1,653 results on '"Holmes, Susan"'
Search Results
2. Publisher Correction: Abrupt perturbation and delayed recovery of the vaginal ecosystem following childbirth
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Costello, Elizabeth K., DiGiulio, Daniel B., Robaczewska, Anna, Symul, Laura, Wong, Ronald J., Shaw, Gary M., Stevenson, David K., Holmes, Susan P., Kwon, Douglas S., and Relman, David A.
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- 2024
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3. Pro-inflammatory feedback loops define immune responses to pathogenic Lentivirus infection
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Wilk, Aaron J., Marceau, Joshua O., Kazer, Samuel W., Fleming, Ira, Miao, Vincent N., Galvez-Reyes, Jennyfer, Kimata, Jason T., Shalek, Alex K., Holmes, Susan, Overbaugh, Julie, and Blish, Catherine A.
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- 2024
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4. Comparative analysis of cell–cell communication at single-cell resolution
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Wilk, Aaron J., Shalek, Alex K., Holmes, Susan, and Blish, Catherine A.
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- 2024
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5. Profiling the human intestinal environment under physiological conditions
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Shalon, Dari, Culver, Rebecca Neal, Grembi, Jessica A, Folz, Jacob, Treit, Peter V, Shi, Handuo, Rosenberger, Florian A, Dethlefsen, Les, Meng, Xiandong, Yaffe, Eitan, Aranda-Díaz, Andrés, Geyer, Philipp E, Mueller-Reif, Johannes B, Spencer, Sean, Patterson, Andrew D, Triadafilopoulos, George, Holmes, Susan P, Mann, Matthias, Fiehn, Oliver, Relman, David A, and Huang, Kerwyn Casey
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Clinical Research ,Digestive Diseases ,Underpinning research ,1.1 Normal biological development and functioning ,Oral and gastrointestinal ,Humans ,Bile Acids and Salts ,Gastrointestinal Microbiome ,Metabolome ,Proteome ,Bacteria ,Bacteriophages ,Feces ,Intestines ,Digestion ,General Science & Technology - Abstract
The spatiotemporal structure of the human microbiome1,2, proteome3 and metabolome4,5 reflects and determines regional intestinal physiology and may have implications for disease6. Yet, little is known about the distribution of microorganisms, their environment and their biochemical activity in the gut because of reliance on stool samples and limited access to only some regions of the gut using endoscopy in fasting or sedated individuals7. To address these deficiencies, we developed an ingestible device that collects samples from multiple regions of the human intestinal tract during normal digestion. Collection of 240 intestinal samples from 15 healthy individuals using the device and subsequent multi-omics analyses identified significant differences between bacteria, phages, host proteins and metabolites in the intestines versus stool. Certain microbial taxa were differentially enriched and prophage induction was more prevalent in the intestines than in stool. The host proteome and bile acid profiles varied along the intestines and were highly distinct from those of stool. Correlations between gradients in bile acid concentrations and microbial abundance predicted species that altered the bile acid pool through deconjugation. Furthermore, microbially conjugated bile acid concentrations exhibited amino acid-dependent trends that were not apparent in stool. Overall, non-invasive, longitudinal profiling of microorganisms, proteins and bile acids along the intestinal tract under physiological conditions can help elucidate the roles of the gut microbiome and metabolome in human physiology and disease.
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- 2023
6. Generative Models: An Interdisciplinary Perspective
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Sankaran, Kris and Holmes, Susan P.
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Statistics - Methodology ,Statistics - Applications ,Statistics - Computation - Abstract
By linking conceptual theories with observed data, generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, theory building in particle physics, and resource allocation in epidemiology, for example. We introduce the probabilistic and computational concepts underlying modern generative models and then analyze how they can be used to inform experimental design, iterative model refinement, goodness-of-fit evaluation, and agent-based simulation. We emphasize a modular view of generative mechanisms and discuss how they can be flexibly recombined in new problem contexts. We provide practical illustrations throughout, and code for reproducing all examples is available at https://github.com/krisrs1128/generative_review. Finally, we observe how research in generative models is currently split across several islands of activity, and we highlight opportunities lying at disciplinary intersections.
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- 2022
7. Chimpanzee and pig-tailed macaque iPSCs: Improved culture and generation of primate cross-species embryos
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Roodgar, Morteza, Suchy, Fabian P, Nguyen, Lan H, Bajpai, Vivek K, Sinha, Rahul, Vilches-Moure, Jose G, Van Bortle, Kevin, Bhadury, Joydeep, Metwally, Ahmed, Jiang, Lihua, Jian, Ruiqi, Chiang, Rosaria, Oikonomopoulos, Angelos, Wu, Joseph C, Weissman, Irving L, Mankowski, Joseph L, Holmes, Susan, Loh, Kyle M, Nakauchi, Hiromitsu, VandeVoort, Catherine A, and Snyder, Michael P
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Biological Sciences ,Stem Cell Research ,Stem Cell Research - Induced Pluripotent Stem Cell ,Biotechnology ,Stem Cell Research - Nonembryonic - Non-Human ,Animals ,Induced Pluripotent Stem Cells ,Macaca mulatta ,Macaca nemestrina ,Pan troglodytes ,Proteomics ,CP: Developmental biology ,Nonhuman primates ,blastocyst ,BCL2 ,chimpanzee ,cross-species embryos ,iPSCs ,interspecies chimera ,pig-tailed macaque ,Biochemistry and Cell Biology ,Medical Physiology ,Biological sciences - Abstract
As our closest living relatives, non-human primates uniquely enable explorations of human health, disease, development, and evolution. Considerable effort has thus been devoted to generating induced pluripotent stem cells (iPSCs) from multiple non-human primate species. Here, we establish improved culture methods for chimpanzee (Pan troglodytes) and pig-tailed macaque (Macaca nemestrina) iPSCs. Such iPSCs spontaneously differentiate in conventional culture conditions, but can be readily propagated by inhibiting endogenous WNT signaling. As a unique functional test of these iPSCs, we injected them into the pre-implantation embryos of another non-human species, rhesus macaques (Macaca mulatta). Ectopic expression of gene BCL2 enhances the survival and proliferation of chimpanzee and pig-tailed macaque iPSCs within the pre-implantation embryo, although the identity and long-term contribution of the transplanted cells warrants further investigation. In summary, we disclose transcriptomic and proteomic data, cell lines, and cell culture resources that may be broadly enabling for non-human primate iPSCs research.
