The preprint was published in June 2022:
And the final paper was accepted in September 2023, so we expect it to be published December of this year.
HCATonsilData is available in BioConductor and can be installed as follows:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("HCATonsilData")Alternatively, you can install it from GitHub using the devtools package:
if (!require("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("massonix/HCATonsilData", build_vignettes = TRUE)HCATonsilData is a data package. As such, comprehensive documentation is an essential component of the package, and we provide it through the package vignette:
browseVignettes("HCATonsilData")
HCATonsilData has two versions: version 1.0 corresponds to the first data release that came out during the preprint. During the revision process, we included 7 additional tonsils, and almost doubled the number of cells of the atlas. We provide this data in version 2.0, which is the one associated with the final publication.
The data for version 2.0 was uploaded to ExperimentHub just before the 3.18 release of BioConductor. Thus, you will need the release >= 3.18 of Bioconductor, and R >= 4.3.0
We provide access to SingleCellExperiment objects of the main cellular compartments
described in our manuscript. The function listCellTypes prints the available
cell types for a given assay (RNA, ATAC, CITE or Spatial):
listCellTypes(assayType = "RNA")
[1] "All" "NBC-MBC" "GCBC"
[4] "PC" "CD4-T" "Cytotoxic"
[7] "myeloid" "FDC" "epithelial"
[10] "PDC"
HCATonsilAtlas data allows to install scRNA-seq and spatial transcriptomics data as SingleCellExperiments and SpatialExperiments, respectively. For scATAC-seq, Multiome, and CITE-seq we provide in the vignette the instructions on how to access the data archived at Zenodo Note that for now HCATonsilAtlas only provides access to RNA data, but we are working hard to extend it to other modalities, which are/will be archived in Zenodo as Seurat objects.
To obtain the SingleCellExperiment object associated with a given cell type we use
the HCATonsilData() function:
(myeloid <- HCATonsilData(assayType = "RNA", cellType = "myeloid", version = "2.0"))
class: SingleCellExperiment
dim: 37378 5334
metadata(0):
assays(2): counts logcounts
rownames(37378): AL627309.1 AL627309.3 ... AC136616.1 AC023491.2
rowData names(3): gene_name highly_variable gene_id
colnames(5334): bw94nf57_vm85woki_AAAGTGACAAGGAGTC-1 bw94nf57_vm85woki_AAGCATCCACTAGTAC-1 ...
zoxefjul_5m23u91h_TTAGCCTGTTCGGGAT-1 zoxefjul_5m23u91h_TTATTGCTCCTTAATC-1
colData names(39): barcode donor_id ... UMAP_1_20230508 UMAP_2_20230508
reducedDimNames(3): PCA UMAP HARMONY
mainExpName: NULL
altExpNames(0):
library(patchwork)
p1 <- scater::plotUMAP(myeloid, colour_by = "annotation_20230508")
p2 <- scater::plotUMAP(myeloid, colour_by = "SELENOP")
p1 | p2
10X Visium data can be installed as follows:
(spe <- HCATonsilData("Spatial"))
class: SpatialExperiment
dim: 26846 16224
metadata(0):
assays(2): counts logcounts
rownames(26846): AL627309.1 AL627309.5 ... AC007325.4 AC007325.2
rowData names(8): gene_name vst.mean ... gene_id highly_variable
colnames: NULL
colData names(24): barcode donor_id ... area sample_id
reducedDimNames(3): HARMONY PCA UMAP
mainExpName: Spatial
altExpNames(0):
spatialCoords names(0) :
imgData names(4): sample_id image_id data scaleFactor
To plot gene expression you can use the ggspavis package:
library(ggspavis)
sub <- spe[, spe$sample_id == "esvq52_nluss5"]
plt <- plotVisium(sub, fill="SELENOP") +
scale_fill_gradientn(colors=rev(hcl.colors(9, "Spectral")))
plt$layers[[2]]$aes_params$size <- 1.5
plt$layers[[2]]$aes_params$alpha <- 1
plt$layers[[2]]$aes_params$stroke <- NA
plt
To allow users to traceback the rationale behind each and every of our annotations, we provide a detailed glossary of 121 cell types and states and related functions to get the explanation, markers and references of every annotation. You can acces the glossary as a dataframe as follows:
glossary_df <- TonsilData_glossary()
To get the glossary for each cell type with nice printing formatting you can use
the TonsilData_cellinfo() function:
TonsilData_cellinfo("Tfr")
Tfr
------------------------------
Annotation Level 1: CD4_T
Cell Markers: T-follicular regulatory cells in the tonsils are CD25-. These cells down-regulate effector Treg markers (IL2RA, FOXP3, CTLA4). This cluster expressed high levels of FCRL3, CLNK, LEF1, TCF7, RBMS3, SESN3, and PDE3B. The top marker FCRL3 can bind secretory IgA to suppress the Tfr inhibitory function. TCF7 and LEF1 are essential for Tfr development in mice (Wing et al., 2017; Agarwal et al., 2020 ; Yang et al., 2019).
Cell Markers: FCRL3,CLNK,LEF1,TCF7,RBMS3,SESN3,PDE3B
Related references: Wing2017|10.1073/pnas.1705551114;Agarwal2020|10.1016/j.celrep.2019.12.099;Yang2019|10.1016/j.celrep.2019.05.061
Alternatively, you can get a static html with links to markers and articles with
TonsilData_cellinfo_html
TonsilData_cellinfo_html("Tfr")
Although we have put massive effort in annotating tonsillar cell types, cell type
annotations are dynamic by nature. New literature or other interpretations of the
data can challenge and refine our annotations. To accommodate this, we have developed
the updateAnnotation function, which allows to periodically provide newer
annotations as extra columns in the colData slot of the SingleCellExperiment
objects. If you want to contribute in one of these versions of the upcoming annotations,
please open an issue and
describe your annotation.
While we provide data in the form of SingleCellExperiment objects, you may want to analyze your data using a different single-cell data container. In future releases, we will strive to make the SingleCellExperiment objects compatible with Seurat v5, which will come out after this release of BioConductor. Alternatively, you may want to obtain AnnData objects to analyze your data in scanpy ecosystem. You can convert and save the data as follows:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("zellkonverter")
library(zellkonverter)
epithelial <- HCATonsilData(assayType = "RNA", cellType = "epithelial")
writeH5AD(sce = epithelial, file = "epithelial.h5ad")