We focused on 3 major barriers to understanding #breastmilk
1⃣Milk contains thousands of components, yet most studies analyze just one or a few
2⃣Milk changes over time, yet longitudinal studies are rare
3⃣We lack computational tools & methods to study milk as a complex SYSTEM
From a #DataScience perspective, to study complex biological processes such as #humanmilk and lactation, it is essential to take an integrative and ‘multi-layer’ approach.
Nonetheless, it is computationally challenging to integrate signals from multiple modalities - especially temporal ones - in a unified model. We therefore propose leveraging methods developed to study another complex system - the #microbiome.
We showcase the wide array of parallels between #humanmilk and the microbiome and demonstrate how incorporating knowledge gleaned from community ecology and computational microbiology can serve as an anchor to advance the study of #humanmilk as a system.
We further suggest that #humanmilk is a complex adaptive system, in which low-level local interactions and selection mechanisms combine to create high-level patterns.
In the unique case of #humanmilk, it originates in the mother but its properties emerge in the infant, emphasizing the importance of the mother-milk-infant “triad” and its environment as the unit of study.
To fully unlock the potential of this approach, we advocate to Record, Represent and Decipher the mother-milk-infant “triad” during the course of lactation.
Longitudinal, multi-layer #humanmilk studies, along with tailored computational methods, may lead to identification and characterization of milk interactions and dynamics that are associated with optimal health outcomes.
We did it, @DaveZeevi! Our paper is now published in @ScienceMagazine! science.sciencemag.org/content/370/65…
We present a data-driven, computational perspective on how selective pressures resulting from nutrient limitation shape microbial coding sequences. Thread below:
We study ‘resource-driven’ selection using metagenomic and single-cell data of marine microbes, while adopting concepts common in statistical genetics like linear mixed models with variance components.
Using tailored algorithms, we partition the variance in selection metrics, calculated using marine microbes, and show that a significant portion of the selection is explained by the environment and is associated with nitrogen availability.
Our paper on Compositional Tensor Factorization (CTF) of microbial dynamics is now published in @NatureBiotech! nature.com/articles/s4158…
It might change how you analyze longitudinal microbiome data. Thread below:
In a cross-sectional study, you run a PCoA and look at the top PCs. But with temporal data, applying PCoA separately on each time point may mask important information that is carried over time.
For your longitudinal data analysis you should use CTF!