---
title: "ontoProc: Ontology interfaces for Bioconductor, with focus on cell type identification"
author: "Vincent J. Carey, stvjc at channing.harvard.edu"
date: "`r format(Sys.time(), '%B %d, %Y')`"
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor}
  %\VignetteEncoding{UTF-8}
bibliography: ontobib.bib
output:
  BiocStyle::html_document:
    highlight: pygments
    number_sections: yes
    theme: united
    toc: yes
---

# Introduction



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. 

@Bakken2017 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."
@Aevermann2018 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.
```{r setup,echo=FALSE,results="hide",message=FALSE}
library(knitr)
library(ontoProc)
cl = getOnto("cellOnto", "2023") # for continuity -- 	has_high_plasma_membrane_amount: list
#	has_low_plasma_membrane_amount: list are present in 2021 not 2022 - seems these have
# moved to RO (relations ontology)
#> grep("plasma", nn, value=TRUE)
#                       RO:0002104                        RO:0015015 
#       "has plasma membrane part" "has high plasma membrane amount" 
#                       RO:0015016 
# "has low plasma membrane amount" 
#
go = getOnto("goOnto")
pr = getOnto("PROnto")
library(ontologyPlot)
library(BiocStyle)
library(SingleCellExperiment)
library(celldex)
```
```{r setup2, message=FALSE}
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
```


# Scope of package

The following table describes the most up-to-date resources available with `getOnto`.
<!-- use inst/scripts/desc.R to generate all but the 'purpose' which is added by hand -->
```{r lksco}
data(packDesc2023)
kable(packDesc2023[,-c(1,7)])
```
Other resources are listed in `packDesc202x` and `packDesc2019`.

# Methods

## Conceptual overview of ontology with cell types

__Definitions, semantics.__ For concreteness, we provide some definitions and examples.
We use `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 
an example, `lung ciliated cell` has URI \url{http://purl.obolibrary.org/obo/CL_1000271}.
This URI includes a fixed-length identifier `CL_1000271` with 
unambiguous interpretation wherever it is encountered.  There is 
a chain of relationships from `lung ciliated cell` 
up through `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 
is denoted `SubclassOf`.  `Ciliated cell` is equivalent to 
a `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 `ontologyProc` package 
includes a function `liberalMap`
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.

## Illustration in a single-cell RNA-seq dataset

The 10X Genomics corporation has distributed
a dataset on results of sequencing 10000 PBMC from a healthy donor
\url{https://support.10xgenomics.com/single-cell-gene-expression/datasets}.
Subsets of the data are used in tutorials for the Seurat analytical 
suite (@butler).

### Labeling PBMC in the Seurat tutorial

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.

```{r lklk}
kable(stab <- seur3kTab())
```

### Relationships asserted in the Cell Ontology

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
ontologyPlot with `make_graphNEL_from_ontology_plot`.  This
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.
```{r lklklk}
library(ontoProc)
cl = getOnto("cellOnto", "2023")
onto_plot2(cl, stab$tag)
```

### Molecular features asserted in the Cell Ontology

The `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
`has_part`, `lacks_part`, `has_plasma_membrane_part`,
`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.
```{r lkfa}
suppressMessages({
kable(CLfeats(cl, "CL:0002531", pr=pr, go=go))
})
```

The `ctmarks` function starts a shiny app that generates
tables of this sort for selected cell types.

![ctmarks snapshot](ctmarks.png)

### Mapping from gene 'presence/role' to cell type

The `sym2CellOnto` function helps find mention of
given gene symbols in properties or parts of cell types.

```{r lksy}
kable(sdf <- as.data.frame(sym2CellOnto("ITGAM", cl, pr)))
table(sdf$cond)
kable(as.data.frame(sym2CellOnto("FOXP3", cl, pr)))
```

## Adding terms to ontology_index structures to 'extend' Cell Ontology

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 `ontology_index`
instances.

### Use case: a set of cell types defined by "diagonal expression"

For a demonstration, we consider the discussion in
@Bakken2017, 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.

![Diagonal expression pattern.](usecaseCyclic.png)

The necessary information is collected in a vector.
The vector is the set of genes, the name of element i
is the tag to be associated with the type of cell that expresses gene i
and does not express any other gene in the set.
```{r lksig}
sigels = c("CL:X01"="GRIK3", "CL:X02"="NTNG1", "CL:X03"="BAGE2",
             "CL:X04"="MC4R", "CL:X05"="PAX6", "CL:X06"="TSPAN12", 
             "CL:X07"="hSHISA8", "CL:X08"="SNCG", "CL:X09"="ARHGEF28", 
             "CL:X10"="EGF")
```

### A data.frame defining the cell types and their properties

The `cyclicSigset` function produces a data.frame instance
connecting cell types with the genes expressed or unexpressed.
```{r lkdfff}
cs = cyclicSigset(sigels)
dim(cs)
cs[c(1:5,9:13),]
table(cs$cond)
```
It is expected that a tabular layout like this will suffice to 
handle general situations of cell type definition.

