WARNING - if you have changed the channel names, you will also need to adjust the measurement labels used in each of these scripts. Once you have more than 15 cells, however, it is less likely that the classifier will use a measurement that has bearing on your experiment. Almost any of those 15 measurements could be used to differentiate the two cells assuming the measurements were not the exact same. Imagine the extreme case of 15 measurements and 2 cells, one marked positive and one marked negative. There is no fixed number of required objects for training, but in general I prefer to have more objects than there are measurements being used for the classification. There is no probability threshold below which something will be left as "unknown."ĥ. However, it IS exclusive, so if there is a flaw in the phenotypes you are looking for, you will have forced incorrect classifications in some cases. Following on from #3, this is an excellent way to define phenotypes - T cells, B cells, tumor cells, etc. All PD1, PDL1 double positive cells are only going to be classified as PD1 OR PDL1. The quality of the classifier shown in 2 is going to be really, really, really bad, because it is exclusive. The machine learning classifier is NOT a Composite classifier. Here I should add a few more CD8 cells, most likely, but at least nothing is excessively imbalanced.ģ. Preferably from more than one image, using the Load training button! In the newest version of the object classifier, there is no class balancing (as of 0.3.0 ), so it is more important than ever to have approximately even numbers of training samples for each class. If your immune cell classifier is using Cytoplasmic CK: Mean as one of it's primary measurements - you might have some cross-talk or bias in how you are training the classifier! I strongly recommend having some sort of validation step for any machine learning classifier, but at least knowing that the classifier is looking at the "right" information is a nice sanity check. I like Random Trees because, using the Edit button, you can check Calculate Variable Importance, which will let you look at the actual measurements having the largest impact on your classifier in the View->Show log. There is way too much to unpack here, so I am only going to skim some of the highlights. “CD8:CD3” is not the same as “CD3:CD8” - at least to the program or most analysis software. The order is important, especially when doing something like mentioned in (1), as accidentally changing the order will result in differently named cell classifications. Single measurement or machine learning classifiers can be used as inputs. If you want to edit a composite classifier by adjusting one of the component classifiers, you need to overwrite the composite classifier - the change is not automatically passed along. These use multiple classifiers in the order that they are added in the composite menu.
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