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Comparison of NetworkVI and TotalVI in High-Noise Settings

NetworkVI and TotalVI are both advanced methods for integrating multi-omics data, particularly in single-cell contexts. However, their performance can vary significantly under high-noise conditions, which are common in biological datasets.

1. Overview of NetworkVI and TotalVI

• NetworkVI: This method utilizes a variational inference framework that incorporates network information to enhance the integration of multi-omics data. It is designed to be robust against noise by leveraging the relationships between different modalities.
• TotalVI: TotalVI is a model that integrates transcriptomic and protein expression data using a variational autoencoder approach. It aims to provide a comprehensive view of cellular states but can be sensitive to noise in the data.

2. Performance in High-Noise Environments

In high-noise settings, the ability of a method to maintain accuracy and robustness is crucial. Recent studies have shown that:

• NetworkVI demonstrates superior performance in high-noise conditions by effectively denoising the data through its network-based approach, which allows it to utilize the structure of the data to mitigate the effects of noise.
• In contrast, TotalVI's performance tends to degrade significantly as noise levels increase. This is primarily due to its reliance on the quality of the input data, which can lead to inaccurate representations when faced with high levels of noise.

3. Empirical Evidence

For instance, a recent study highlighted that NetworkVI maintained a higher accuracy in identifying spatial domains in multi-omics datasets even as Gaussian noise levels increased. In comparison, TotalVI showed a marked decline in accuracy under similar conditions, indicating its vulnerability to noise.

Furthermore, methods like CANDIES, which integrate denoising techniques, have been shown to outperform TotalVI in high-noise scenarios, suggesting that incorporating robust denoising strategies is essential for effective multi-omics integration.

4. Conclusion

Overall, NetworkVI is better suited for high-noise settings compared to TotalVI, as it effectively leverages network information to enhance data integration and maintain accuracy. This makes it a preferable choice for researchers dealing with noisy multi-omics data.

References

• CANDIES Study