Paper: HiCBridge: Resolution Enhancement of Hi-C Data Using Direct Diffusion Bridge (ICLR-2024)

Reviewer comment (Novelty):
The authors seem to have only used existing DDPM model to generate new low-resolution Hi-C data corresponding to the high-resolution ones. The authors have primarily relied on existing methods to generate the data, which, in itself, does not constitute a significant contribution. It is crucial for the authors to clearly explain the problem they aim to address and the challenges associated with it.

Author response:
Please see **General Comments, 1. Difference from original methods**. We explored the best approach to leverage both the regression and generation capabilities of DDB and diffusion model. Various metrics show that our framework outperforms other models at Hi-C enhancement task. Additionally, our model has advantages not only in performance but also in the efficiency of Hi-C enhancement.

Reviewer comment (Experiments):
The authors claim that the conventional regression models, which are based on the Mean Squared Error (MSE) loss, cause the model to regress to the mean in the target domain. This regression leads to blurring and loss of details. However, the authors do not provide visual results to demonstrate this issue. The authors should elaborate on the challenges posed by the problem. Describing the difficulties and complexities associated with addressing the problem will highlight its importance and demonstrate the need for the proposed solutions.

Author response:
Please see **General Comments, 4. Biological Significance**. We highlight the limitations of models trained with traditional MSE and GAN approaches. Our model showcases conservative reconstruction, preserving structural information without introducing artifacts that could potentially lead to misinterpretation of the reconstructed Hi-C data.

An MSE-based model tends to yield blurry output as it represents the average of all plausible reconstructions. In contrast, our approach breaks down the mapping into multiple timesteps, preventing regression to the mean. While GAN-based models excel at generating photo-realistic images by training generator and discriminator, a high level of similarity in the image domain does not necessarily ensure excellent biological reconstruction. Previous methods have incorporated various additional losses to involve biological meaning, but these approaches reduce the interpretability of the model and lead inadvertent results. Our method avoids using additional losses like perception loss or insulation loss.

Reviewer comment (Novelty):
Although the application of diffusion-based methods to Hi-C is meaningful, the method seems lack of novelty. The authors should explain carefully the difference between the core method and previous Direct Diffusion Bridge.

Author response:
Please see **General Comments, 1. Difference from original methods.** We propose the best framework for the Hi-C enhancement task through a combination of DDB and diffusion. Considering the regression performance of the DDB and the generative performance of the diffusion model at the same time, our model showed superior results on various metrics.

Reviewer comment (Evaluation):
I suggest the authors add comparison about memory and time consumption to help readers get a full picture of their method.

Author response:
Please see **General Comments, 2. Computation efficiency.** Our model takes 0.16 seconds to process a single 256 $\times$ 256 Hi-C contact map and has a competitive memory requirement.

Reviewer comment (Evaluation):
During the process of enhancing resolution, new content will sprout. I am concerned whether the method will generate inrelevant or even harmful content, which may incur severe results.

Author response:
Please see **General Comments, 4. Biological significance.** We verified HiCBridge recovers structural information without misleading contents.

Reviewer comment (Experiments):
The robustness of the model against various datasets and its performance under different conditions (such as varying levels of input resolution) would need thorough examination.

Author response:
We conducted a series of experiments to elucidate the generalization abilities of our models. We evaluated standard visual metrics, HiCRep and TAD Insulation score on different resolution (GM12878 from GSM1551551), different cell type (HMEC from GSE63525 and GSM1551610) and another species (CH12-LX from GSE63525 and GSM1551640). In the **Table 12 in Appendix E** of the revised paper, our models also reconstructed visual information and TAD boundaries on all Hi-C datasets. HMEC, with a resolution of about 1/20, poses a noisier dataset than our training data, and CH12-LX, being a mouse lymphoma cell, introduces a cross-species challenge. We supposed that these data shifts might contribute to the variation in SSIM or HiCRep performance in our models. 


