@article{grantham_reich_laber_pacifici_dunn_fierer_gebert_allwood_faith_2020, title={Global forensic geolocation with deep neural networks}, volume={69}, ISSN={["1467-9876"]}, DOI={10.1111/rssc.12427}, abstractNote={Summary}, number={4}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS}, author={Grantham, Neal S. and Reich, Brian J. and Laber, Eric B. and Pacifici, Krishna and Dunn, Robert R. and Fierer, Noah and Gebert, Matthew and Allwood, Julia S. and Faith, Seth A.}, year={2020}, month={Aug}, pages={909–929} }
 @article{grantham_guan_reich_borer_gross_2020, title={MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2019.1626242}, abstractNote={Abstract Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this article, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call microbiome mixed model (MIMIX). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2 × 2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of Andropogon gerardii, a perennial bunchgrass, as part of the global Nutrient Network research initiative. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.}, number={530}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Grantham, Neal S. and Guan, Yawen and Reich, Brian J. and Borer, Elizabeth T. and Gross, Kevin}, year={2020}, month={Apr}, pages={599–609} }
 @article{allwood_fierer_dunn_breen_reich_laber_clifton_grantham_faith_2020, title={Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance}, volume={310}, ISSN={["1872-6283"]}, DOI={10.1016/j.forsciint.2020.110250}, abstractNote={The use of environmental trace material to aid criminal investigations is an ongoing field of research within forensic science. The application of environmental material thus far has focused upon a variety of different objectives relevant to forensic biology, including sample provenance (also referred to as sample attribution). The capability to predict the provenance or origin of an environmental DNA sample would be an advantageous addition to the suite of investigative tools currently available. A metabarcoding approach is often used to predict sample provenance, through the extraction and comparison of the DNA signatures found within different environmental materials, such as the bacteria within soil or fungi within dust. Such approaches are combined with bioinformatics workflows and statistical modelling, often as part of large-scale study, with less emphasis on the investigation of the adaptation of these methods to a smaller scale method for forensic use. The present work was investigating a small-scale approach as an adaptation of a larger metabarcoding study to develop a model for global sample provenance using fungal DNA signatures collected from dust swabs. This adaptation was to facilitate a standardized method for consistent, reproducible sample treatment, including bioinformatics processing and final application of resulting data to the available prediction model. To investigate this small-scale method, 76 DNA samples were treated as anonymous test samples and analyzed using the standardized process to demonstrate and evaluate processing and customized sequence data analysis. This testing included samples originating from countries previously used to train the model, samples artificially mixed to represent multiple or mixed countries, as well as outgroup samples. Positive controls were also developed to monitor laboratory processing and bioinformatics analysis. Through this evaluation we were able to demonstrate that the samples could be processed and analyzed in a consistent manner, facilitated by a relatively user-friendly bioinformatic pipeline for sequence data analysis. Such investigation into standardized analyses and application of metabarcoding data is of key importance for the future use of applied microbiology in forensic science.}, journal={FORENSIC SCIENCE INTERNATIONAL}, author={Allwood, Julia S. and Fierer, Noah and Dunn, Robert R. and Breen, Matthew and Reich, Brian J. and Laber, Eric B. and Clifton, Jesse and Grantham, Neal S. and Faith, Seth A.}, year={2020}, month={May} }
 @article{grantham_reich_liu_chang_2018, title={Spatial regression with an informatively missing covariate: Application to mapping fine particulate matter}, volume={29}, number={4}, journal={Environmetrics}, author={Grantham, N. S. and Reich, B. J. and Liu, Y. and Chang, H. H.}, year={2018} }
 @article{grantham_reich_pacifici_laber_menninger_henley_barberán_leff_fierer_dunn_2015, title={Fungi Identify the Geographic Origin of Dust Samples}, volume={10}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0122605}, DOI={10.1371/journal.pone.0122605}, abstractNote={There is a long history of archaeologists and forensic scientists using pollen found in a dust sample to identify its geographic origin or history. Such palynological approaches have important limitations as they require time-consuming identification of pollen grains, a priori knowledge of plant species distributions, and a sufficient diversity of pollen types to permit spatial or temporal identification. We demonstrate an alternative approach based on DNA sequencing analyses of the fungal diversity found in dust samples. Using nearly 1,000 dust samples collected from across the continental U.S., our analyses identify up to 40,000 fungal taxa from these samples, many of which exhibit a high degree of geographic endemism. We develop a statistical learning algorithm via discriminant analysis that exploits this geographic endemicity in the fungal diversity to correctly identify samples to within a few hundred kilometers of their geographic origin with high probability. In addition, our statistical approach provides a measure of certainty for each prediction, in contrast with current palynology methods that are almost always based on expert opinion and devoid of statistical inference. Fungal taxa found in dust samples can therefore be used to identify the origin of that dust and, more importantly, we can quantify our degree of certainty that a sample originated in a particular place. This work opens up a new approach to forensic biology that could be used by scientists to identify the origin of dust or soil samples found on objects, clothing, or archaeological artifacts.}, number={4}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Grantham, Neal S. and Reich, Brian J. and Pacifici, Krishna and Laber, Eric B. and Menninger, Holly L. and Henley, Jessica B. and Barberán, Albert and Leff, Jonathan W. and Fierer, Noah and Dunn, Robert R.}, editor={Rokas, AntonisEditor}, year={2015}, month={Apr}, pages={e0122605} }