celldex 1.16.0
The celldex package provides convenient access to several cell type reference datasets. Most of these references are derived from bulk RNA-seq or microarray data of cell populations that (hopefully) consist of a pure cell type after sorting and/or culturing. The aim is to provide a common resource for further analysis like cell type annotation of single cell expression data or deconvolution of cell type proportions in bulk expression datasets.
Each dataset contains a log-normalized expression matrix that is intended to be comparable
to log-UMI counts from common single-cell protocols (Aran et al. 2019)
or gene length-adjusted values from bulk datasets.
By default, gene annotation is returned in terms of gene symbols,
but they can be coerced to Ensembl annotation with ensembl=TRUE for more robust cross-referencing across studies.
Typically, each reference provides three levels of cell type annotation in its column metadata:
label.main, broad annotation that defines the major cell types.
This has few unique levels that allows for fast annotation but at low resolution.label.fine, fine-grained annotation that defines subtypes or states.
This has more unique levels that results in slower annotation but at much higher resolution.label.ont, fine-grained annotation mapped to the standard vocabulary in the Cell Ontology.
This enables synchronization of labels across references as well as dynamic adjustment of the resolution.We can examine the available references using the surveyReferences() function.
This returns a DataFrame of the reference’s name and version,
along with additional information like the title, description, species, number of samples, available labels, and so on.
library(celldex)
surveyReferences()## DataFrame with 7 rows and 10 columns
## name version path title
## <character> <character> <character> <character>
## 1 dice 2024-02-26 NA Human bulk RNA-seq d..
## 2 blueprint_encode 2024-02-26 NA Human bulk RNA-seq d..
## 3 immgen 2024-02-26 NA Mouse microarray exp..
## 4 mouse_rnaseq 2024-02-26 NA Bulk RNA-seq data of..
## 5 hpca 2024-02-26 NA Microarray data from..
## 6 novershtern_hematopo.. 2024-02-26 NA Bulk microarray expr..
## 7 monaco_immune 2024-02-26 NA Human bulk RNA-seq d..
## description taxonomy_id genome samples
## <character> <List> <List> <integer>
## 1 Human bulk RNA-seq d.. 9606 1561
## 2 Human bulk RNA-seq d.. 9606 259
## 3 Mouse microarray exp.. 10090 830
## 4 Bulk RNA-seq data of.. 10090 MGSCv37 358
## 5 Microarray data from.. 9606 713
## 6 Bulk microarray expr.. 9606 211
## 7 Human bulk RNA-seq d.. 9606 GRCh38 114
## labels
## <List>
## 1 label.main,label.fine,label.ont
## 2 label.main,label.fine,label.ont
## 3 label.main,label.fine,label.ont
## 4 label.main,label.fine,label.ont
## 5 label.main,label.fine,label.ont
## 6 label.main,label.fine,label.ont
## 7 label.main,label.fine,label.ont
## sources
## <SplitDataFrameList>
## 1 PubMed:30449622:NA,ExperimentHub:EH3488:NA,ExperimentHub:EH3489:NA
## 2 PubMed:22955616:NA,PubMed:24091925:NA,PubMed:30643263:NA
## 3 PubMed:18800157:NA,GEO:GSE15907:NA,GEO:GSE37448:NA
## 4 PubMed:30858345:NA,URL:https://github.com/B..:NA,PubMed:30643263:NA
## 5 PubMed:24053356:NA,PubMed:30643263:NA,GitHub:dviraran/SingleR:adc4a0e4d5cfa79db18f..
## 6 PubMed:21241896:NA,GEO:GSE24759:NA,ExperimentHub:EH3490:NA
## 7 PubMed:30726743:NA,GEO:GSE107011:NA,ExperimentHub:EH3496:NAAlternatively, users can search the text of each reference’s metadata to identify relevant datasets. This may require some experimentation as it depends on the level of detail in the metadata supplied by the uploader.
