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

[jing-wan]

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

[rraadd88]

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.

[nchernia]

Hello, Please look at the supplementary materials of the Cell 2014 paper - there is a long discussion of normalization techniques. VC is vanilla coverage. Best Neva

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On Sat, May 20, 2017 at 10:50 AM, Jing Wan ***@***.***> wrote: 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 — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#19>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ABWiWxtCovNCGjjwEjRVB5HKEBlMR8uwks5r7qlYgaJpZM4NhPIs> .

-- Neva Cherniavsky Durand, Ph.D. Staff Scientist, Aiden Lab www.aidenlab.org