Deep neural network for cell type differentiation in myelodysplastic syndrome diagnosis performs similarly when trained on compensated or uncompensated data

Myelodysplastic syndromes (MDS) are relatively rare blood diseases that vary widely in their severity, symptoms, and tendency to progress to acute myeloid leukemia and therefore require precise diagnosis and classification1,2. Flow cytometry immunophenotyping of bone marrow cells could be helpful in making the diagnosis of MDS3. Due to natural properties of the fluorescent dyes used in flow cytometry, raw digital data from the instrument must be compensated to account for the spillover of signal between fluorochromes. "Ground truth"cell type classification in MDS immunophenotype flow cytometry panel of 14 markers performed on samples from patients with confirmed MDS (n=118), precursor condition (CCUS, n=86), non-clonal idiopathic cytopenia of uncertain significance (ICUS, n=152) and normal controls (n=21) was performed using Infinicyt. A neural network with an input layer accepting light scatter properties (6 channels) and fluorescent channels (8 channels) for each tube along with a tube indicator (15 total channels) followed by three fully connected hidden layers (64, 128 and 64 nodes) and an output layer including aggregates, basophils, blasts, dendritic cells, debris, granulocytes, hematogones, lymphocytes, mast cells, monocytes, plasma cells, RBCs, and unknown was trained twice on a randomly selected 80% of 353,655,369 unique events, once on uncompensated data and again with the per-tube compensated data. The uncompensated network trained to a cost of 0.16514 in 275 epochs. The compensated network reached a cost of 0.17089 after 1067 epochs. Tested on reserved data, the networks perform essentially identically, providing support to the potential clinical validity of using uncompensated data.