Open LazDaria opened 1 year ago
Hi there!
Thanks for the interest.
we ran the analysis in two steps: first we did model selection with GMMs of various mixtures (2-6) and selected the best based on a metric (please check methods). On a second step, we binarized the data (0/1 classification) with the best model by classifying the top two mixtures as positive (or top if a model of 2 mixture was selected).
Importantly, this was run for all cells of all images at once (~350,000) which helped make sure every marker had positive cells.
Overall this is likely an inferior approach to using marker intensity as continuous values, so use with care.
Best wishes, André
Hi,
thank your for your work! In your paper, you describe the identification of positive cells based on GMMs. Afterwards, do you binarize your data (e.g. setting signal to 1 and background to 0) or do you use some kind of normalization of positive and negative cells? How does GMM work if there are no positive cells for a given marker and sample, does it only find one mixture in that case?
Thanks in advance!