1 Introduction

SingleR is an automatic annotation method for single-cell RNA sequencing (scRNAseq) data (Aran et al. 2019). Given a reference dataset of samples (single-cell or bulk) with known labels, it labels new cells from a test dataset based on similarity to the reference set. Specifically, for each test cell:

  1. We compute the Spearman correlation between its expression profile and that of each reference sample. This is done across the union of marker genes identified between all pairs of labels.
  2. We define the per-label score as a fixed quantile (by default, 0.8) of the distribution of correlations.
  3. We repeat this for all labels and we take the label with the highest score as the annotation for this cell.
  4. We optionally perform a fine-tuning step:
  • The reference dataset is subsetted to only include labels with scores close to the maximum.
  • Scores are recomputed using only marker genes for the subset of labels.
  • This is iterated until one label remains.

Automatic annotation provides a convenient way of transferring biological knowledge across datasets. In this manner, the burden of manually interpreting clusters and defining marker genes only has to be done once, for the reference dataset, and this knowledge can be propagated to new datasets in an automated manner.

2 Using the built-in references

SingleR provides several reference datasets (mostly derived from bulk RNA-seq or microarray data) through dedicated data retrieval functions. For example, we obtain reference data from the Human Primary Cell Atlas using the HumanPrimaryCellAtlasData() function, which returns a SummarizedExperiment object containing matrix of log-expression values with sample-level labels.

hpca.se <- HumanPrimaryCellAtlasData()
## class: SummarizedExperiment 
## dim: 19363 713 
## metadata(0):
## assays(1): logcounts
## rownames(19363): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(713): GSM112490 GSM112491 ... GSM92233 GSM92234
## colData names(3): label.main label.fine label.ont

Our test dataset will is taken from La Manno et al. (2016).
For the sake of speed, we will only label the first 100 cells from this dataset.

hESCs <- LaMannoBrainData('human-es')
hESCs <- hESCs[,1:100]

# SingleR() expects log-counts, but the function will also happily take raw
# counts for the test dataset. The reference, however, must have log-values.
hESCs <- logNormCounts(hESCs)

We use our hpca.se reference to annotate each cell in hESCs via the SingleR() function, which uses the algorithm described above. Note that the default marker detection method is to take the genes with the largest positive log-fold changes in the per-label medians for each gene.

pred.hesc <- SingleR(test = hESCs, ref = hpca.se, labels = hpca.se$label.main)
## DataFrame with 100 rows and 5 columns
##                                          scores         first.labels
##                                        <matrix>          <character>
## 1772122_301_C02  0.347652:0.109547:0.123901:... Neuroepithelial_cell
## 1772122_180_E05  0.361187:0.134934:0.148672:... Neuroepithelial_cell
## 1772122_300_H02  0.446411:0.190084:0.222594:... Neuroepithelial_cell
## 1772122_180_B09  0.373512:0.143537:0.164743:... Neuroepithelial_cell
## 1772122_180_G04  0.357341:0.126511:0.141987:... Neuroepithelial_cell
## ...                                         ...                  ...
## 1772122_299_E07 0.371989:0.169379:0.1986877:... Neuroepithelial_cell
## 1772122_180_D02 0.353314:0.115864:0.1374981:... Neuroepithelial_cell
## 1772122_300_D09 0.348789:0.136732:0.1303042:... Neuroepithelial_cell
## 1772122_298_F09 0.332361:0.141439:0.1437860:... Neuroepithelial_cell
## 1772122_302_A11 0.324928:0.101609:0.0949826:... Neuroepithelial_cell
##                       tuning.scores               labels        pruned.labels
##                         <DataFrame>          <character>          <character>
## 1772122_301_C02 0.1824402:0.0991116 Neuroepithelial_cell Neuroepithelial_cell
## 1772122_180_E05 0.1375484:0.0647134              Neurons              Neurons
## 1772122_300_H02 0.2757982:0.1369690 Neuroepithelial_cell Neuroepithelial_cell
## 1772122_180_B09 0.0851623:0.0819878 Neuroepithelial_cell Neuroepithelial_cell
## 1772122_180_G04 0.1988415:0.1016622 Neuroepithelial_cell Neuroepithelial_cell
## ...                             ...                  ...                  ...
## 1772122_299_E07 0.1760025:0.0922504 Neuroepithelial_cell Neuroepithelial_cell
## 1772122_180_D02 0.1967609:0.1124805 Neuroepithelial_cell Neuroepithelial_cell
## 1772122_300_D09 0.0816424:0.0221368 Neuroepithelial_cell Neuroepithelial_cell
## 1772122_298_F09 0.1872499:0.0671893 Neuroepithelial_cell Neuroepithelial_cell
## 1772122_302_A11 0.1560800:0.1051322            Astrocyte            Astrocyte

