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What are the general principles of the VC, VC_SQRT and KR normalization methods? #19

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jing-wan opened this issue May 20, 2017 · 2 comments

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@jing-wan
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hello,
I used juicer_tools to dump my Hi-C data recently. In juicer's dump you provided three normalization methods: VC, VC_SQRT, KR, and I want to know what are the principles of them. I searched them on the internet and your paper(Rao et al. 2014), but only find KR.
I have to know about the normalization method of Hi-C in my study, so would you tell me the general principles of the three normalization method in your tools? Or some relative materails and references is good.
Thank you!
Yours,
J.Wan

@nchernia
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nchernia commented May 24, 2017 via email

@rraadd88
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rraadd88 commented Mar 6, 2020

FYI,
VC: vanilla coverage (overcorrects low coverage loci)
VC_SQRT: square root vanilla coverage (creates a matrix whose row and column sums are all approximately equal)
KR : Knight and Ruiz normalization (works at both low and high resolutions.)
Ref: Knight, P., and Ruiz, D. (2012). A fast algorithm for matrix balancing. IMA J. Numer. Anal. Published online October 26, 2012. http://dx.doi.org/10.1093/imanum/drs019.

Took me quite a time to dig out this info. I hope it helps.

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