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- 2022
8. Statistical Modeling for Practical Pooled Testing During the COVID-19 Pandemic
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Comess, Saskia, Wang, Hannah, Holmes, Susan, and Donnat, Claire
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Statistics - Applications - Abstract
Pooled testing offers an efficient solution to the unprecedented testing demands of the COVID-19 pandemic, although with potentially lower sensitivity and increased costs to implementation in some settings. Assessments of this trade-off typically assume pooled specimens are independent and identically distributed. Yet, in the context of COVID-19, these assumptions are often violated: testing done on networks (housemates, spouses, co-workers) captures correlated individuals, while infection risk varies substantially across time, place and individuals. Neglecting dependencies and heterogeneity may bias established optimality grids and induce a sub-optimal implementation of the procedure. As a lesson learned from this pandemic, this paper highlights the necessity of integrating field sampling information with statistical modeling to efficiently optimize pooled testing. Using real data, we show that (a) greater gains can be achieved at low logistical cost by exploiting natural correlations (non-independence) between samples -- allowing improvements in sensitivity and efficiency of up to 30% and 90% respectively; and (b) these gains are robust despite substantial heterogeneity across pools (non-identical). Our modeling results complement and extend the observations of Barak et al (2021) who report an empirical sensitivity well beyond expectations. Finally, we provide an interactive tool for selecting an optimal pool size using contextual information
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- 2021
9. Abrupt perturbation and delayed recovery of the vaginal ecosystem following childbirth
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Costello, Elizabeth K., DiGiulio, Daniel B., Robaczewska, Anna, Symul, Laura, Wong, Ronald J., Shaw, Gary M., Stevenson, David K., Holmes, Susan P., Kwon, Douglas S., and Relman, David A.
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- 2023
- Full Text
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10. A Statistical Perspective on the Challenges in Molecular Microbial Biology
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Jeganathan, Pratheepa and Holmes, Susan P.
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Statistics - Applications - Abstract
High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development., Comment: To appear in the Journal of Agricultural, Biological and Environmental Statistics
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- 2021
11. Reporting guidelines for human microbiome research: the STORMS checklist
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Mirzayi, Chloe, Renson, Audrey, Zohra, Fatima, Elsafoury, Shaimaa, Geistlinger, Ludwig, Kasselman, Lora J, Eckenrode, Kelly, van de Wijgert, Janneke, Loughman, Amy, Marques, Francine Z, MacIntyre, David A, Arumugam, Manimozhiyan, Azhar, Rimsha, Beghini, Francesco, Bergstrom, Kirk, Bhatt, Ami, Bisanz, Jordan E, Braun, Jonathan, Bravo, Hector Corrada, Buck, Gregory A, Bushman, Frederic, Casero, David, Clarke, Gerard, Collado, Maria Carmen, Cotter, Paul D, Cryan, John F, Demmer, Ryan T, Devkota, Suzanne, Elinav, Eran, Escobar, Juan S, Fettweis, Jennifer, Finn, Robert D, Fodor, Anthony A, Forslund, Sofia, Franke, Andre, Furlanello, Cesare, Gilbert, Jack, Grice, Elizabeth, Haibe-Kains, Benjamin, Handley, Scott, Herd, Pamela, Holmes, Susan, Jacobs, Jonathan P, Karstens, Lisa, Knight, Rob, Knights, Dan, Koren, Omry, Kwon, Douglas S, Langille, Morgan, Lindsay, Brianna, McGovern, Dermot, McHardy, Alice C, McWeeney, Shannon, Mueller, Noel T, Nezi, Luigi, Olm, Matthew, Palm, Noah, Pasolli, Edoardo, Raes, Jeroen, Redinbo, Matthew R, Rühlemann, Malte, Balfour Sartor, R, Schloss, Patrick D, Schriml, Lynn, Segal, Eran, Shardell, Michelle, Sharpton, Thomas, Smirnova, Ekaterina, Sokol, Harry, Sonnenburg, Justin L, Srinivasan, Sujatha, Thingholm, Louise B, Turnbaugh, Peter J, Upadhyay, Vaibhav, Walls, Ramona L, Wilmes, Paul, Yamada, Takuji, Zeller, Georg, Zhang, Mingyu, Zhao, Ni, Zhao, Liping, Bao, Wenjun, Culhane, Aedin, Devanarayan, Viswanath, Dopazo, Joaquin, Fan, Xiaohui, Fischer, Matthias, Jones, Wendell, Kusko, Rebecca, Mason, Christopher E, Mercer, Tim R, Sansone, Susanna-Assunta, Scherer, Andreas, Shi, Leming, Thakkar, Shraddha, Tong, Weida, Wolfinger, Russ, Hunter, Christopher, Segata, Nicola, and Huttenhower, Curtis
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Human Genome ,Genetics ,Computational Biology ,Dysbiosis ,Humans ,Microbiota ,Observational Studies as Topic ,Research Design ,Translational Science ,Biomedical ,Genomic Standards Consortium ,Massive Analysis and Quality Control Society ,Medical and Health Sciences ,Immunology - Abstract
The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.
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- 2021
12. Modeling the Heterogeneity in COVID-19's Reproductive Number and its Impact on Predictive Scenarios
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Donnat, Claire and Holmes, Susan
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Statistics - Applications ,Quantitative Biology - Populations and Evolution - Abstract
The correct evaluation of the reproductive number $R$ for COVID-19 -- which characterizes the average number of secondary cases generated by each typical primary case -- is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, $R$ is modeled as a universal constant for the virus across outbreak clusters and individuals -- effectively averaging out the inherent variability of the transmission process due to varying individual contact rates, population densities, demographics, or temporal factors amongst many. Yet, due to the exponential nature of epidemic growth, the error due to this simplification can be rapidly amplified and lead to inaccurate predictions and/or risk evaluation. From the statistical modeling perspective, the magnitude of the impact of this averaging remains an open question: how can this intrinsic variability be percolated into epidemic models, and how can its impact on uncertainty quantification and predictive scenarios be better quantified? In this paper, we propose to study this question through a Bayesian perspective, creating a bridge between the agent-based and compartmental approaches commonly used in the literature. After deriving a Bayesian model that captures at scale the heterogeneity of a population and environmental conditions, we simulate the spread of the epidemic as well as the impact of different social distancing strategies, and highlight the strong impact of this added variability on the reported results. We base our discussion on both synthetic experiments -- thereby quantifying of the reliability and the magnitude of the effects -- and real COVID-19 data.
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- 2020
13. Geomstats: A Python Package for Riemannian Geometry in Machine Learning
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Miolane, Nina, Brigant, Alice Le, Mathe, Johan, Hou, Benjamin, Guigui, Nicolas, Thanwerdas, Yann, Heyder, Stefan, Peltre, Olivier, Koep, Niklas, Zaatiti, Hadi, Hajri, Hatem, Cabanes, Yann, Gerald, Thomas, Chauchat, Paul, Shewmake, Christian, Kainz, Bernhard, Donnat, Claire, Holmes, Susan, and Pennec, Xavier
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Computer Science - Machine Learning ,Computer Science - Mathematical Software - Abstract
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at \url{geomstats.ai}.
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- 2020
14. Arcsine laws for random walks generated from random permutations with applications to genomics
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Fang, Xiao, Gan, Han Liang, Holmes, Susan, Huang, Haiyan, Peköz, Erol, Röllin, Adrian, and Tang, Wenpin
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Mathematics - Probability ,Mathematics - Statistics Theory ,60C05, 60J65, 05A05 - Abstract
A classical result for the simple symmetric random walk with $2n$ steps is that the number of steps above the origin, the time of the last visit to the origin, and the time of the maximum height all have exactly the same distribution and converge when scaled to the arcsine law. Motivated by applications in genomics, we study the distributions of these statistics for the non-Markovian random walk generated from the ascents and descents of a uniform random permutation and a Mallows($q$) permutation and show that they have the same asymptotic distributions as for the simple random walk. We also give an unexpected conjecture, along with numerical evidence and a partial proof in special cases, for the result that the number of steps above the origin by step $2n$ for the uniform permutation generated walk has exactly the same discrete arcsine distribution as for the simple random walk, even though the other statistics for these walks have very different laws. We also give explicit error bounds to the limit theorems using Stein's method for the arcsine distribution, as well as functional central limit theorems and a strong embedding of the Mallows$(q)$ permutation which is of independent interest.
