Comments (7)
@aditya-sarkar441, as I downloaded and checked that dataset - please remove the proteome expression in that data, by command such as adata = adata[:, ~adata.var_names.str.startswith("AB_")]
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ok. why do we remove this proteome data ?
I got these cell types : ILC, Tcells, Monocytes, B cells. Why am I not getting NK and subtypes of T cells (CD4T, CD8T) ?
Also how can i keep only those cells which are predicted as T cells ?
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@aditya-sarkar441, the prediction is purely based on gene expression, so proteome data should be removed. There should be subtypes showing up, did you select the right model (for example, Immune_All_Low)?
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@ChuanXu1 My Postdoc mentor is telling me that celtypist can predict dozens of cell types. I am not sure how to do this. Can you please help me with this ?
This is the command I'm using :
pred_cont = celltypist.annotate(nature_cont, model = 'Immune_All_High.pkl', majority_voting = True)
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@aditya-sarkar441, as I said, you can choose the "Immune_All_Low.pkl" model instead of "Immune_All_High.pkl"
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Ok thanks, I'll try this out.
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Related Issues (20)
- " have completed preprocessing and cell clustering. " HOT 2
- codes for harmonizing the cell labels HOT 2
- the question about conf_score HOT 3
- CellTypist_Immune_Reference_v2 question HOT 2
- Immune_All_Low - model training question HOT 4
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- Preparing custom reference files HOT 2
- Error running celltypist HOT 10
- Allow to specify the AnnData var field that has the gene_symbols instead of only relying on var_names HOT 2
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- Request: allow to specify AnnData layer to use HOT 1
- confidence score definition HOT 2
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- Using model trained by scRNA-seq datasets to predict Spatial transcriptmoic dataset HOT 2
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- Running `celltypist.annotate` with `min_prop` can't create "Heterogeneous" category HOT 2
- Use of metacells? HOT 4
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