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ChuanXu1 avatar ChuanXu1 commented on June 4, 2024

@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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aditya-sarkar441 avatar aditya-sarkar441 commented on June 4, 2024

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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ChuanXu1 avatar ChuanXu1 commented on June 4, 2024

@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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aditya-sarkar441 avatar aditya-sarkar441 commented on June 4, 2024

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aditya-sarkar441 avatar aditya-sarkar441 commented on June 4, 2024

@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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ChuanXu1 avatar ChuanXu1 commented on June 4, 2024

@aditya-sarkar441, as I said, you can choose the "Immune_All_Low.pkl" model instead of "Immune_All_High.pkl"

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aditya-sarkar441 avatar aditya-sarkar441 commented on June 4, 2024

Ok thanks, I'll try this out.

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