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16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA ( https://github.com/theislab/scCODA ), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses. Imbalance and loss of cell types is a hallmark in many diseases. Still, quantifying compositional changes in scRNAseq data remains challenging. Here the authors present scCODA, a Bayesian model to assess cell type compositions in scRNA-seq data.

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson’s disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson’s patients, for example, with a classification accuracy of about 70–80%.

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

There is increasing interest in the clinical effects of cholinergic basal forebrain and tegmental pedunculopontine complex (PPN) projection degeneration in Parkinson’s disease (PD). Recent evidence supports an expanded role beyond cognitive impairment, including effects on olfaction, mood, REM sleep behavior disorder, and motor functions. Cholinergic denervation is variable in PD without dementia and may contribute to clinical symptom heterogeneity. Early in vivo imaging evidence that impaired cholinergic integrity of the PPN associates with frequent falling in PD is now confirmed by human post-mortem evidence. Brainstem cholinergic lesioning studies in primates confirm the role of the PPN in mobility impairment. Degeneration of basal forebrain cholinergic projections correlates with decreased walking speed. Cumulatively, these findings provide evidence for a new paradigm to explain dopamine-resistant features of mobility impairments in PD. Recognition of the increased clinical role of cholinergic system degeneration may motivate new research to expand indications for cholinergic therapy in PD.

Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.

We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as physics informed neural networks (PINNs). PINNs are popular machine-learning templates that allow for seamless integration of physical equation models with data. Their training amounts to solving an optimization problem over a weighted sum of data-fidelity and equation-fidelity objectives. Conflicts between objectives can arise from scale imbalances, heteroscedasticity in the data, stiffness of the physical equation, or from catastrophic interference during sequential training. We explain the training pathology arising from this and propose a simple yet effective inverse Dirichlet weighting strategy to alleviate the issue. We compare with Sobolev training of neural networks, providing the baseline of analytically ε -optimal training. We demonstrate the effectiveness of inverse Dirichlet weighting in various applications, including a multi-scale model of active turbulence, where we show orders of magnitude improvement in accuracy and convergence over conventional PINN training. For inverse modeling using sequential training, we find that inverse Dirichlet weighting protects a PINN against catastrophic forgetting.

Key Points The clinical, neuroradiological and surgical definition of hypothalamic involvement is a fundamental factor related to poor postoperative outcome, progressive obesity and neuropsychological impairment in children after surgical removal of craniopharyngioma The previously assumed 'gold-standard' objective of a primary radical removal of the lesion in all cases needs to be replaced with the new paradigm of a limited resection plus focused radiotherapy in patients with craniopharyngioma and hypothalamic lesions Hypothalamic involvement and treatment-related hypothalamic lesions are associated with the highest risk of postoperative sequelae and impaired quality of survival 3D intensity-modulated proton beam radiotherapy has potential advantages over photon beam methods to focus and limit the radiation effects to optic and hypothalamic structures Preclinical, in vivo mouse models can be used to investigate the pathogenesis of adamantinomatous craniopharyngioma and to test novel treatments Childhood-onset craniopharyngioma is a rare embryonic tumour of low-grade malignancy that has traditionally been treated by radical resection. Here, Müller and colleagues review recent advances in the molecular pathogenesis of the disease and treatment strategies that could lead to novel targeted therapies and more-limited surgeries. Childhood-onset craniopharyngiomas are rare embryonic tumours of low-grade histological malignancy. Novel insights into the molecular pathogenesis of human adamantinomatous craniopharyngioma have started to unveil the possibility of testing novel treatments targeting pathogenic pathways. Hypothalamic involvement and/or treatment-related lesions result in impaired physical and social functionality and in severe neuroendocrine sequelae. Quality of survival in patients with craniopharyngioma with hypothalamic involvement is impaired by severe obesity, physical fatigue and non-optimal psychosocial development. Patients with craniopharyngioma involving hypothalamic structures have reduced 20-year overall survival, but overall and progression-free survival are not related to the degree of surgical resection. Irradiation is effective in the prevention of tumour progression and recurrence. For favourably localized craniopharyngiomas, the preferred treatment of choice is to attempt complete resection with preservation of visual, hypothalamic and pituitary function. For unfavourably localized tumours in close proximity to optic and/or hypothalamic structures, a radical neurosurgical strategy attempting complete resection is not recommended owing to potential severe sequelae. As expertise has been shown to have an impact on post-treatment morbidity, medical societies should establish criteria for adequate professional expertise for the treatment of craniopharyngioma. On the basis of these criteria, health authorities should organize the certification of centres of excellence that are authorized to treat and care for patients with this chronic disease.

Many scientific datasets are compositional in nature. Important biological examples include species abundances in ecology, cell-type compositions derived from single-cell sequencing data, and amplicon abundance data in microbiome research. Here, we provide a causal view on compositional data in an instrumental variable setting where the composition acts as the cause. First, we crisply articulate potential pitfalls for practitioners regarding the interpretation of compositional causes from the viewpoint of interventions and warn against attributing causal meaning to common summary statistics such as diversity indices in microbiome data analysis. We then advocate for and develop multivariate methods using statistical data transformations and regression techniques that take the special structure of the compositional sample space into account while still yielding scientifically interpretable results. In a comparative analysis on synthetic and real microbiome data we show the advantages and limitations of our proposal. We posit that our analysis provides a useful framework and guidance for valid and informative cause-effect estimation in the context of compositional data.