searchReferences("B cell")## DataFrame with 4 rows and 10 columns
## name version path title
## <character> <character> <character> <character>
## 1 dice 2024-02-26 NA Human bulk RNA-seq d..
## 2 mouse_rnaseq 2024-02-26 NA Bulk RNA-seq data of..
## 3 novershtern_hematopo.. 2024-02-26 NA Bulk microarray expr..
## 4 monaco_immune 2024-02-26 NA Human bulk RNA-seq d..
## description taxonomy_id genome samples
## <character> <List> <List> <integer>
## 1 Human bulk RNA-seq d.. 9606 1561
## 2 Bulk RNA-seq data of.. 10090 MGSCv37 358
## 3 Bulk microarray expr.. 9606 211
## 4 Human bulk RNA-seq d.. 9606 GRCh38 114
## labels
## <List>
## 1 label.main,label.fine,label.ont
## 2 label.main,label.fine,label.ont
## 3 label.main,label.fine,label.ont
## 4 label.main,label.fine,label.ont
## sources
## <SplitDataFrameList>
## 1 PubMed:30449622:NA,ExperimentHub:EH3488:NA,ExperimentHub:EH3489:NA
## 2 PubMed:30858345:NA,URL:https://github.com/B..:NA,PubMed:30643263:NA
## 3 PubMed:21241896:NA,GEO:GSE24759:NA,ExperimentHub:EH3490:NA
## 4 PubMed:30726743:NA,GEO:GSE107011:NA,ExperimentHub:EH3496:NAsearchReferences(
defineTextQuery("immun%", partial=TRUE) &
defineTextQuery("10090", field="taxonomy_id")
)## DataFrame with 1 row and 10 columns
## name version path title
## <character> <character> <character> <character>
## 1 immgen 2024-02-26 NA Mouse microarray exp..
## description taxonomy_id genome samples
## <character> <List> <List> <integer>
## 1 Mouse microarray exp.. 10090 830
## labels
## <List>
## 1 label.main,label.fine,label.ont
## sources
## <SplitDataFrameList>
## 1 PubMed:18800157:NA,GEO:GSE15907:NA,GEO:GSE37448:NAKeep in mind that the search results are not guaranteed to be reproducible - more datasets may be added over time, and existing datasets may be updated with new versions. Once a dataset of interest is identified, users should explicitly list the name and version of the dataset in their scripts to ensure reproducibility.
The HPCA reference consists of publicly available microarray datasets derived from human primary cells (Mabbott et al. 2013). Most of the labels refer to blood subpopulations but cell types from other tissues are also available.
library(celldex)
ref <- fetchReference("hpca", "2024-02-26")This reference also contains many cells and cell lines that have been treated or collected from pathogenic conditions.
The Blueprint/ENCODE reference consists of bulk RNA-seq data for pure stroma and immune cells generated by Blueprint (Martens and Stunnenberg 2013) and ENCODE projects (The ENCODE Project Consortium 2012).
ref <- fetchReference("blueprint_encode", "2024-02-26")This reference is best suited to mixed samples that do not require fine resolution, and is particularly suited for situations where easily interpretable labels are required quickly. It provides decent immune cell granularity, though it does not contain finer monocyte and dendritic cell subtypes.
This reference consists of a collection of mouse bulk RNA-seq data sets downloaded from the gene expression omnibus (Benayoun et al. 2019). A variety of cell types are available, again mostly from blood but also covering several other tissues.
ref <- fetchReference("mouse_rnaseq", "2024-02-26")This reference is best suited to bulk tissue samples from brain, blood, or heart where low-resolution labels are adequate.
The ImmGen reference consists of microarray profiles of pure mouse immune cells from the project of the same name (Heng et al. 2008). This is currently the most highly resolved immune reference - possibly overwhelmingly so, given the granularity of the fine labels.
ref <- fetchReference("immgen", "2024-02-26")This reference provides exhaustive coverage of a dizzying number of cell subtypes. However, this can be a double-edged sword as the high resolution can be difficult to interpret, especially for samples derived from experimental conditions that are not of interest. Users may want to remove certain samples themselves depending on the use case.
