The ambitions of collaborative single cell biology will only be achieved through the coordinated efforts of many groups, to help clarify cell types and dynamics in an array of functional and environmental contexts. The use of formal ontology in this pursuit is well-motivated and research progress has already been substantial.
Bakken et al. (2017) discuss “strategies for standardized cell type representations based on the data outputs from [high-content flow cytometry and single cell RNA sequencing], including ‘context annotations’ in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models.” Aevermann et al. (2018) describe how the FAIR principles can be implemented using statistical identification of necessary and sufficient conditions for determining cell class membership. They propose that Cell Ontology can be transformed to a broadly usable knowledgebase through the incorporation of accurate marker gene signatures for cell classes.
In this vignette, we review key concepts and tasks required to make progress in the adoption and application of ontological discipline in Bioconductor-oriented data analysis.
We’ll start by setting up some package attachments and ontology objects.
## Warning: replacing previous import 'utils::findMatches' by ## 'S4Vectors::findMatches' when loading 'AnnotationDbi'
library(ontoProc) library(ontologyPlot) library(BiocStyle) # for package references cl = getOnto("cellOnto", "2023") # for continuity -- has_high_plasma_membrane_amount: list go = getOnto("goOnto", "2023") # if updated, some assertions will fail... pr = getOnto("PROnto", "2023") # important case change
The following table describes the most up-to-date resources available with
Other resources are listed in
Definitions, semantics. For concreteness, we provide some definitions and examples.
ontology to denote the systematic organization
of terminology used in a conceptual domain. The
Cell Ontology is a graphical data structure with
carefully annotated terms as nodes and conventionally defined
semantic relationships among terms serving as edges. As
lung ciliated cell has URI .
This URI includes a fixed-length identifier
unambiguous interpretation wherever it is encountered. There is
a chain of relationships from
lung ciliated cell
ciliated cell, then
native cell, then
cell, each possessing its own URI and related
interpretive metadata. The relationship connecting the more precise
to the less precise term in this chain
Ciliated cell is equivalent to
native cell that
has plasma membrane part
cilium. Semantic characteristics of terms and relationships are
used to infer relationships among terms that may
not have relations directly specified in available ontologies.
Barriers to broad adoption. Given the wealth of material available in biological
ontologies, it is somewhat surprising that formal annotation
is so seldom used in practice. Barriers to
more common use of ontology in data annotation
include: (i) Non-existence of exact matching between intended
term and terms available in ontologies of interest.
(ii) The practical problem of decoding ontology identifiers.
A GO tag or CL tag is excellent
for programming, but it is clumsy to co-locate
with the tag the associated natural language term
or phrase. (iii) Likelihood of disagreement of suitability
of terms for conditions observed at the boundaries
of knowledge. To help cope with the first
of these problems, Bioconductor’s
includes a function
which will search an ontology for terms lexically
close to some target term or phrase. The
second problem can be addressed with more elaborate
data structures for variable annotation and programming in
R, and the third problem will diminish in
importance as the value of ontology adoption becomes
manifest in more applications.
Class vs. instance. It is important to distinguish the practice of designing and maintaining ontologies from the use of ontological class terms to annotate instances of the concepts. The combination of an ontology and a set of annotated instances is called a knowledge base. To illustrate some of the salient distinctions here, consider the cell line called A549, which is established from a human lung adenocarcinoma sample. There is no mention of A549 in the Cell Ontology. However, A549 is present in the EBI Experimental Factor Ontology as a subclass of the “Homo sapiens cell line” class. Presumably this is because A549 is a class of cells that are widely used experimentally, and this cell line constitutes a concept deserving of mapping in the universe of experimental factors. In the universe of concepts related to cell structure and function per se, A549 is an individual that can be characterized through possession of or lack of properties enumerated in Cell Ontology, but it is not deserving of inclusion in that ontology.
The 10X Genomics corporation has distributed a dataset on results of sequencing 10000 PBMC from a healthy donor . Subsets of the data are used in tutorials for the Seurat analytical suite (Butler et al. (2018)).
One result of the tutorial analysis of the 3000 cell subset is a table of cell types and expression-based markers of cell identity. The first three columns of the table below are from concluding material in the Seurat tutorial; the remaining columns are created by “manual” matching between the Seurat terms and terms found in Cell Ontology.
|0||IL7R||CD4 T cells||CD4-positive helper T cell||CL:0000492|
|1||CD14, LYZ||CD14+ Monocytes||CD14-positive monocyte||CL:0001054|
|2||MS4A1||B cells||B cell||CL:0000236|
|3||CD8A||CD8 T cells||CD8-positive, alpha-beta T cell||CL:0000625|
|4||FCGR3A, MS4A7||FCGR3A+ Monocytes||monocyte||CL:0000576|
|5||GNLY, NKG7||NK cells||natural killer cell||CL:0000623|
|6||FCER1A, CST3||Dendritic Cells||dendritic cell||CL:0000451|
Given the informally selected tags in the table above, we can
sketch the Cell Ontology graph connecting the associated
cell types. The ontoProc package adds functionality to
allows use of all Rgraphviz and igraph visualization facilities
for graphs derived from ontology structures.
