Choosing the Right Approach
As the field of Hi-C data analysis continues to evolve, researchers often grapple with the choice of normalization method and pipeline. To date, no single “gold standard” method has emerged. Various studies have compared different algorithms, with Rao et al. opting for the KR method due to its computational efficiency. However, KR may falter with sparse contact matrices, in which case ICE, a robust balancing method, can be used. Overall, SCN, KR, and ICE strategies tend to perform similarly, with only minor differences at lower resolutions.
In practical terms, the choice between these approaches often depends on the tools and pipelines you are using, as many provide KR and ICE as common normalization methods. The field is rapidly advancing, and newer algorithms like Binless may influence the consensus in the future.
For a more detailed explanation of the various methods of data normalization for Hi-C data, we’ve created the whitepaper linked below: