Fig 1.
Flowchart of the RBP-ADDA method.
During Data Encoding, each sequence in the sample (in vitro and in vivo) is represented as a concatenation of a one-hot encoding vector representing the nucleotides. Step 1. Pre-training. We use in vitro data to pre-train a source network and task predictor. Step 2.1. Initialize the target network. Target network is initialized by sharing the same parameters and architecture with source network. Step 2.2. ADDA. We apply adversarial learning to train the target network on in vivo data and train the domain discriminator. Step 3. Fine-tuning. We use both the source and target network to fine-tune the task predictor. Solid lines indicate steps in which the network parameters are fixed.
Fig 2.
Comparison of performances between RBP-ADDA and other methods.
(A) Comparison on 25 in vitro RNAcompete datasets; (B) Comparison on 19 eCLIP datasets from HepG2 cell line; (C) Comparison on 19 eCLIP datasets from K562 cell line. P-values are computed using unpaired Wilcoxon rank sum one-tailed test with p.adjust.
Fig 3.
Performance of RBP-ADDA model after data augmentation operations.
In each panel, the predictive performances on an RBP are grouped and shown as norm (non-augment), gap, replacement, and swap. Within each group, the performances after pre-training step, domain adaptation step and fine-tuning step are indicated as “1”, “2” and “3”.
Fig 4.
Visualization of attribution scores, consensus motif, motifs obtained from in vitro (RNAcompete) and in vivo (eCLIP) experiments.