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  • Journal article
    Fulcher B, Lubba C, Sethi S, Jones Net al., 2020,

    A self-organizing, living library of time-series data

    , Scientific Data, Vol: 7, ISSN: 2052-4463

    Time-series data are measured across the sciences, from astronomy to biomedicine, but meaningful cross-disciplinary interactions are limited by the challenge of identifying fruitful connections. Here we introduce the web platform, CompEngine, a self-organizing, living library of time-series data, that lowers the barrier to forming meaningful interdisciplinary connections between time series. Using a canonical feature-based representation, CompEngine places all time series in a common feature space, regardless of their origin, allowing users to upload their data and immediately explore diverse data with similar properties, and be alerted when similar data is uploaded in future. In contrast to conventional databases which are organized by assigned metadata, CompEngine incentivizes data sharing by automatically connecting experimental and theoretical scientists across disciplines based on the empirical structure of the data they measure. CompEngine’s growing library of interdisciplinary time-series data also enables the comprehensive characterization of time-series analysis algorithms across diverse types of empirical data.

  • Journal article
    Arnaudon A, Peach R, Barahona M, 2020,

    Scale-dependent measure of network centrality from diffusion dynamics

    , Physical Review Research, Vol: 2, ISSN: 2643-1564

    Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A node is defined to be central if it breaks the metricity of the diffusion as a consequence of the effective boundaries and inhomogeneities in the graph. Our measure is naturally multiscale, as it is computed relative to graph neighbourhoods within the varying time horizon of the diffusion. We find that the centrality of nodes can differ widely at different scales. In particular, our measure correlates with degree (i.e., hubs) at small scales and with closeness (i.e., bridges) at large scales, and also reveals the existence of multi-centric structures in complex networks. By examining centrality across scales, our measure thus provides an evaluation of node importance relative to local and global processes on the network.

  • Conference paper
    Insalata F, Hoitzing H, Aryaman J, Jones Net al., 2020,

    Survival of the Densest Explains the Expansion of Mitochondrial Deletions in Skeletal Muscle Fibres

    , 48th European Mathematical Genetics Meeting (EMGM), Publisher: KARGER, Pages: 211-211, ISSN: 0001-5652
  • Journal article
    Schreglmann S, Wang D, Peach R, Li J, Zhang X, Latorre A, Rhodes E, Panella E, Boyden E, Barahona M, Santaniello S, Bhatia K, Rothwell J, Grossman Net al., 2020,

    Non-invasive amelioration of essential tremor via phase-locked disruption of its temporal coherence

    , Nature Communications, Vol: 12, ISSN: 2041-1723

    Abstract Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be noninvasively suppressed via electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained after the end of the stimulation and can be phenomenologically predicted. Finally, using feature-based statistical-learning and neurophysiological-modelling we show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the oscillation via perturbation of the tremor generating a cascade of synchronous activity in the olivocerebellar loop. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may represent a powerful neuromodulatory strategy to treat brain disorders.

  • Conference paper
    Beaney T, Clarke J, Barahona M, Majeed Aet al., 2020,

    A primary care network analysis: natural communities of general practices in London

    , Publisher: Royal College of General Practitioners, ISSN: 0960-1643

    BACKGROUND: Primary care networks (PCNs) are a new organisational hierarchy introduced in the NHS Long Term Plan with wide-ranging responsibilities. The vision is that they represent 'natural' communities of general practices with boundaries that make sense to practices, other healthcare providers, and local communities. AIM: Our study aims to identify natural communities of general practices based on patient registration patterns, using network analysis methods and unsupervised clustering to create catchments for these communities. METHOD: Patients resident in and attending GP practices in London were identified from Hospital Episode Statistics from 2017 to 2018. We used a series of novel methods for unsupervised graph clustering. A cosine similarity matrix was constructed representing similarities between each general practice to each other, based on registration of patients in each Lower Super Output Area (LSOA). Unsupervised graph partitioning using Markov Multiscale Community Detection was conducted to identify communities of general practices. Catchments were assigned to each PCN based on the majority attendance from an LSOA. RESULTS: In total 3 428 322 unique patients attended 1334 GPs in general practices LSOAs in London. The model grouped 1291 general practices (96.8%) and 4721 LSOAs (97.6%), into 165 mutually exclusive PCNs. The median PCN list size was 53 490 and a median of 70.1% of patients attended a general practice within their allocated PCN, ranging from 44.6% to 91.4%. CONCLUSION: With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital we recognise how PCNs represent their communities. This method may be used by policymakers to understand the populations and geography shared between networks.

  • Journal article
    Heaton LLM, Jones NS, Fricker MD, 2020,

    A mechanistic explanation of the transition to simple multicellularity in fungi.