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- 2020
15. Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders
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Miolane, Nina and Holmes, Susan
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Statistics - Machine Learning ,Computer Science - Machine Learning ,Mathematics - Differential Geometry - Abstract
Manifold-valued data naturally arises in medical imaging. In cognitive neuroscience, for instance, brain connectomes base the analysis of coactivation patterns between different brain regions on the analysis of the correlations of their functional Magnetic Resonance Imaging (fMRI) time series - an object thus constrained by construction to belong to the manifold of symmetric positive definite matrices. One of the challenges that naturally arises consists of finding a lower-dimensional subspace for representing such manifold-valued data. Traditional techniques, like principal component analysis, are ill-adapted to tackle non-Euclidean spaces and may fail to achieve a lower-dimensional representation of the data - thus potentially pointing to the absence of lower-dimensional representation of the data. However, these techniques are restricted in that: (i) they do not leverage the assumption that the connectomes belong on a pre-specified manifold, therefore discarding information; (ii) they can only fit a linear subspace to the data. In this paper, we are interested in variants to learn potentially highly curved submanifolds of manifold-valued data. Motivated by the brain connectomes example, we investigate a latent variable generative model, which has the added benefit of providing us with uncertainty estimates - a crucial quantity in the medical applications we are considering. While latent variable models have been proposed to learn linear and nonlinear spaces for Euclidean data, or geodesic subspaces for manifold data, no intrinsic latent variable model exists to learn nongeodesic subspaces for manifold data. This paper fills this gap and formulates a Riemannian variational autoencoder with an intrinsic generative model of manifold-valued data. We evaluate its performances on synthetic and real datasets by introducing the formalism of weighted Riemannian submanifolds.
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- 2019
16. Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks
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Miolane, Nina, Poitevin, Frédéric, Li, Yee-Ting, and Holmes, Susan
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Quantitative Biology - Quantitative Methods ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Statistics - Machine Learning - Abstract
Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at near-atomic resolution. As such, it represents one of the most promising imaging techniques in structural biology. However, raw cryo-EM images are only highly corrupted - noisy and band-pass filtered - 2D projections of the target 3D biomolecules. Reconstructing the 3D molecular shape starts with the removal of image outliers, the estimation of the orientation of the biomolecule that has produced the given 2D image, and the estimation of camera parameters to correct for intensity defects. Current techniques performing these tasks are often computationally expensive, while the dataset sizes keep growing. There is a need for next-generation algorithms that preserve accuracy while improving speed and scalability. In this paper, we combine variational autoencoders (VAEs) and generative adversarial networks (GANs) to learn a low-dimensional latent representation of cryo-EM images. We perform an exploratory analysis of the obtained latent space, that is shown to have a structure of "orbits", in the sense of Lie group theory, consistent with the acquisition procedure of cryo-EM images. This analysis leads us to design an estimation method for orientation and camera parameters of single-particle cryo-EM images, together with an outliers detection procedure. As such, it opens the door to geometric approaches for unsupervised estimations of orientations and camera parameters, making possible fast cryo-EM biomolecule reconstruction.
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- 2019
17. Constrained Bayesian ICA for Brain Connectome Inference
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Donnat, Claire, Tozzi, Leonardo, and Holmes, Susan
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Statistics - Applications ,Quantitative Biology - Neurons and Cognition - Abstract
Brain connectomics is a developing field in neurosciences which strives to understand cognitive processes and psychiatric diseases through the analysis of interactions between brain regions. However, in the high-dimensional, low-sample, and noisy regimes that typically characterize fMRI data, the recovery of such interactions remains an ongoing challenge: how can we discover patterns of co-activity between brain regions that could then be associated to cognitive processes or psychiatric disorders? In this paper, we investigate a constrained Bayesian ICA approach which, in comparison to current methods, simultaneously allows (a) the flexible integration of multiple sources of information (fMRI, DWI, anatomical, etc.), (b) an automatic and parameter-free selection of the appropriate sparsity level and number of connected submodules and (c) the provision of estimates on the uncertainty of the recovered interactions. Our experiments, both on synthetic and real-life data, validate the flexibility of our method and highlight the benefits of integrating anatomical information for connectome inference.
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- 2019
18. Convex Hierarchical Clustering for Graph-Structured Data
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Donnat, Claire and Holmes, Susan
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Statistics - Applications ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Convex clustering is a recent stable alternative to hierarchical clustering. It formulates the recovery of progressively coalescing clusters as a regularized convex problem. While convex clustering was originally designed for handling Euclidean distances between data points, in a growing number of applications, the data is directly characterized by a similarity matrix or weighted graph. In this paper, we extend the robust hierarchical clustering approach to these broader classes of similarities. Having defined an appropriate convex objective, the crux of this adaptation lies in our ability to provide: (a) an efficient recovery of the regularization path and (b) an empirical demonstration of the use of our method. We address the first challenge through a proximal dual algorithm, for which we characterize both the theoretical efficiency as well as the empirical performance on a set of experiments. Finally, we highlight the potential of our method by showing its application to several real-life datasets, thus providing a natural extension to the current scope of applications of convex clustering.
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- 2019
19. Uncertainty Quantification in Multivariate Mixed Models for Mass Cytometry Data
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Seiler, Christof, Kronstad, Lisa M., Simpson, Laura J., Gars, Mathieu Le, Vendrame, Elena, Blish, Catherine A., and Holmes, Susan
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Statistics - Applications - Abstract
Mass cytometry technology enables the simultaneous measurement of over 40 proteins on single cells. This has helped immunologists to increase their understanding of heterogeneity, complexity, and lineage relationships of white blood cells. Current statistical methods often collapse the rich single-cell data into summary statistics before proceeding with downstream analysis, discarding the information in these multivariate datasets. In this article, our aim is to exhibit the use of statistical analyses on the raw, uncompressed data thus improving replicability, and exposing multivariate patterns and their associated uncertainty profiles. We show that multivariate generative models are a valid alternative to univariate hypothesis testing. We propose two models: a multivariate Poisson log-normal mixed model and a logistic linear mixed model. We show that these models are complementary and that either model can account for different confounders. We use Hamiltonian Monte Carlo to provide Bayesian uncertainty quantification. Our models applied to a recent pregnancy study successfully reproduce key findings while quantifying increased overall protein-to-protein correlations between first and third trimester.
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- 2019
20. Effect of water, sanitation, handwashing and nutrition interventions on enteropathogens in children 14 months old: a cluster-randomized controlled trial in rural Bangladesh.
