Supplementary Figure 7: RNN-CNN outperforms feature-based methods with respect to calibration.
From: Prospective identification of hematopoietic lineage choice by deep learning
(a) Area under the curve (AUC, mean±sd, n=3 rounds) for generations before and after an annotated marker onset for our RNN-CNN (red), two CNNs (AlexNet in blue, LeNet-5 in green), a random forest (yellow), a support vector machine (SVM, gray), and a conditional random field (CRF, white). Our method is on par with the CNN-only implementations and the random forest based classification. It outperforms the SVM and the CRF on annotated cells and most latent generations. (b) F1 scores (averaged lineage score threshold: 0.5, mean±sd, n=3 rounds) for generations before and after an annotated marker onset for the methods in a and the algorithmic information theoretic prediction (AITP, brown). Due to better calibration, our RNN-CNN shows highest scores and lowest variance in most generations.