Here we display the PBMC cell sets reported in the Seurat tutorial.
## loading from cache
CLfeats function traces relationships and
properties from a given Cell Ontology class.
Briefly, each class can assert that it is the
intersection_of other classes, and
lacks_plasma_membrane_part can be asserted as
relationships holding between cell type instances
and cell components. The components are often cross-referenced
to Protein Ontology or Gene Ontology. When the Protein Ontology
component has a synonym for which an HGNC symbol is provided, that
symbol is retrieved by
CLfeats. Here we obtain the listing
for a mature CD1a-positive dermal dendritic cell.
|CL:0000451||PR:000001002||lacksPMP||CD19 molecule||CD19||dendritic cell|
|CL:0000451||PR:000001003||lacksPMP||CD34 molecule||CD34||dendritic cell|
|CL:0000451||PR:000001020||lacksPMP||CD3 epsilon||CD3E||dendritic cell|
|CL:0000451||PR:000001024||lacksPMP||neural cell adhesion molecule 1||NCAM1||dendritic cell|
|CL:0000451||PR:000001289||lacksPMP||membrane-spanning 4-domains subfamily A member 1||MS4A1||dendritic cell|
|CL:0000451||GO:0042613||hasPart||MHC class II protein complex||NA||dendritic cell|
ctmarks function starts a shiny app that generates
tables of this sort for selected cell types.
sym2CellOnto function helps find mention of
given gene symbols in properties or parts of cell types.
|ITGAM||lacksPMP||CL:0000037||hematopoietic stem cell|
|ITGAM||lacksPMP||CL:0000553||megakaryocyte progenitor cell|
|ITGAM||lacksPMP||CL:0000611||eosinophil progenitor cell|
|ITGAM||lacksPMP||CL:0000613||basophil progenitor cell|
|ITGAM||lacksPMP||CL:0000831||mast cell progenitor|
|ITGAM||lacksPMP||CL:0000837||hematopoietic multipotent progenitor cell|
|ITGAM||lacksPMP||CL:0000872||splenic marginal zone macrophage|
|ITGAM||lacksPMP||CL:0000941||thymic conventional dendritic cell|
|ITGAM||lacksPMP||CL:0000942||thymic plasmacytoid dendritic cell|
|ITGAM||lacksPMP||CL:0000989||CD11c-low plasmacytoid dendritic cell|
|ITGAM||lacksPMP||CL:0000998||CD8_alpha-negative CD11b-negative dendritic cell|
|ITGAM||lacksPMP||CL:0001000||CD8_alpha-positive CD11b-negative dendritic cell|
|ITGAM||lacksPMP||CL:0001029||common dendritic progenitor|
|ITGAM||lacksPMP||CL:0001060||hematopoietic oligopotent progenitor cell, lineage-negative|
|ITGAM||lacksPMP||CL:0001066||erythroid progenitor cell, mammalian|
|ITGAM||lacksPMP||CL:0002010||pre-conventional dendritic cell|
|ITGAM||lacksPMP||CL:0002089||group 2 innate lymphoid cell, mouse|
|ITGAM||lacksPMP||CL:0002679||natural helper lymphocyte|
## ## lacksPMP ## 22
|FOXP3||hasPart||CL:0000902||induced T-regulatory cell|
|FOXP3||hasPart||CL:0000903||natural T-regulatory cell|
|FOXP3||hasPart||CL:0000919||CD8-positive, CD25-positive, alpha-beta regulatory T cell|
|FOXP3||hasPart||CL:0000920||CD8-positive, CD28-negative, alpha-beta regulatory T cell|
The task of extending an ontology is partly bureaucratic in
nature and depends on a collection of endorsements and updates
to centralized information structures. In order to permit
experimentation with interfaces and new content that may
be quite speculative, we include an approach to combining new
ontology ‘terms’ of structure similar to those endorsed in
Cell Ontology, to ontologyIndex-based
For a demonstration, we consider the discussion in Bakken et al. (2017), of a ‘diagonal’ expression pattern defining a group of novel cell types. A set of genes is identified and cells are distinguised by expressing exactly one gene from the set.