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Grembi, Jessica A, Lin, Audrie, Karim, Md Abdul, Islam, Md Ohedul, Miah, Rana, Arnold, Benjamin F, McQuade, Elizabeth T Rogawski, Ali, Shahjahan, Rahman, Md Ziaur, Hussain, Zahir, Shoab, Abul K, Famida, Syeda L, Hossen, Md Saheen, Mutsuddi, Palash, Rahman, Mahbubur, Unicomb, Leanne, Haque, Rashidul, Taniuchi, Mami, Liu, Jie, Platts-Mills, James A, Holmes, Susan P, Stewart, Christine P, Benjamin-Chung, Jade, Colford, John M, Houpt, Eric R, and Luby, Stephen P
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Bangladesh ,and handwashing ,child health ,enteric pathogens ,nutrition ,sanitation ,water ,Digestive Diseases ,Clinical Research ,Infectious Diseases ,Nutrition ,Prevention ,Clinical Trials and Supportive Activities ,Pediatric ,Infection ,Microbiology ,Biological Sciences ,Medical and Health Sciences - Abstract
BackgroundWe evaluated the impact of low-cost water, sanitation, handwashing (WSH) and child nutrition interventions on enteropathogen carriage in the WASH Benefits cluster-randomized controlled trial in rural Bangladesh.MethodsWe analyzed 1411 routine fecal samples from children 14±2 months old in the WSH (n = 369), nutrition counseling plus lipid-based nutrient supplement (n = 353), nutrition plus WSH (n = 360), and control (n = 329) arms for 34 enteropathogens using quantitative PCR. Outcomes included the number of co-occurring pathogens; cumulative quantity of four stunting-associated pathogens; and prevalence and quantity of individual pathogens. Masked analysis was by intention-to-treat.Results326 (99.1%) control children had one or more enteropathogens detected (mean 3.8±1.8). Children receiving WSH interventions had lower prevalence and quantity of individual viruses than controls (prevalence difference for norovirus: -11% [95% confidence interval [CI], -5 to -17%]; sapovirus: -9% [95%CI, -3 to -15%]; and adenovirus 40/41: -9% [95%CI, -2 to - 15%]). There was no difference in bacteria, parasites, or cumulative quantity of stunting-associated pathogens between controls and any intervention arm.ConclusionsWSH interventions were associated with fewer enteric viruses in children aged 14 months. Different strategies are needed to reduce enteric bacteria and parasites at this critical young age.
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- 2020
21. Microbiota assembly, structure, and dynamics among Tsimane horticulturalists of the Bolivian Amazon.
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Sprockett, Daniel D, Martin, Melanie, Costello, Elizabeth K, Burns, Adam R, Holmes, Susan P, Gurven, Michael D, and Relman, David A
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Tongue ,Feces ,Humans ,DNA ,Bacterial ,RNA ,Ribosomal ,16S ,Longitudinal Studies ,Phylogeny ,Adolescent ,Adult ,Middle Aged ,Child ,Child ,Preschool ,Infant ,Infant ,Newborn ,Bolivia ,Female ,Male ,Young Adult ,Microbiota ,Horticulture ,Indigenous Peoples ,Preschool ,DNA ,Bacterial ,Newborn ,RNA ,Ribosomal ,16S - Abstract
Selective and neutral forces shape human microbiota assembly in early life. The Tsimane are an indigenous Bolivian population with infant care-associated behaviors predicted to increase mother-infant microbial dispersal. Here, we characterize microbial community assembly in 47 infant-mother pairs from six Tsimane villages, using 16S rRNA gene amplicon sequencing of longitudinal stool and tongue swab samples. We find that infant consumption of dairy products, vegetables, and chicha (a fermented drink inoculated with oral microbes) is associated with stool microbiota composition. In stool and tongue samples, microbes shared between mothers and infants are more abundant than non-shared microbes. Using a neutral model of community assembly, we find that neutral processes alone explain the prevalence of 79% of infant-colonizing microbes, but explain microbial prevalence less well in adults from river villages with more regular access to markets. Our results underscore the importance of neutral forces during microbiota assembly. Changing lifestyle factors may alter traditional modes of microbiota assembly by decreasing the role of neutral processes.
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- 2020
22. Variability in the analysis of a single neuroimaging dataset by many teams
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Botvinik-Nezer, Rotem, Holzmeister, Felix, Camerer, Colin F, Dreber, Anna, Huber, Juergen, Johannesson, Magnus, Kirchler, Michael, Iwanir, Roni, Mumford, Jeanette A, Adcock, R Alison, Avesani, Paolo, Baczkowski, Blazej M, Bajracharya, Aahana, Bakst, Leah, Ball, Sheryl, Barilari, Marco, Bault, Nadège, Beaton, Derek, Beitner, Julia, Benoit, Roland G, Berkers, Ruud MWJ, Bhanji, Jamil P, Biswal, Bharat B, Bobadilla-Suarez, Sebastian, Bortolini, Tiago, Bottenhorn, Katherine L, Bowring, Alexander, Braem, Senne, Brooks, Hayley R, Brudner, Emily G, Calderon, Cristian B, Camilleri, Julia A, Castrellon, Jaime J, Cecchetti, Luca, Cieslik, Edna C, Cole, Zachary J, Collignon, Olivier, Cox, Robert W, Cunningham, William A, Czoschke, Stefan, Dadi, Kamalaker, Davis, Charles P, Luca, Alberto De, Delgado, Mauricio R, Demetriou, Lysia, Dennison, Jeffrey B, Di, Xin, Dickie, Erin W, Dobryakova, Ekaterina, Donnat, Claire L, Dukart, Juergen, Duncan, Niall W, Durnez, Joke, Eed, Amr, Eickhoff, Simon B, Erhart, Andrew, Fontanesi, Laura, Fricke, G Matthew, Fu, Shiguang, Galván, Adriana, Gau, Remi, Genon, Sarah, Glatard, Tristan, Glerean, Enrico, Goeman, Jelle J, Golowin, Sergej AE, González-García, Carlos, Gorgolewski, Krzysztof J, Grady, Cheryl L, Green, Mikella A, Guassi Moreira, João F, Guest, Olivia, Hakimi, Shabnam, Hamilton, J Paul, Hancock, Roeland, Handjaras, Giacomo, Harry, Bronson B, Hawco, Colin, Herholz, Peer, Herman, Gabrielle, Heunis, Stephan, Hoffstaedter, Felix, Hogeveen, Jeremy, Holmes, Susan, Hu, Chuan-Peng, Huettel, Scott A, Hughes, Matthew E, Iacovella, Vittorio, Iordan, Alexandru D, Isager, Peder M, Isik, Ayse I, Jahn, Andrew, Johnson, Matthew R, Johnstone, Tom, Joseph, Michael JE, Juliano, Anthony C, Kable, Joseph W, Kassinopoulos, Michalis, Koba, Cemal, and Kong, Xiang-Zhen
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Generic health relevance ,Brain ,Data Analysis ,Data Science ,Datasets as Topic ,Female ,Functional Neuroimaging ,Humans ,Logistic Models ,Magnetic Resonance Imaging ,Male ,Meta-Analysis as Topic ,Models ,Neurological ,Reproducibility of Results ,Research Personnel ,Software ,General Science & Technology - Abstract
