Annotating single cell data automatically

Question

[Gerard]

Hello !

When using seurat to get an object with different clusters of a specific cell type, let's say t cells, I'm looking for ways to automatically annotate them, so it can tell me which one are cd4, which are cd8, which are memory etc.

I've been told to use azimuth, however from seurat I didn't manage to get the appropriate input data. Same for the SingleR package, which requires a numeric matrix of single-cell expression values where rows are genes and columns are cells.

In either case, I struggle to get the appropriate data from my seurat object to run these.

Can anybody help ?

Thank you

Answer 1

[Francesco]

• In order to run Azimuth:

AzimuthClusters <- RunAzimuth(seurat_obj, reference = "pbmcref")

• singleR

surveyReferences() # it lists all available references

ref <- fetchReference("monaco_immune", "2024-02-26")
seurat_dex <- as.SingleCellExperiment(seurat_obj)
predMainMonaco <- SingleR(test = seurat_dex, ref = ref, labels = ref$label.main, BPPARAM = BiocParallel::SerialParam())

Hope it helps

Answer 2

[Nat.Nataren]

Perhaps you have tried this, but I used the Seurat function as.SingleCellExperiment() and this creates an object compatible with SingleR. DietSeurat() can be used to trim down the object to include only the features you want, just look up its documentation for more info.

Download your reference set from celldex for annotation

ref <- HumanPrimaryCellAtlasData(ensembl = TRUE)

Then create your SingleCellExperiment object

sce <- as.SingleCellExperiment(DietSeurat(seurat_obj))

Then you can perform your annotation

human.main <- SingleR(test = sce,assay.type.test = 1,ref = ref,labels = ref$label.main)

This resource might be helpful, it goes into using SingleR in more depth.