The DICE reference consists of bulk RNA-seq samples of sorted cell populations from the project of the same name (Schmiedel et al. 2018).
ref <- fetchReference("dice", "2024-02-26")This reference is particularly useful to those interested in CD4+ T cell subsets, though the lack of CD4+ central memory and effector memory samples may decrease accuracy in some cases. In addition, the lack of dendritic cells and a single B cell subset may result in those populations being improperly labeled or having their label pruned in a typical PBMC sample.
The Novershtern reference (previously known as Differentiation Map) consists of microarray datasets for sorted hematopoietic cell populations from GSE24759 (Novershtern et al. 2011).
ref <- fetchReference("novershtern_hematopoietic", "2024-02-26")This reference provides the greatest resolution for myeloid and progenitor cells among the human immune references. It has fewer T cell subsets than the other immune references but contains many more NK, erythroid, and granulocytic subsets. It is likely the best option for bone marrow samples.
The Monaco reference consists of bulk RNA-seq samples of sorted immune cell populations from GSE107011 (Monaco et al. 2019).
ref <- fetchReference("monaco_immune", "2024-02-26")This is the human immune reference that best covers all of the bases for a typical PBMC sample. It provides expansive B and T cell subsets, differentiates between classical and non-classical monocytes, includes basic dendritic cell subsets, and also includes neutrophil and basophil samples to help identify small contaminating populations that may have slipped into a PBMC preparation.
Want to contribute your own reference dataset to this package? It’s easy! Just follow these simple steps for instant fame and prestige.
Obtain log-normalized expression values and cell type labels.
While not strictly required, we recommend trying to organize the labels into label.fine, label.main and label.ont,
so that users have a consistent experience with your reference.
Let’s just make up something here.
norm <- matrix(runif(1000), ncol=20)
rownames(norm) <- sprintf("GENE_%i", seq_len(nrow(norm)))
labels <- DataFrame(label.main=rep(LETTERS[1:5], each=4))
labels$label.fine <- sprintf("%s%i", labels$label.main, rep(c(1, 1, 2, 2), 5))
labels$label.ont <- sprintf("CL:000%i", rep(1:5, each=4))Assemble the metadata for your dataset.
This should be a list structured as specified in the Bioconductor metadata schema.
Check out some examples from fetchMetadata() - note that the application.takane property will be automatically added later, and so can be omitted from the list that you create.
meta <- list(
title="My reference",
description="This is my reference dataset",
taxonomy_id="10090",
genome="GRCh38",
sources=list(
list(provider="GEO", id="GSE12345"),
list(provider="PubMed", id="1234567")
),
maintainer_name="Chihaya Kisaragi",
maintainer_email="[email protected]"
)Save your normalized expression matrix and labels to disk with saveReference().
This saves the dataset into a “staging directory” using language-agnostic file formats - check out the alabaster framework for more details.
In more complex cases involving multiple datasets, users may save each dataset into a subdirectory of the staging directory.
# Simple case: you only have one dataset to upload.
staging <- tempfile()
saveReference(norm, labels, staging, meta)
list.files(staging, recursive=TRUE)## [1] "OBJECT" "_bioconductor.json"
## [3] "assays/0/OBJECT" "assays/0/array.h5"
## [5] "assays/names.json" "column_data/OBJECT"
## [7] "column_data/basic_columns.h5" "row_data/OBJECT"
## [9] "row_data/basic_columns.h5"# Complex case: you have multiple subdatasets to upload.
staging <- tempfile()
dir.create(staging)
saveReference(norm, labels, file.path(staging, "foo"), meta)
saveReference(norm, labels, file.path(staging, "bar"), meta) # and so on.You can check that everything was correctly saved by reloading the on-disk data into the R session for inspection.
This yields a SummarizedExperiment with the log-expression matrix in the assay named "logcounts".
alabaster.base::readObject(file.path(staging, "foo"))## class: SummarizedExperiment
## dim: 50 20
## metadata(0):
## assays(1): logcounts
## rownames(50): GENE_1 GENE_2 ... GENE_49 GENE_50
## rowData names(0):
## colnames: NULL
## colData names(3): label.main label.fine label.ontOpen a pull request (PR) for the addition of a new reference. You will need to provide a few things here:
scripts/ directory of this package,
in order to provide some record of how the dataset was created.Wait for us to grant temporary upload permissions to your GitHub account.