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
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- 2020
23. Microbial biogeography and ecology of the mouth and implications for periodontal diseases.
- Author
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Proctor, Diana M, Shelef, Katie M, Gonzalez, Antonio, Davis, Clara L, Dethlefsen, Les, Burns, Adam R, Loomer, Peter M, Armitage, Gary C, Ryder, Mark I, Millman, Meredith E, Knight, Rob, Holmes, Susan P, and Relman, David A
- Subjects
Mouth ,Humans ,Periodontal Diseases ,Periodontitis ,Dental Caries ,Microbiota ,biogeography ,oral microbiome ,oral microbiota ,spatial pattern ,subgingival ,supragingival ,Clinical Research ,Dental/Oral and Craniofacial Disease ,Infectious Diseases ,Oral and gastrointestinal ,Dentistry - Abstract
In humans, the composition of microbial communities differs among body sites and between habitats within a single site. Patterns of variation in the distribution of organisms across time and space are referred to as "biogeography." The human oral cavity is a critical observatory for exploring microbial biogeography because it is spatially structured, easily accessible, and its microbiota has been linked to the promotion of both health and disease. The biogeographic features of microbial communities residing in spatially distinct, but ecologically similar, environments on the human body, including the subgingival crevice, have not yet been adequately explored. The purpose of this paper is twofold. First, we seek to provide the dental community with a primer on biogeographic theory, highlighting its relevance to the study of the human oral cavity. We summarize what is known about the biogeographic variation of dental caries and periodontitis and postulate that disease occurrence reflects spatial patterning in the composition and structure of oral microbial communities. Second, we present a number of methods that investigators can use to test specific hypotheses using biogeographic theory. To anchor our discussion, we apply each method to a case study and examine the spatial variation of the human subgingival microbiota in 2 individuals. Our case study suggests that the composition of subgingival communities may conform to an anterior-to-posterior gradient within the oral cavity. The gradient appears to be structured by both deterministic and nondeterministic processes, although additional work is needed to confirm these findings. A better understanding of biogeographic patterns and processes will lead to improved efficacy of dental interventions targeting the oral microbiota.
- Published
- 2020
24. The Block Bootstrap Method for Longitudinal Microbiome Data
- Author
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Jeganathan, Pratheepa, Callahan, Benjamin J., Proctor, Diana M., Relman, David A., and Holmes, Susan P.
- Subjects
Statistics - Methodology ,Statistics - Applications - Abstract
Microbial ecology serves as a foundation for a wide range of scientific and biomedical studies. Rapidly-evolving high-throughput sequencing technology enables the comprehensive search for microbial biomarkers using longitudinal experiments. Such experiments consist of repeated biological observations from each subject over time and are essential in accounting for the high between-subject and within-subject variability. Unfortunately, many of the statistical tests based on parametric models rely on correctly specifying temporal dependence structure which is unavailable in most microbiome data. In this paper, we propose an extension of the nonparametric bootstrap method that enables inference on these types longitudinal data. The proposed moving block bootstrap (MBB) method accounts for within-subject dependency by using overlapping blocks of repeated observations within each subject to draw valid inferences based on approximately pivotal statistics. Our simulation studies show an increase in power compared to merge-by-subject (MBS) strategies. We also show that compared to tests that presume independent samples (PIS), our proposed method reduces false microbial biomarker discovery rates. In this paper, we illustrated the MBB method using three different pregnancy data and an oral microbiome data. We provide an open-source R package https://github.com/PratheepaJ/bootLong to make our method accessible and the study in this paper reproducible.
- Published
- 2018
25. Tracking network dynamics: a survey of distances and similarity metrics
- Author
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Donnat, Claire and Holmes, Susan
- Subjects
Statistics - Applications ,Computer Science - Social and Information Networks ,Physics - Physics and Society - Abstract
From longitudinal biomedical studies to social networks, graphs have emerged as a powerful framework for describing evolving interactions between agents in complex systems. In such studies, after pre-processing, the data can be represented by a set of graphs, each representing a system's state at different points in time. The analysis of the system's dynamics depends on the selection of the appropriate analytical tools. After characterizing similarities between states, a critical step lies in the choice of a distance between graphs capable of reflecting such similarities. While the literature offers a number of distances that one could a priori choose from, their properties have been little investigated and no guidelines regarding the choice of such a distance have yet been provided. In particular, most graph distances consider that the nodes are exchangeable and do not take into account node identities. Accounting for the alignment of the graphs enables us to enhance these distances' sensitivity to perturbations in the network and detect important changes in graph dynamics. Thus the selection of an adequate metric is a decisive --yet delicate--practical matter. In the spirit of Goldenberg, Zheng and Fienberg's seminal 2009 review, the purpose of this article is to provide an overview of commonly-used graph distances and an explicit characterization of the structural changes that they are best able to capture. We use as a guiding thread to our discussion the application of these distances to the analysis of both a longitudinal microbiome dataset and a brain fMRI study. We show examples of using permutation tests to detect the effect of covariates on the graphs' variability. Synthetic examples provide intuition as to the qualities and drawbacks of the different distances. Above all, we provide some guidance for choosing one distance over another in certain types of applications.
- Published
- 2018
26. SARS-CoV-2 escapes direct NK cell killing through Nsp1-mediated downregulation of ligands for NKG2D
- Author
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Lee, Madeline J., Leong, Michelle W., Rustagi, Arjun, Beck, Aimee, Zeng, Leiping, Holmes, Susan, Qi, Lei S., and Blish, Catherine A.
- Published
- 2022
- Full Text
- View/download PDF
27. Genotypic correlates of resistance to the HIV-1 strand transfer integrase inhibitor cabotegravir
- Author
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Rhee, Soo-Yon, Parkin, Neil, Harrigan, P. Richard, Holmes, Susan, and Shafer, Robert W.
- Published
- 2022
- Full Text
- View/download PDF
28. Inference of Dynamic Regimes in the Microbiome
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Sankaran, Kris and Holmes, Susan P.
- Subjects
Statistics - Applications - Abstract
Many studies have been performed to characterize the dynamics and stability of the microbiome across a range of environmental contexts [Costello et al., 2012, Faust et al., 2015]. For example, it is often of interest to identify time intervals within which certain subsets of taxa have an interesting pattern of behavior. Viewed abstractly, these problems often have a flavor not just of time series modeling but also of regime detection, a problem with a rich history across a variety of applications, including speech recognition [Fox et al., 2011], finance [Lee, 2009], EEG analysis [Camilleri et al., 2014], and geophysics [Weatherley and Mora, 2002]. In spite of the parallels, regime detection methods are rarely used in microbiome analysis, most likely due to the fact that references for these methods are scattered across several literatures, descriptions are inaccessible outside limited research communities, and implementations are difficult to come across. We distill the core ideas of different regime detection methods, provide example applications, and share reproducible code, making these techniques more accessible to microbiome researchers. We re-analyze data of Dethlefsen and Relman [2011], a study of the effects of antibiotics on the microbiome, using Classification and Regression Trees (CART) [Breiman et al., 1984], Hidden Markov Models (HMMs) [Rabiner and Juang, 1986], Bayesian nonparametric HMMs [Teh and Jordan, 2010, Fox et al., 2008], mixtures of Gaussian Processes (GPs) [Rasmussen and Ghahramani, 2002], switching dynamical systems [Linderman et al., 2016], and multiple changepoint detection [Fan and Mackey, 2015]. Along the way, we summarize each method, their relevance to the microbiome, and tradeoffs associated with using them. Ultimately, our goal is to describe types of temporal or regime switching structure that can be incorporated into studies of microbiome dynamics.
