A decorated tree approach to random permutations in substitution-closed classes

We establish a novel bijective encoding that represents permutations as forests of decorated (or enriched) trees. This allows us to prove local convergence of uniform random permutations from substitution-closed classes satisfying a criticality constraint. It also enables us to reprove and strengthen permuton limits for these classes in a new way, that uses a semi-local version of Aldous' skeleton decomposition for size-constrained Galton--Watson trees.Comment: New version including referee's corrections, accepted for publication in Electronic Journal of Probabilit

Integrative analysis identifies candidate tumor microenvironment and intracellular signaling pathways that define tumor heterogeneity in NF1

Neurofibromatosis type 1 (NF1) is a monogenic syndrome that gives rise to numerous symptoms including cognitive impairment, skeletal abnormalities, and growth of benign nerve sheath tumors. Nearly all NF1 patients develop cutaneous neurofibromas (cNFs), which occur on the skin surface, whereas 40-60% of patients develop plexiform neurofibromas (pNFs), which are deeply embedded in the peripheral nerves. Patients with pNFs have a ~10% lifetime chance of these tumors becoming malignant peripheral nerve sheath tumors (MPNSTs). These tumors have a severe prognosis and few treatment options other than surgery. Given the lack of therapeutic options available to patients with these tumors, identification of druggable pathways or other key molecular features could aid ongoing therapeutic discovery studies. In this work, we used statistical and machine learning methods to analyze 77 NF1 tumors with genomic data to characterize key signaling pathways that distinguish these tumors and identify candidates for drug development. We identified subsets of latent gene expression variables that may be important in the identification and etiology of cNFs, pNFs, other neurofibromas, and MPNSTs. Furthermore, we characterized the association between these latent variables and genetic variants, immune deconvolution predictions, and protein activity predictions

Refining interaction search through signed iterative Random Forests

Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically black-boxes, learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest algorithm took a step towards bridging this gap by providing a computationally tractable procedure to identify the stable, high-order feature interactions that drive the predictive accuracy of Random Forests (RF). Here we refine the interactions identified by iRF to explicitly map responses as a function of interacting features. Our method, signed iRF, describes subsets of rules that frequently occur on RF decision paths. We refer to these rule subsets as signed interactions. Signed interactions share not only the same set of interacting features but also exhibit similar thresholding behavior, and thus describe a consistent functional relationship between interacting features and responses. We describe stable and predictive importance metrics to rank signed interactions. For each SPIM, we define null importance metrics that characterize its expected behavior under known structure. We evaluate our proposed approach in biologically inspired simulations and two case studies: predicting enhancer activity and spatial gene expression patterns. In the case of enhancer activity, s-iRF recovers one of the few experimentally validated high-order interactions and suggests novel enhancer elements where this interaction may be active. In the case of spatial gene expression patterns, s-iRF recovers all 11 reported links in the gap gene network. By refining the process of interaction recovery, our approach has the potential to guide mechanistic inquiry into systems whose scale and complexity is beyond human comprehension

Human Skin, Oral, and Gut Microbiomes Predict Chronological Age.

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases

Genome-Wide Associations of Signaling Pathways in Glioblastoma Multiforme

Background: eQTL analysis is a powerful method that allows the identification of causal genomic alterations, providing an explanation of expression changes of single genes. However, genes mediate their biological roles in groups rather than in isolation, prompting us to extend the concept of eQTLs to whole gene pathways. Methods: We combined matched genomic alteration and gene expression data of glioblastoma patients and determined associations between the expression of signaling pathways and genomic copy number alterations with a non-linear machine learning approach. Results: Expectedly, over-expressed pathways were largely associated to tag-loci on chromosomes with signature alterations. Surprisingly, tag-loci that were associated to under-expressed pathways were largely placed on other chromosomes, an observation that held for composite effects between chromosomes as well. Indicating their biological relevance, identified genomic regions were highly enriched with genes having a reported driving role in gliomas. Furthermore, we found pathways that were significantly enriched with such driver genes. Conclusions: Driver genes and their associated pathways may represent a functional core that drive the tumor emergence and govern the signaling apparatus in GBMs. In addition, such associations may be indicative of drug combinations for the treatment of brain tumors that follow similar patterns of common and diverging alterations

Combining Static and Dynamic Features for Multivariate Sequence Classification

Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data. In this work, we demonstrate how generative models such as Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can be used to extract temporal information from the dynamic data. We explore how the extracted information can be combined with the static features in order to improve the classification performance. We evaluate the existing techniques and suggest a hybrid approach, which outperforms other methods on several public datasets.Comment: Presented at IEEE DSAA 201

Carbon fluxes in a mature deciduous forest under elevated CO₂

This PhD thesis addressed several major aspects of the carbon (C) cycle in a c. 100-year-old, mixed deciduous forest under elevated CO₂ with an emphasis on below-ground processes. The aim was to assess the responses of tree fine roots and soil respiration to canopy CO₂ enrichment (? 550 ppm) in this tallest forest studied to date. Furthermore, leaf gas-exchange of the five study species was examined to ascertain the long-term response of photosynthetic carbon uptake to elevated atmospheric CO₂. Investigations at the Swiss Canopy Crane (SCC) experimental site were guided by the following key questions: (1) Does below-ground C allocation to fine root production increase in response to CO₂ enrichment in order to acquire more nutrients to match the enhanced C supply in the forest canopy? (2) Is below-ground metabolism enhanced and therefore forest soil respiration stimulated by canopy CO₂ enrichment? (3) Is leaf-level photosynthesis persistently stimulated by elevated CO₂ in this stand or had these mature broad-leaved trees reduced their carbon up- take by photosynthetic down-regulation under long-term CO₂ enrichment? Findings from earlier studies at the SCC site, including 13C isotope tracing, all point towards an in- creased flux of C through CO₂-enriched trees to the soil but neither fine root biomass nor soil respiration were stimulated by elevated CO₂. Surprisingly, fine root biomass in bulk soil and ingrowth cores showed strong reductions by ? 30% in year five and six but were unaffected in the following seventh year of CO₂ enrichment. Given the absence of a positive biomass response of fine roots, we assumed that the extra C assimilated in the CO₂-enriched forest canopy was largely respired back to the atmosphere via increases in fine root and rhizosphere respiration and the metabolization of increased root derived exudates by soil microbes. Indeed, 52% higher soil air CO₂ concentration during the growing season and 14% greater soil microbial biomass both in- dicated enhanced below-ground metabolism in soil under CO₂-enriched trees. However, this did not translate into a persistent stimulation of soil respiration. At times of high or continuous precipitation soil water savings under CO₂-exposed trees (resulting from reduced sapflow) led to excessive soil moisture (> 45 vol.-%) impeding soil gas-exchange and thus soil respiration. Depending on the interplay between soil temperature and the consistently high soil water content in this stand, instantaneous rates of soil respiration were periodically reduced or increased under elevated CO₂ but on a diel scale and integrated over the growing season soil CO₂ emissions were similar under CO₂-enriched and control trees. Soil respiration could therefore not explain the fate of the extra C. The lacking sink capacity for additional assimilates led us to assume downward adjustment of photosynthetic capacity in CO₂-enriched trees thereby reducing carbon uptake in the forest canopy. Photosynthetic acclimation cannot completely eliminate the CO₂-driven stimulation in carbon uptake, but a reduction could hamper the detection of a CO₂ effect considering the low statistical power inevitably involved with such large-scale experiments. However, after eight years of CO₂ enrichment we found sustained stimulation in leaf photosynthesis (42-49%) indicating a lack of closure in the carbon budget for this stand under elevated atmospheric CO₂