Figure 2
From: AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses

AutoGater labeling and training framework. (A) Gradients of two modes of killing (heat and ethanol) were used to generate data for training. Four conditions were used for training and all remaining were used for test. (B) AutoGater’s two stage framework first predicts the set of held out conditions based on the weak labels using a random forest classifier (RFC), and then adjusts those predictions based on the CFU data at that condition using a neural network. (C) The three different methods provide three very different assessments of percentages of live cells for the same sample. Methods are needed to harmonize across these different assessments. (D) Architecture of a neural network that takes non-color channels from a flow cytometer as input (FSC, SSC) and is trained to jointly optimize cell-based and population based notions of death.