[Feature Request] - Add Reciprocal Logarithmic Ratio (RLR)

[jolespin]

https://www.biorxiv.org/content/10.1101/2021.01.25.428037v1.full.pdf?utm_source=perplexity

Several types of ecological studies such as trophic changes in lakes, marine plankton
communities, forest ecosystems, terrestrial ecosystems, and interactions between plants
and soils have employed time-series analyses for identifying the factors that might
cause state changes. With the rise of next-generation sequencing, our understanding of
these ecosystems is expanding to analyze sequence data. A nonlinear time series
analysis method, termed empirical dynamic modeling, has been recently applied for
analyzing time-series data. Among different empirical dynamic modeling methods,
convergent cross-mapping (CCM) is frequently used to infer a causal relationship.
Although CCM enables the directional interpretation of causal relationships, it cannot
be applied to compositional data analysis. This study proposes a novel type of
transformation, Reciprocal Logarithmic Ratio (RLR) transformation, that enables CCM
to be applied to compositional data. With RLR-transformation, CCM results for
compositional data are comparable to those for absolute data, and it is confirmed that
the transformation is applicable to sequence data as well. The RLR-transformation is
expected to provide a better understanding of ecological interactions by estimating
causal relationships in compositional data.