Each row of the output DataFrame contains prediction results for a single cell. Labels are shown before fine-tuning (first.labels), after fine-tuning (labels) and after pruning (pruned.labels), along with the associated scores. We summarize the distribution of labels across our subset of cells:

##            Astrocyte Neuroepithelial_cell              Neurons 
##                   14                   81                    5

At this point, it is worth noting that SingleR is workflow/package agnostic. The above example uses SummarizedExperiment objects, but the same functions will accept any (log-)normalized expression matrix.

3 Using single-cell references

Here, we will use two human pancreas datasets from the scRNAseq package. The aim is to use one pre-labelled dataset to annotate the other unlabelled dataset. First, we set up the Muraro et al. (2016) dataset to be our reference.

sceM <- MuraroPancreasData()

# One should normally do cell-based quality control at this point, but for
# brevity's sake, we will just remove the unlabelled libraries here.
sceM <- sceM[,!is.na(sceM$label)]

sceM <- logNormCounts(sceM)

We then set up our test dataset from Grun et al. (2016). To speed up this demonstration, we will subset to the first 100 cells.

sceG <- GrunPancreasData()
sceG <- sceG[,colSums(counts(sceG)) > 0] # Remove libraries with no counts.
sceG <- logNormCounts(sceG) 
sceG <- sceG[,1:100]

We then run SingleR() as described previously but with a marker detection mode that considers the variance of expression across cells. Here, we will use the Wilcoxon ranked sum test to identify the top markers for each pairwise comparison between labels. This is slower but more appropriate for single-cell data compared to the default marker detection algorithm (which may fail for low-coverage data where the median is frequently zero).

pred.grun <- SingleR(test=sceG, ref=sceM, labels=sceM$label, de.method="wilcox")
## acinar   beta  delta   duct 
##     53      4      2     41

4 Annotation diagnostics

4.1 Based on the scores within cells

SingleR provides a few basic yet powerful visualization tools. plotScoreHeatmap() displays the scores for all cells across all reference labels, which allows users to inspect the confidence of the predicted labels across the dataset. The actual assigned label for each cell is shown in the color bar at the top; note that this may not be the visually top-scoring label if fine-tuning is applied, as the only the pre-tuned scores are directly comparable across all labels.


For this plot, the key point is to examine the spread of scores within each cell. Ideally, each cell (i.e., column of the heatmap) should have one score that is obviously larger than the rest, indicating that it is unambiguously assigned to a single label. A spread of similar scores for a given cell indicates that the assignment is uncertain, though this may be acceptable if the uncertainty is distributed across similar cell types that cannot be easily resolved.

We can also display other metadata information for each cell by setting clusters= or annotation_col=. This is occasionally useful for examining potential batch effects, differences in cell type composition between conditions, relationship to clusters from an unsupervised analysis, etc. In the code below, we display which donor each cell comes from:


4.2 Based on the deltas across cells

The pruneScores() function will remove potentially poor-quality or ambiguous assignments. In particular, ambiguous assignments are identified based on the per-cell “delta”, i.e., the difference between the score for the assigned label and the median across all labels for each cell. Low deltas indicate that the assignment is uncertain, which is especially relevant if the cell’s true label does not exist in the reference. The exact threshold used for pruning is identified using an outlier-based approach that accounts for differences in the scale of the correlations in various contexts.

to.remove <- pruneScores(pred.grun)
##    Mode   FALSE    TRUE 
## logical      96       4

By default, SingleR() will report pruned labels in the pruned.labels field where low-quality assignments are replaced with NA. However, the default pruning thresholds may not be appropriate for every dataset - see ?pruneScores for a more detailed discussion. We provide the plotScoreDistribution() to help in determining whether the thresholds are appropriate by using information across cells with the same label. This displays the per-label distribution of the deltas across cells, from which pruneScores() defines an appropriate threshold as 3 median absolute deviations (MADs) below the median.

plotScoreDistribution(pred.grun, show = "delta.med", ncol = 3, show.nmads = 3)