- Published
- 2017
29. Gut microbiome transition across a lifestyle gradient in Himalaya.
- Author
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Jha, Aashish, Davenport, Emily, Gautam, Yoshina, Bhandari, Dinesh, Tandukar, Sarmila, Ng, Katharine, Fragiadakis, Gabriela, Holmes, Susan, Gautam, Guru, Leach, Jeff, Sherchand, Jeevan, Bustamante, Carlos, and Sonnenburg, Justin
- Subjects
Adult ,Bacteria ,Diet ,Diet ,Paleolithic ,Feces ,Female ,Gastrointestinal Microbiome ,Genetics ,Population ,Geography ,Humans ,Life Style ,Male ,Microbiota ,Middle Aged ,Nepal ,RNA ,Ribosomal ,16S ,Rural Population - Abstract
The composition of the gut microbiome in industrialized populations differs from those living traditional lifestyles. However, it has been difficult to separate the contributions of human genetic and geographic factors from lifestyle. Whether shifts away from the foraging lifestyle that characterize much of humanitys past influence the gut microbiome, and to what degree, remains unclear. Here, we characterize the stool bacterial composition of four Himalayan populations to investigate how the gut community changes in response to shifts in traditional human lifestyles. These groups led seminomadic hunting-gathering lifestyles until transitioning to varying levels of agricultural dependence upon farming. The Tharu began farming 250-300 years ago, the Raute and Raji transitioned 30-40 years ago, and the Chepang retain many aspects of a foraging lifestyle. We assess the contributions of dietary and environmental factors on their gut-associated microbes and find that differences in the lifestyles of Himalayan foragers and farmers are strongly correlated with microbial community variation. Furthermore, the gut microbiomes of all four traditional Himalayan populations are distinct from that of the Americans, indicating that industrialization may further exacerbate differences in the gut community. The Chepang foragers harbor an elevated abundance of taxa associated with foragers around the world. Conversely, the gut microbiomes of the populations that have transitioned to farming are more similar to those of Americans, with agricultural dependence and several associated lifestyle and environmental factors correlating with the extent of microbiome divergence from the foraging population. The gut microbiomes of Raute and Raji reveal an intermediate state between the Chepang and Tharu, indicating that divergence from a stereotypical foraging microbiome can occur within a single generation. Our results also show that environmental factors such as drinking water source and solid cooking fuel are significantly associated with the gut microbiome. Despite the pronounced differences in gut bacterial composition across populations, we found little differences in alpha diversity across lifestyles. These findings in genetically similar populations living in the same geographical region establish the key role of lifestyle in determining human gut microbiome composition and point to the next challenging steps of determining how large-scale gut microbiome reconfiguration impacts human biology.
- Published
- 2018
30. Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome
- Author
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Goltsman, Daniela S Aliaga, Sun, Christine L, Proctor, Diana M, DiGiulio, Daniel B, Robaczewska, Anna, Thomas, Brian C, Shaw, Gary M, Stevenson, David K, Holmes, Susan P, Banfield, Jillian F, and Relman, David A
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Perinatal Period - Conditions Originating in Perinatal Period ,Biotechnology ,Human Genome ,Genetics ,Pediatric ,2.1 Biological and endogenous factors ,Aetiology ,Bacteria ,Contig Mapping ,DNA ,Bacterial ,DNA ,Ribosomal ,Female ,Gastrointestinal Tract ,Humans ,Longitudinal Studies ,Metagenomics ,Phylogeny ,Pregnancy ,Pregnancy Outcome ,RNA ,Ribosomal ,16S ,Sequence Analysis ,DNA ,Vagina ,Biological Sciences ,Medical and Health Sciences ,Bioinformatics - Abstract
Recent studies suggest that the microbiome has an impact on gestational health and outcome. However, characterization of the pregnancy-associated microbiome has largely relied on 16S rRNA gene amplicon-based surveys. Here, we describe an assembly-driven, metagenomics-based, longitudinal study of the vaginal, gut, and oral microbiomes in 292 samples from 10 subjects sampled every three weeks throughout pregnancy. Nonhuman sequences in the amount of 1.53 Gb were assembled into scaffolds, and functional genes were predicted for gene- and pathway-based analyses. Vaginal assemblies were binned into 97 draft quality genomes. Redundancy analysis (RDA) of microbial community composition at all three body sites revealed gestational age to be a significant source of variation in patterns of gene abundance. In addition, health complications were associated with variation in community functional gene composition in the mouth and gut. The diversity of Lactobacillus iners-dominated communities in the vagina, unlike most other vaginal community types, significantly increased with gestational age. The genomes of co-occurring Gardnerella vaginalis strains with predicted distinct functions were recovered in samples from two subjects. In seven subjects, gut samples contained strains of the same Lactobacillus species that dominated the vaginal community of that same subject and not other Lactobacillus species; however, these within-host strains were divergent. CRISPR spacer analysis suggested shared phage and plasmid populations across body sites and individuals. This work underscores the dynamic behavior of the microbiome during pregnancy and suggests the potential importance of understanding the sources of this behavior for fetal development and gestational outcome.
- Published
- 2018
31. Extrusion and 3D printing of novel lipid-polymer blends for oral drug applications
- Author
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Tang, Tiffany O., Holmes, Susan, Boyd, Ben J., and Simon, George P.
- Published
- 2022
- Full Text
- View/download PDF
32. Latent Variable Modeling for the Microbiome
- Author
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Sankaran, Kris and Holmes, Susan P.
- Subjects
Statistics - Applications - Abstract
The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet Allocation, Nonnegative Matrix Factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of [10], a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly., Comment: 31 pages, 16 figures
- Published
- 2017
33. Towards Sustainable Materials: A Review of Acylhydrazone Chemistry for Reversible Polymers.
- Author
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Ramimoghadam, Donya, Eyckens, Daniel J., Evans, Richard A., Moad, Graeme, Holmes, Susan, and Simons, Ranya
- Abstract
Transitioning towards a circular economy, extensive research has focused on dynamic covalent bonds (DCBs) to pave the way for more sustainable materials. These bonds enable debonding and rebonding on demand, as well as facilitating end‐of‐life recycling. Acylhydrazone/hydrazone chemistry offers a material with high stability under neutral and basic conditions making it a promising candidate for materials research, though the material is susceptible to acid degradation. However, this degradation under acidic conditions can be exploited, making it widely applicable in self‐healing and biomedical fields, with potential for reprocessing and recycling. This review highlights studies exploring the reversibility of acylhydrazone/hydrazone bonds in various polymers, altering their properties, and utilizing them in applications such as self‐healing, reprocessing, and recycling. The review also focuses on how the mechanical properties are affected by the presence of dynamic linkages, and methods to improve the mechanical performance. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
