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Hannes A. Gamper's avatar

I guess that effect size analyses must be applied to transcriptomics and metabolomics data at different integrative levels of samples of different completeness of the organism of interest and of its habitat, which must also be characterised by basic and integrative parameters after its factorial manipulation. - Challenging, indeed. - The larger, more complete the sample, the less mechanistic the insight will be, since drivers and responses will integrate over many things.

Jiqiong zhou's avatar

Really interesting question. I like the shift from counting environmental variables to asking how many of them an organism can actually perceive and respond to. Maybe the key is not the total number of variables, but the number of distinct responses they generate,if different cues trigger the same response, they may belong to the same effective dimension.

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