|Method|PCC|SPC|PSNR|SSIM|MSE|SNR|HiCRep|Insulation score|
|---|---|---|---|---|---|---|:---:|:---:|
|GM12878$_{GSM1551551}$||||||||||
|Low resolution|0.807|0.680|14.40|0.292|0.03750|339.3|0.981|4.731|
|HiCBridge$^+$|0.962|0.897|22.33|0.480|0.00606|846.6|0.980|2.578|
|HMEC||||||||||
|Low resolution|0.476|0.397|9.62|0.123|0.10968|172.3|0.850|16.253|
|HiCBridge$^+$|0.791|0.690|13.16|0.114|0.04894|259.5|0.801|11.344|
|CH12_LX||||||||||
|Low resolution|0.690|0.585|13.09|0.324|0.05035|207.8|0.953|10.417|
|HiCBridge$^+$|0.858|0.709|17.12|0.281|0.02070|336.7|0.852|6.933|

Reviewer comment (Experiments):
The model's generalizability to different types of Hi-C data, such as those from various species or cells in different states, should be assessed. If the model has only been tested on a narrow range of data, its practical applicability could be limited.

Author response:
As explained in the response to In **Weakness 1**, we verified our model’s generalizability at different cell type (HMEC; Human Mammary Epithelial Cells) and different species (CH12-LX; Mouse Lymphoma Cells).

Reviewer comment (Evaluation):
The paper should address the interpretability of the HiCBridge model. Understanding how the model makes its predictions is crucial, particularly in genomic studies where the biological implications of the findings are as important as the findings themselves.

Author response:
To understand how the model handles a given input, we analyzed the attention map of the self-attention module in the first layer of HiCBridge$^+$. As depicted in **Fig.7 in Appendix D** of the revised paper, we observed that each head emphasized a distinct region: head 0 allocates more attention to diagonal bins, head 1 highlights regions slightly off the diagonals, head 2 is dedicated to a 0.5Mb range of bins, and head 3 focuses on bins in more distant regions. Accordingly, our network can understand semantic structure of the contact map, a desired property for Hi-C data processing.

Reviewer comment (Novelty):
The contribution of this paper is limited. The proposed HiCBridge directly borrows idea from the Direct Diffusion Bridge without any improvement. While the proposed diffusion-based data augmentation (Algorithm 1 and 2) is also trivial. It seems like the paper tends to apply the existing method to the new task, but lack of novelty.

Author response:
Please see **General Comments, 1. Difference from original methods** along with the revised paper. We highlight that we found the optimal combination of DDB and diffusion for Hi-C enhancement task, which cannot be regarded as trivial given the importance of the Hi-C data processing.

Reviewer comment (Writing):
It is not clear how the Algorithm 1 can be used to generate the low-resolution data as your target distribution is based on x0 (high-resolution Hi-C data). Please check whether the x0 in Algorithm 1 should be x1.

Author response:
Thanks for pointing out the typo. We have fixed our Algorithm 1 accordingly.

Reviewer comment (Evaluation):
It seems like the diffusion augmentation is unusable in higher resolution cases for inferior performance, according to the Table 4.

Author response:
If our interpretation of the reviewer's comment is accurate, the aim is to clarify the subpar performance of HiCBridge$^+$ at extremely low resolutions (1/50 and 1/100). As highlighted in the **General Comments, 3. Robustness to data biases**, our analysis suggests that biased data augmentation holds the potential to compromise the model's generalization. While the incorporation of generated low-resolution data contributes to the overall generalization of HiCBridge, an excessive bias with such data may lead to a deterioration in HiCBridge$^+$'s performance across diverse Hi-C datasets. It is crucial to note that the data compared in Table 4 of our paper consists of downsampled data rather than real low-resolution data, presenting a distribution distinct from the one the model was trained on [1]. This finding offers a plausible explanation for the observed reduction in the generalizability of HiCBridge$^+$ compared to HiCBridge, especially at extremely low-resolution settings.

[1] A comprehensive evaluation of generalizability of deep-learning based Hi-C resolution improvement methods, Murtaza et al., Biorxiv, 2022