34. A spatial gradient of bacterial diversity in the human oral cavity shaped by salivary flow.
- Author
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Proctor, Diana M, Fukuyama, Julia A, Loomer, Peter M, Armitage, Gary C, Lee, Stacey A, Davis, Nicole M, Ryder, Mark I, Holmes, Susan P, and Relman, David A
- Subjects
Mouth ,Tongue ,Mouth Mucosa ,Saliva ,Tooth ,Humans ,Bacteria ,Sjogren's Syndrome ,Xerostomia ,RNA ,Ribosomal ,16S ,Biodiversity ,Salivation ,Adult ,Aged ,Middle Aged ,Female ,Male ,Genetic Variation ,Young Adult ,RNA ,Ribosomal ,16S ,Sjogrens Syndrome - Abstract
Spatial and temporal patterns in microbial communities provide insights into the forces that shape them, their functions and roles in health and disease. Here, we used spatial and ecological statistics to analyze the role that saliva plays in structuring bacterial communities of the human mouth using >9000 dental and mucosal samples. We show that regardless of tissue type (teeth, alveolar mucosa, keratinized gingiva, or buccal mucosa), surface-associated bacterial communities vary along an ecological gradient from the front to the back of the mouth, and that on exposed tooth surfaces, the gradient is pronounced on lingual compared to buccal surfaces. Furthermore, our data suggest that this gradient is attenuated in individuals with low salivary flow due to Sjögren's syndrome. Taken together, our findings imply that salivary flow influences the spatial organization of microbial communities and that biogeographical patterns may be useful for understanding host physiological processes and for predicting disease.
- Published
- 2018
35. Acquired Disorders of Hair
- Author
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Harries, Matthew J., primary, Holmes, Susan, additional, McMichael, Amy, additional, and Messenger, Andrew G., additional
- Published
- 2024
- Full Text
- View/download PDF
36. Longitudinal gut microbiota composition of South African and Nigerian infants in relation to tetanus vaccine responses
- Author
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Iwase, Saori C., primary, Osawe, Sophia, additional, Happel, Anna-Ursula, additional, Gray, Clive M., additional, Holmes, Susan P., additional, Blackburn, Jonathan M., additional, Abimiku, Alash'le, additional, and Jaspan, Heather B., additional
- Published
- 2024
- Full Text
- View/download PDF
37. Template shape estimation: correcting an asymptotic bias
- Author
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Miolane, Nina, Holmes, Susan, and Pennec, Xavier
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Mathematics - Differential Geometry - Abstract
We use tools from geometric statistics to analyze the usual estimation procedure of a template shape. This applies to shapes from landmarks, curves, surfaces, images etc. We demonstrate the asymptotic bias of the template shape estimation using the stratified geometry of the shape space. We give a Taylor expansion of the bias with respect to a parameter $\sigma$ describing the measurement error on the data. We propose two bootstrap procedures that quantify the bias and correct it, if needed. They are applicable for any type of shape data. We give a rule of thumb to provide intuition on whether the bias has to be corrected. This exhibits the parameters that control the bias' magnitude. We illustrate our results on simulated and real shape data.
- Published
- 2016
38. Bayesian Nonparametric Ordination for the Analysis of Microbial Communities
- Author
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Ren, Boyu, Bacallado, Sergio, Favaro, Stefano, Holmes, Susan, and Trippa, Lorenzo
- Subjects
Statistics - Methodology ,Statistics - Applications - Abstract
Human microbiome studies use sequencing technologies to measure the abundance of bacterial species or Operational Taxonomic Units (OTUs) in samples of biological material. Typically the data are organized in contingency tables with OTU counts across heterogeneous biological samples. In the microbial ecology community, ordination methods are frequently used to investigate latent factors or clusters that capture and describe variations of OTU counts across biological samples. It remains important to evaluate how uncertainty in estimates of each biological sample's microbial distribution propagates to ordination analyses, including visualization of clusters and projections of biological samples on low dimensional spaces. We propose a Bayesian analysis for dependent distributions to endow frequently used ordinations with estimates of uncertainty. A Bayesian nonparametric prior for dependent normalized random measures is constructed, which is marginally equivalent to the normalized generalized Gamma process, a well-known prior for nonparametric analyses. In our prior the dependence and similarity between microbial distributions is represented by latent factors that concentrate in a low dimensional space. We use a shrinkage prior to tune the dimensionality of the latent factors. The resulting posterior samples of model parameters can be used to evaluate uncertainty in analyses routinely applied in microbiome studies. Specifically, by combining them with multivariate data analysis techniques we can visualize credible regions in ecological ordination plots. The characteristics of the proposed model are illustrated through a simulation study and applications in two microbiome datasets.
- Published
- 2016
- Full Text
- View/download PDF
39. Sub-communities of the vaginal microbiota in pregnant and non-pregnant women
- Author
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Symul, Laura, primary, Jeganathan, Pratheepa, additional, Costello, Elizabeth K., additional, France, Michael, additional, Bloom, Seth M., additional, Kwon, Douglas S., additional, Ravel, Jacques, additional, Relman, David A., additional, and Holmes, Susan, additional
- Published
- 2023
- Full Text
- View/download PDF
40. Impact of Previous Alopecia Areata Treatment on Efficacy Responses After 24 and 48 Weeks of Treatment With Ritlecitinib
- Author
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Fu, Jennifer, primary, Egeberg, Alexander, additional, Holmes, Susan, additional, Vano-Galvan, Sergio, additional, Steinhoff, Martin, additional, Edwards, Roger, additional, Nagra, Ranjit, additional, Wolk, Robert, additional, Tran, Helen, additional, and Law, Ernest, additional
- Published
- 2023
- Full Text
- View/download PDF
41. Shared Genetic Risk Variants in Both Male and Female Frontal Fibrosing Alopecia
- Author
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Rayinda, Tuntas, primary, McSweeney, Sheila M., additional, Fenton, David, additional, Stefanato, Catherine M., additional, Harries, Matthew, additional, Palamaras, Ioulios, additional, Tidman, Alice, additional, Holmes, Susan, additional, Koutalopoulou, Anastasia, additional, Ardern-Jones, Michael, additional, Williams, Greg, additional, Papanikou, Sofia, additional, Chasapi, Vasiliki, additional, Vañó-Galvan, Sergio, additional, Saceda-Corralo, David, additional, Melián-Olivera, Ana, additional, Azcarraga-Llobet, Carlos, additional, Lobato-Berezo, Alejandro, additional, Bustamante, Mariona, additional, Sunyer, Jordi, additional, Starace, Michela Valeria Rita, additional, Piraccini, Bianca Maria, additional, Wiss, Isabel Pupo, additional, Senna, Maryanne Makredes, additional, Singh, Rashmi, additional, Hilmann, Kathrin, additional, Kanti-Schmidt, Varvara, additional, Blume-Peytavi, Ulrike, additional, Simpson, Michael, additional, McGrath, John A., additional, Dand, Nick, additional, and Tziotzios, Christos, additional
- Published
- 2023
- Full Text
- View/download PDF
42. A short course of antibiotics selects for persistent resistance in the human gut