    , Nature Communications, Vol: 11, ISSN: 2041-1723

    Development of multicellularity was one of the major transitions in evolution and occurred independently multiple times in algae, plants, animals, and fungi. However recent comparative genome analyses suggest that fungi followed a different route to other eukaryotic lineages. To understand the driving forces behind the transition from unicellular fungi to hyphal forms of growth, we develop a comparative model of osmotrophic resource acquisition. This predicts that whenever the local resource is immobile, hard-to-digest, and nutrient poor, hyphal osmotrophs outcompete motile or autolytic unicellular osmotrophs. This hyphal advantage arises because transporting nutrients via a contiguous cytoplasm enables continued exploitation of remaining resources after local depletion of essential nutrients, and more efficient use of costly exoenzymes. The model provides a mechanistic explanation for the origins of multicellular hyphal organisms, and explains why fungi, rather than unicellular bacteria, evolved to dominate decay of recalcitrant, nutrient poor substrates such as leaf litter or wood.

  • Journal article
    Gosztolai A, Barahona M, 2020,

    Cellular memory enhances bacterial chemotactic navigation in rugged environments

    , Communications Physics, Vol: 3, ISSN: 2399-3650

    The response of microbes to external signals is mediated by biochemical networks with intrinsic time scales. These time scales give rise to a memory that impacts cellular behaviour. Here we study theoretically the role of cellular memory in Escherichia coli chemotaxis. Using an agent-based model, we show that cells with memory navigating rugged chemoattractant landscapes can enhance their drift speed by extracting information from environmental correlations. Maximal advantage is achieved when the memory is comparable to the time scale of fluctuations as perceived during swimming. We derive an analytical approximation for the drift velocity in rugged landscapes that explains the enhanced velocity, and recovers standard Keller–Segel gradient-sensing results in the limits when memory and fluctuation time scales are well separated. Our numerics also show that cellular memory can induce bet-hedging at the population level resulting in long-lived, multi-modal distributions in heterogeneous landscapes.

  • Journal article
    Peach RL, Arnaudon A, Barahona M, 2020,

    Semi-supervised classification on graphs using explicit diffusion dynamics

    , Foundations of Data Science, Vol: 2, Pages: 19-33, ISSN: 2639-8001

    Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that consider the graph as the originator of an explicit graph diffusion. We show that appending graph diffusion to feature-based learning as a posteriori refinement achieves state-of-the-art classification accuracy. This method, which we call Graph Diffusion Reclassification (GDR), uses overshooting events of a diffusive graph dynamics to reclassify individual nodes. The method uses intrinsic measures of node influence, which are distinct for each node, and allows the evaluation of the relationship and importance of features and graph for classification. We also present diff-GCN, a simple extension of Graph Convolutional Neural Network (GCN) architectures that leverages explicit diffusion dynamics, and allows the natural use of directed graphs. To showcase our methods, we use benchmark datasets of documents with associated citation data.

  • Journal article
    Tang W, Bertaux F, Thomas P, Stefanelli C, Saint M, Marguerat S, Shahrezaei Vet al., 2020,

    bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

    , Bioinformatics, Vol: 36, Pages: 1174-1181, ISSN: 1367-4803

    Motivation:Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to theirinterpretation. The marked technical variability, high amounts of missing observations and batch effecttypical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient andunified approach for normalisation, imputation and batch effect correction.Results:Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priorsare estimated from expression values across cells using an empirical Bayes approach. We first validateour assumptions by showing this model can reproduce different statistics observed in real scRNA-seqdata. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data thatbayNorm allows robust imputation of missing values generating realistic transcript distributions that matchsingle molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNormimproves accuracy and sensitivity of differential expression analysis and reduces batch effect comparedto other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scalingnormalisation, imputation and true count recovery of gene expression measurements from scRNA-seqdata.Availability:The R package “bayNorm” is available at https://github.com/WT215/bayNorm. The code foranalysing data in this paper is available at https://github.com/WT215/bayNorm_papercode.Contact:samuel.marguerat@imperial.ac.uk or v.shahrezaei@imperial.ac.ukSupplementary information:Supplementary data are available atBioinformaticsonline.

  • Journal article
    Greenbury S, Barahona M, Johnston I, 2020,

    HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways

    , Cell Systems, Vol: 10, Pages: 39-51, ISSN: 2405-4712

    The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalisable statistical platform to infer the dynamic pathways by which many, potentially interacting, discrete traits are acquired or lost over time in biomedical systems. The platform uses HyperTraPS (hypercubic transition path sampling) to learn progression pathways from cross-sectional, longitudinal, or phylogenetically-linked data with unprecedented efficiency, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. Its Bayesian structure quantifies uncertainty in pathway structure and allows interpretable predictions of behaviours, such as which symptom a patient will acquire next. We exploit the model’s topology to provide visualisation tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.

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