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DNA Methylation Deep Review Section # 2 of 3 - Inference, Imputation, and Prediction #954
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Pushing background
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Make DNAm Background More Readable for Editing
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Merge pull request #1 from greenelab/master
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Deep Review PR # 2
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Tab-delimited hopefully.
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Response to recommendations, methylation section
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What is the current state of the art performance for imputation, and is it sufficient for downstream analyses (in your view) or is getting to "useful for many downstream analyses" still a work in progress?
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I normally just use MICE, K-NN and even Mean imputation, and personally have not tried deep learning imputation approaches, though I am open to developing and implementing new methodologies. I think many of these methods are more geared towards BS-Seq, which can make it harder to adopt for users of 450K and EPIC arrays. Though its conceivable that some of these methods could speed up the analysis, incorporating other modalities may make them more accurate, but coming across this data could still be a challenge. I think making them useful, easy-to-use, and tractable may still be a challenge, but standardized and modular workflows that incorporate these methods may make them more easily adoptable and mainstream.
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Can you add maybe one or two sentences at the very end of this paragraph around how these methods compare to what's used in practice and whether or not they are at the stage yet where they can replace current methods? From my read of what you wrote, the answer is no because there are still some bespoke processes to get them working on new data (which is not true of other methods). However, you can see a path to get there. Is that right?