Question: Classify cells using seurat
0
gravatar for ApoorvaB
6 months ago by
ApoorvaB140
United States
ApoorvaB140 wrote:

Hi,

I am working on single cell data and I have a general question. Monocle has a function 'classifyCells' to assign celltype using known marker genes. Here is the bit from the documentation

cth <- newCellTypeHierarchy()

MYF5_id <- row.names(subset(fData(cds), gene_short_name == "MYF5"))
ANPEP_id <- row.names(subset(fData(cds), gene_short_name == "ANPEP"))

cth <- addCellType(cth, "Myoblast", classify_func =
    function(x) { x[MYF5_id,] >= 1 })
cth <- addCellType(cth, "Fibroblast", classify_func =
    function(x) { x[MYF5_id,] < 1 & x[ANPEP_id,] > 1 } )

cds <- classifyCells(cds, cth, 0.1)

Is there a similar function is Seurat. Something that allows to classify cells by cell type and adds it to the meta-data in the seurat object?

seurat scrna monocle • 475 views
ADD COMMENTlink modified 3 months ago by dppb0570 • written 6 months ago by ApoorvaB140
1

I have used SetIdent() to do something like that.

ADD REPLYlink written 6 months ago by Madelaine Gogol5.0k
0
gravatar for dppb05
3 months ago by
dppb0570
dppb0570 wrote:

Is there a similar function is Seurat. Something that allows to classify cells by cell type and adds it to the meta-data in the seurat object?

There is ClassifyCells function. But keep in mind that this function works differently from monocle::classifyCells. Monocle's docs, as you have probably read, give a good high-level explanation on their classification approach.

For Seurat::ClassifyCells you need some labelled data that will be used to train Random Forest classifiers. Those trained classifiers will then be used to classify your unlabelled data. You could subset your Seurat object (using SubsetData) based on some marker genes and set the ident (using SetIdent) of this subset according to those markers, then use that as your training set.

ADD COMMENTlink modified 3 months ago • written 3 months ago by dppb0570
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