Quantifying HiPSC-CM structural organization at scale with deep learning-enhanced SarcGraph
Fig 7
Application of explainable clustering on cell features for unsupervised organization scoring.
(A-i) Decision tree derived from the Train dataset classifying cells into Low, Medium, and High organization clusters. (A-ii) Distribution of cells in 2D PCA-reduced feature space. (B) Histograms showing the distribution of average expert scores ((Expert 1 + Expert 2)/2) within each cluster for Train, Test FISH, and Test Live datasets. (C) Cell count distribution across organization levels (Low, Medium, High) for Train, Test FISH, and Test Live datasets. To compare with clustering results, expert scores are transformed into three categories (scores 1,2: Low, score 3: Medium, scores 4,5: High), and SVR predicted scores are similarly categorized (score < 2.33: Low, 2.33 ≤ score < 3.67: Medium, score ≥ 3.67: High). (D) Representative cell images from each cluster selected from the Train dataset, shown with their corresponding expert and SVR-predicted scores. Scale bars: 20 μm.