### Translating the data.frame elements to OBO Term instances

The most complicated aspect of novel OBO term construction is the
proper specifications of relationships with existing ontology components.
A prolog that is mostly shared by all terms is generated programmatically
for the diagonal pattern task.
```{r lklk1}
 makeIntnProlog = function(id, ...) {
 # make type-specific prologs as key-value pairs
     c(  
       sprintf("id: %s", id),
       sprintf("name: %s-expressing cortical layer 1 interneuron, human", ...),
       sprintf("def: '%s-expressing cortical layer 1 interneuron, human described via RNA-seq observations' [PMID 29322913]", ...),
       "is_a: CL:0000099 ! interneuron",
       "intersection_of: CL:0000099 ! interneuron")
 }
```
The `ldfToTerms` API uses this to create a set of strings that can be parsed
as a term.
```{r doterm}
pmap = c("hasExp"="has_expression_of", lacksExp="lacks_expression_of")
head(unlist(tms <- ldfToTerms(cs, pmap, sigels, makeIntnProlog)), 20)
```

The content in tms can then be appended to the content of the Cell Ontology cl.obo as
text for import with `ontologyIndex::get_OBO`.

## Subsetting SingleR resources using ontological mapping

### A data.frame mapping from informal to formal terms

Aaron Lun has produced a mapping from informal terms used in the
Human Primary Cell Atlas to Cell Ontology tags.  We provisionally
include a copy of this mapping in ontoProc:

```{r lkmap}
hpca_map = read.csv(system.file("extdata/hpca.csv", package="ontoProc"), strings=FALSE)
head(hpca_map)
```

We will rename columns of this map for convenience of our `bind_formal_tags` method.
```{r doren}
names(hpca_map) = c("informal", "formal")  # obligatory for now
```

### Binding formal tags to the HPCA data

I am turning this code off for now because there is no
standard approach to getting the mapping from the SummarizedExperment
yet.  When SingleR merges the 'standardized' branch, this will come back.

Let's retrieve the HPCA data from SingleR:
```{r gethpca, eval=TRUE}
library(SingleCellExperiment)
library(celldex)
hpca_sce = HumanPrimaryCellAtlasData()
```
Now bind the formal tags:
```{r dobind, eval=TRUE}
hpca_sce = bind_formal_tags(hpca_sce, "label.fine", hpca_map)
length(unique(hpca_sce$label.ont))
```
We don't check for failed mappings:
```{r justna, eval=TRUE}
length(xx <- which(is.na(hpca_sce$label.ont)))
if (length(xx)>0) print(colData(hpca_sce)[xx,])
sum(hpca_sce$label.ont == "", na.rm=TRUE) # iPS and BM
```

### Subsetting using the class hierarchy of Cell Ontology

```{r dosub, eval=TRUE}
cell_onto = ontoProc::getOnto("cellOnto", "2023")
hpca_mono = subset_descendants( hpca_sce, cell_onto, "^monocyte$" )
table(hpca_mono$label.fine)
table(hpca_mono$label.ont) # not much diversity
hpca_tcell = subset_descendants( hpca_sce, cell_onto, "^T cell$" )
table(hpca_tcell$label.fine)
table(hpca_tcell$label.ont) # 
uu = unique(hpca_tcell$label.ont)
onto_plot2(cell_onto, uu)
```

# Disease concept relationships

The Experimental Factor Ontology
is available and integrates information
among diverse ontologies.  Here we
check on terms likely related to asthma.

```{r lkefo}
ef = getOnto("efoOnto")
alla <- grep("sthma", ef$name, value=TRUE) 
aa <- grep("obso", alla, invert=TRUE, value=TRUE)
onto_plot2(ef, names(aa))
```

However, the Human Disease Ontology seems more developed
in terms of defining asthma subtypes.  We have not
integrated that ontology into ontoProc yet, but it
can be retrieved conveniently as follows:
```{r lkhdo,eval=FALSE}
hdo_2022_09 = get_OBO(
  "https://github.com/DiseaseOntology/HumanDiseaseOntology/raw/main/src/ontology/HumanDO.obo", 
  extract_tags = "everything"
  )
```
With this resource, we can see finer-grained handling of asthma subtyping:

![](hdoasth.png)

# Related tools

Inference on the identities of cells assayed in 
a single cell transcriptomics experiment can be performed 
using the Bioconductor `celaref` package.  This package includes 
a number of reference data resources providing whole-transcriptome 
profiles of cells of known and unknown type.
An approach to systematically structuring data on cell-type
signatures, and conducting inference on cell types in
new experiments, is provided in the [Hancock package](https://github.com/kevinrue/Hancock),
under development.

A CRAN package that is very useful for
R programming with ontologies is `ontologyIndex` @Westbury2015.  This
provides easily used functions for parsing ontologies in
the OBO format and for performing basic queries
on text fields and list structures.

# References
