HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides a R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms.
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Is it possible to separate Hi-C signal into weights and observed contacts #15
If we do this, then we can retrieve any single normalization factor on the fly
Cons:
This means we will have yet another variable that must be defined.
We can circumvent this problem by offering a way to modify the json, so that a single normalization factor is retrieved by default, and if a user wants to retrieve another normalization factor, they are free to do so using a variable.
The above procedure seems clunky, and introduces another layer of complexity, so I do not think users will really go for this.
And anyways, how many Hi-C studies are there that do comparisons across normalizations?
Pros:
Cons:
The above procedure seems clunky, and introduces another layer of complexity, so I do not think users will really go for this.
And anyways, how many Hi-C studies are there that do comparisons across normalizations?