combine-predictions {SingleR} | R Documentation

## Combining results from different references

### Description

It is often desirable to combine information from separate references, thus improving the quality and breadth of the cell type annotation. However, it is not trivial due to the presence of batch effects across references (from differences in technology, experimental protocol or the biological system) as well as differences in the annotation vocabulary between investigators. This page describes some of the considerations with choosing a strategy to combine information from multiple reference datasets.

### Option 1 - using reference-specific labels

This option nests each label within each reference data, (e.g., “Ref1-Bcell” vs “Ref2-Bcell”). It is most applicable if there are relevant biological differences between the references, e.g., one reference is concerned with healthy tissue while the other reference considers diseased tissue.

In practical terms, this option is easily implemented by just `cbind`ing the expression matrices together and `paste`ing the reference name onto the corresponding character vector of labels. There is no need for time-consuming label harmonization between references.

However, the fact that we are comparing across references means that the marker set is likely to contain genes responsible for uninteresting batch effects. This will increase noise during the calculation of the score in each reference, possibly leading to a loss of precision and a greater risk of technical variation dominating the classification results.

### Option 2 - using harmonized labels

This option also involves combining the reference datasets into a single matrix but with harmonization of the labels so that the same cell type is given the same label across references. This would allow feature selection methods to identify robust sets of label-specific markers that are more likely to generalize to other datasets. It would also simplify interpretation, as there is no need to worry about the reference from which the labels came.

The most obvious problem with this approach is that it assumes that harmonized labels are available. This is not always the case due to differences in naming schemes (e.g. `"B cell"` vs `"B"`) between references. Another problem is that of differences in label resolution across references (e.g., how to harmonize `"B cell"` to another reference that splits to `"naive B cell"` and `"mature B cell"`).

To mitigate this, SingleR datasets (e.g., `ImmGenData`) have all their labels mapped to the Cell Ontology, allowing the use of standard terms to refer to the same cell type across references. Users can then traverse the ontology graph to achieve a consistent label resolution across references.

### Option 3 - comparing scores across the union of markers

This option involves performing classification separately within each reference, then collating the results to choose the label with the highest score across references. This is a relatively expedient approach that avoids the need for explicit harmonization while also reduces the potential for reference-specific markers. It is also logistically simpler as it allows each reference to be processed separately (more or less, depending on the exact algorithm) for embarrassing parallelization.

It leaves a mixture of labels in the final results that is up to the user to resolve, though perhaps this may be considered a feature as it smoothly handles differences in resolution between references, e.g., a cell that cannot be resolved as a CD4+ or CD8+ T cell may simply fall back to `"T cell"`. It will also be somewhat suboptimal if there are many reference-specific labels, as markers are not identified with the aim of distinguishing a label in one reference from another label in another reference.

### Author(s)

Aaron Lun

### See Also

`combineCommonResults` and `combineRecomputedResults`, for the functions that implement variants of Option 3.

`matchReferences`, to harmonize labels between reference datasets.

[Package SingleR version 1.1.13 Index]