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Differential analysis with replicates #14

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ghost opened this issue Jun 8, 2017 · 4 comments
Open

Differential analysis with replicates #14

ghost opened this issue Jun 8, 2017 · 4 comments
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@ghost
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ghost commented Jun 8, 2017

Hi Johannes,
I was wondering whether or not you have already thought about a suitable approach to use normR/diffR for differential analysis with several replicates per condition (aside from the obvious "all vs all" and a merge on the level of results) - the examples in the package vignette seem to suggest that this is not possible at the moment. Is there any "hidden" functionality in the package that enables one to do that in a straightforward manner?
Best,
Peter

@your-highness
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Hi @mpi-pebert ,

I am sorry. Until now, there is no "hidden" functionality that enables this feature. However, it was already requested by some people.

Aside from the merge of individual replicate results (which seems to be quite convenient in most cases), we are currently investigating if a differential analysis on the level of normR-estimated enrichment (getEnrichment(fit)) is feasible. I will report back on this issue.

Johannes

@steffenheyne
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Are there any news on using replicates in NormR? We would be also interested in this!
Thanks,
steffen

@your-highness your-highness self-assigned this Feb 26, 2018
@your-highness
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Hi @steffenheyne ,

That's good to hear that people are interested in using replicates in normR. Apparently we have ideas for this function but there is no schedule on when that feature will be available. I keep this issue posted.

Best,
Johannes

@your-highness your-highness added this to the Version 1.7 milestone Jun 19, 2018
@your-highness your-highness modified the milestones: Version 1.7, 1.20 Sep 24, 2021
@your-highness
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For further development on this task see this publication which uses normR for binarization of histone modification signals to segment the chromatin for the identification of active TUs using transcription-associated histone modifications:

Sahu, A., Li, N., Dunkel, I. et al. EPIGENE: genome-wide transcription unit annotation using a multivariate probabilistic model of histone modifications. Epigenetics & Chromatin 13, 20 (2020). https://doi.org/10.1186/s13072-020-00341-z

EPIGENE source code under https://github.com/imbbLab/EPIGENE . The developers copied (!) the source code of normR to create a new repository instead of forking. Why?

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