- Author
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Yaffe, Eitan, primary, Dethlefsen, Les, additional, Patankar, Arati, additional, Gui, Chen, additional, Holmes, Susan, additional, and Relman, David, additional
- Published
- 2023
- Full Text
- View/download PDF
43. Curvature and Concentration of Hamiltonian Monte Carlo in High Dimensions
- Author
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Holmes, Susan, Rubinstein-Salzedo, Simon, and Seiler, Christof
- Subjects
Mathematics - Probability ,Mathematics - Differential Geometry ,Mathematics - Statistics Theory - Abstract
In this article, we analyze Hamiltonian Monte Carlo (HMC) by placing it in the setting of Riemannian geometry using the Jacobi metric, so that each step corresponds to a geodesic on a suitable Riemannian manifold. We then combine the notion of curvature of a Markov chain due to Joulin and Ollivier with the classical sectional curvature from Riemannian geometry to derive error bounds for HMC in important cases, where we have positive curvature. These cases include several classical distributions such as multivariate Gaussians, and also distributions arising in the study of Bayesian image registration. The theoretical development suggests the sectional curvature as a new diagnostic tool for convergence for certain Markov chains., Comment: Comments welcome
- Published
- 2014
44. Discussion of 'Geodesic Monte Carlo on Embedded Manifolds'
- Author
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Byrne, Simon, Girolami, Mark, Diaconis, Persi, Seiler, Christof, Holmes, Susan, Dryden, Ian L., Kent, John T., Pereyra, Marcelo, Shahbaba, Babak, Lan, Shiwei, Streets, Jeffrey, and Simpson, Daniel
- Subjects
Statistics - Computation - Abstract
Contributed discussion and rejoinder to "Geodesic Monte Carlo on Embedded Manifolds" (arXiv:1301.6064), Comment: Discussion of arXiv:1301.6064. To appear in the Scandinavian Journal of Statistics. 18 pages
- Published
- 2013
- Full Text
- View/download PDF
45. Waste Not, Want Not: Why Rarefying Microbiome Data is Inadmissible
- Author
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McMurdie, Paul J. and Holmes, Susan
- Subjects
Quantitative Biology - Quantitative Methods ,Quantitative Biology - Genomics ,Statistics - Applications ,62P10 - Abstract
The interpretation of count data originating from the current generation of DNA sequencing platforms requires special attention. In particular, the per-sample library sizes often vary by orders of magnitude from the same sequencing run, and the counts are overdispersed relative to a simple Poisson model These challenges can be addressed using an appropriate mixture model that simultaneously accounts for library size differences and biological variability. This approach is already well-characterized and implemented for RNA-Seq data in R packages such as edgeR and DESeq. We use statistical theory, extensive simulations, and empirical data to show that variance stabilizing normalization using a mixture model like the negative binomial is appropriate for microbiome count data. In simulations detecting differential abundance, normalization procedures based on a Gamma-Poisson mixture model provided systematic improvement in performance over crude proportions or rarefied counts -- both of which led to a high rate of false positives. In simulations evaluating clustering accuracy, we found that the rarefying procedure discarded samples that were nevertheless accurately clustered by alternative methods, and that the choice of minimum library size threshold was critical in some settings, but with an optimum that is unknown in practice. Techniques that use variance stabilizing transformations by modeling microbiome count data with a mixture distribution, such as those implemented in edgeR and DESeq, substantially improved upon techniques that attempt to normalize by rarefying or crude proportions. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq., Comment: 22 pages, 5 figures, 2 supplementary sections
- Published
- 2013
- Full Text
- View/download PDF
46. Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics
- Author
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Cao, Kim-Anh Lê, Abadi, Al J., Davis-Marcisak, Emily F., Hsu, Lauren, Arora, Arshi, Coullomb, Alexis, Deshpande, Atul, Feng, Yuzhou, Jeganathan, Pratheepa, Loth, Melanie, Meng, Chen, Mu, Wancen, Pancaldi, Vera, Sankaran, Kris, Righelli, Dario, Singh, Amrit, Sodicoff, Joshua S., Stein-O’Brien, Genevieve L., Subramanian, Ayshwarya, Welch, Joshua D., You, Yue, Argelaguet, Ricard, Carey, Vincent J., Dries, Ruben, Greene, Casey S., Holmes, Susan, Love, Michael I., Ritchie, Matthew E., Yuan, Guo-Cheng, Culhane, Aedin C., and Fertig, Elana
- Published
- 2021
- Full Text
- View/download PDF
47. Community-wide hackathons to identify central themes in single-cell multi-omics
- Author
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Lê Cao, Kim-Anh, Abadi, Al J., Davis-Marcisak, Emily F., Hsu, Lauren, Arora, Arshi, Coullomb, Alexis, Deshpande, Atul, Feng, Yuzhou, Jeganathan, Pratheepa, Loth, Melanie, Meng, Chen, Mu, Wancen, Pancaldi, Vera, Sankaran, Kris, Righelli, Dario, Singh, Amrit, Sodicoff, Joshua S., Stein-O’Brien, Genevieve L., Subramanian, Ayshwarya, Welch, Joshua D., You, Yue, Argelaguet, Ricard, Carey, Vincent J., Dries, Ruben, Greene, Casey S., Holmes, Susan, Love, Michael I., Ritchie, Matthew E., Yuan, Guo-Cheng, Culhane, Aedin C., and Fertig, Elana
- Published
- 2021
- Full Text
- View/download PDF
48. CytoGLMM: conditional differential analysis for flow and mass cytometry experiments
- Author
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Seiler, Christof, Ferreira, Anne-Maud, Kronstad, Lisa M., Simpson, Laura J., Le Gars, Mathieu, Vendrame, Elena, Blish, Catherine A., and Holmes, Susan
- Published
- 2021
- Full Text
- View/download PDF
49. Nuclear degradation dynamics in a nonapoptotic programmed cell death
- Author
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Yalonetskaya, Alla, Mondragon, Albert A., Hintze, Zackary J., Holmes, Susan, and McCall, Kimberly
- Published
- 2020
- Full Text
- View/download PDF
50. Measures of dependence between random vectors and tests of independence. Literature review
- Author
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Josse, Julie and Holmes, Susan
- Subjects
Statistics - Methodology ,62H20 - Abstract
Simple correlation coefficients between two variables have been generalized to measure association between two matrices in many ways. Coefficients such as the RV coefficient, the distance covariance (dCov) coefficient and kernel based coefficients have been adopted by different research communities. Scientists use these coefficients to test whether two random vectors are linked. If they are, it is important to uncover what patterns exist in these associations. We discuss the topic of measures of dependence between random vectors and tests of independence and show links between different approaches. We document some of the interesting rediscoveries and lack of interconnection between bodies of literature. After providing definitions of the coefficients and associated tests, we present the recent improvements that enhance their statistical properties and ease of interpretation. We summarize multi-table approaches and provide scenarii where the indices can provide useful summaries of heterogeneous multi-block data. We illustrate these different strategies on several examples of real data and suggest directions for future research., Comment: Incorporated new section on actual examples of data analyses
- Published
- 2013
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