Upload your staging directory to gypsum backend with gypsum::uploadDirectory().
On the first call to this function, it will automatically prompt you to log into GitHub so that the backend can authenticate you.
If you are on a system without browser access (e.g., most computing clusters), a token can be manually supplied via gypsum::setAccessToken().
gypsum::uploadDirectory(staging, "celldex", "my_dataset_name", "my_version")You can check that everything was successfully uploaded by just calling fetchReference():
fetchReference("my_dataset_name", "my_version")If you realized you made a mistake, no worries. Use the following call to clear the erroneous dataset, and try again:
gypsum::rejectProbation("scRNAseq", "my_dataset_name", "my_version")Comment on the PR to notify us that the dataset has finished uploading and you’re happy with it. We’ll review it and make sure everything’s in order. If some fixes are required, we’ll just clear the dataset so that you can upload a new version with the necessary changes. Otherwise, we’ll approve the dataset. Note that once a version of a dataset is approved, no further changes can be made to that version; you’ll have to upload a new version if you want to modify something.
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
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## other attached packages:
## [1] celldex_1.16.0 SummarizedExperiment_1.36.0
## [3] Biobase_2.66.0 GenomicRanges_1.58.0
## [5] GenomeInfoDb_1.42.0 IRanges_2.40.0
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## [83] pkgconfig_2.0.3Aran, D., A. P. Looney, L. Liu, E. Wu, V. Fong, A. Hsu, S. Chak, et al. 2019. “Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.” Nat. Immunol. 20 (2): 163–72.
Benayoun, Bérénice A., Elizabeth A. Pollina, Param Priya Singh, Salah Mahmoudi, Itamar Harel, Kerriann M. Casey, Ben W. Dulken, Anshul Kundaje, and Anne Brunet. 2019. “Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses.” Genome Research 29: 697–709. https://doi.org/10.1101/gr.240093.118.
Heng, Tracy S. P., Michio W. Painter, Kutlu Elpek, Veronika Lukacs-Kornek, Nora Mauermann, Shannon J. Turley, Daphne Koller, et al. 2008. “The immunological genome project: Networks of gene expression in immune cells.” Nature Immunology 9 (10): 1091–4. https://doi.org/10.1038/ni1008-1091.
Mabbott, Neil A., J. K. Baillie, Helen Brown, Tom C. Freeman, and David A. Hume. 2013. “An expression atlas of human primary cells: Inference of gene function from coexpression networks.” BMC Genomics 14. https://doi.org/10.1186/1471-2164-14-632.
Martens, Joost H A, and Hendrik G. Stunnenberg. 2013. “BLUEPRINT: Mapping human blood cell epigenomes.” Haematologica 98: 1487–9. https://doi.org/10.3324/haematol.2013.094243.
Monaco, Gianni, Bernett Lee, Weili Xu, Seri Mustafah, You Yi Hwang, Christophe Carré, Nicolas Burdin, et al. 2019. “RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.” Cell Reports 26 (6): 1627–1640.e7. https://doi.org/10.1016/j.celrep.2019.01.041.
Novershtern, Noa, Aravind Subramanian, Lee N. Lawton, Raymond H. Mak, W. Nicholas Haining, Marie E. McConkey, Naomi Habib, et al. 2011. “Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis.” Cell 144 (2): 296–309. https://doi.org/10.1016/j.cell.2011.01.004.
Schmiedel, Benjamin J., Divya Singh, Ariel Madrigal, Alan G. Valdovino-Gonzalez, Brandie M. White, Jose Zapardiel-Gonzalo, Brendan Ha, et al. 2018. “Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression.” Cell 175 (6): 1701–1715.e16. https://doi.org/10.1016/j.cell.2018.10.022.
The ENCODE Project Consortium. 2012. “An integrated encyclopedia of DNA elements in the human genome.” Nature. https://doi.org/10.1038/nature11247.