Figure 2: Machine learning allows for robust label-free prediction of DNA content and cell cycle phases of Jurkat cells.
From: Label-free cell cycle analysis for high-throughput imaging flow cytometry
(a) We find a Pearson’s correlation of r=0.896±0.007 (error bars indicate the s.d. obtained via 10-fold cross-validation) between actual DNA content and predicted DNA content based on regression using brightfield and darkfield morphological features only (see Methods section). We used the Watson pragmatic curve fitting algorithm to specify the fraction of cells in the G1, S and G2 phases. (b–f) For cells that are actually in a particular phase (for example, b shows cells in G1/S/G2), the bar plots show the classification results based on brightfield and darkfield morphological features only (for example, b shows that the few cells in prophase (Pro), metaphase (Meta), anaphase (Ana), and telophase (Telo) are errors). (g) Bar plot of the true positive rates of the cell cycle classification.
