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Table 3 Table of F-measure statistics as given by (7), where we use the manual gating as the ground truth

From: optimalFlow: optimal transport approach to flow cytometry gating and population matching

  flowMeans DeepCyTOF optimalFlowTemplates + DeepCyTOF optimalFlowTemplates + optimalFlowClassification
\({\mathcal {C}}^2\) 0.8988 0.9546 0.9736 0.9610
\({\mathcal {C}}^5\) 0.8977 0.9161 0.9196 0.9587
\({\mathcal {C}}^7\) 0.9508 0.7514 0.9769 0.9768
\({\mathcal {C}}^9\) 0.8936    0.9172
\({\mathcal {C}}^{14}\) 0.9004 0.9838 0.9530 0.9066
\({\mathcal {C}}^{15}\) 0.8974 0.9408 0.9352 0.9556
\({\mathcal {C}}^{17}\) 0.9405 0.7847 0.9810 0.9848
\({\mathcal {C}}^{18}\) 0.9004 0.7837 0.9796 0.9849
\({\mathcal {C}}^{26}\) 0.9024    0.9313
\({\mathcal {C}}^{27}\) 0.8645    0.9306
\({\mathcal {C}}^{29}\) 0.9475    0.9744
\({\mathcal {C}}^{31}\) 0.9290    0.9656
\({\mathcal {C}}^{40}\) 0.9330    0.9538
  1. First column: results of the unsupervised gating procedure flowMeans on \(\mathcal {TS}\). Second column: results of the supervised procedure DeepCyTOF on \(\mathcal {TS}'\). Third column, results of DeepCyTOF on the clusters \(\mathcal {TS}'_1\), \(\mathcal {TS}'_2\) and \(\mathcal {TS}'_3\) produced by optimalFlowTemplates. Forth column: results of our supervised procedure optimalFlowTemplates + optimalFlowClassification on \(\mathcal {TS}\). In underline we have the best performance according to the F-measure