Comments (3)
That annot_dict
was manually created for rules-based phenotyping from marker levels. You can use it if you want but it isn't general-purpose and may need to be modified based on your specific application, for example it may not be exactly applicable under different marker panels, tissue types, cells populations, disease states, etc.
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Hi Jacob.
Thanks for clarification. it is much appreciated.
Different application use different approach for cell annotation like spatial location and marker expression.
I know about markers for each cell types, but I don't know about expression profiles.
I am wondering how did you specified these marker threshold for example
'CD4':(0.50, 1.0) is for 'CD4+ T lymphocytes' cell
'CD4':(0.0, 0.50) is for 'CD3+ T lymphocytes' cell
is there any database for expression profiles. or you found them based different publications.
Best regards,
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In this case, the values are normalized between 0 and 1. So for a cell to be "CD4+", it must have CD4 expression above some threshold - here the threshold is set at 0.5. In other words, the rule says that if CD4 marker is above 0.5 then the cell is CD4+.
Determining the best set of rules for your specific case isn't really something we can help with, because the rules to use would probably vary for each experiment. You would need to combine experimental design (i.e. which markers did you choose for the experiment, what tissue types are you looking at, how were the images captured, etc.) with domain knowledge (which cell populations do you want to identify, etc.). A literature search would probably be a good place to start. Hope this helps!
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