MBio. The UC dataset from Nielsen et al. As expected, the incorrect feature selection led to inflated performance estimates in cross-validation but lower generalization to an external dataset, whereas the correct procedure gave a better estimate of the performance in the external test set; the fewer features were selected, the more the performance in the external datasets dropped (see Fig. (B) Subgraph containing the largest Proteobacteria module found in consensus network. The resilience of module i is defined as the median of \(\{\frac{d_1}{r_i}, \frac{d_2}{r_i}, \dots , \frac{d_K}{r_i}\}\), where K is the number of diseased networks (\(K = 9\) in this paper). To avoid this, dependent measurements need to be blocked during cross-validation, ensuring that measurements of the same individual are assigned to the same test set. Together, this may suggest that the enrichment of Proteobacteria edges observed in disease networks are contributed by rare disease-specific edges, and provide greater interconnectivity between Proteobacteria containing edges that would be otherwise be considered loosely connected when compared to the healthy network. SIAMCAT reproduces the results of previous machine learning meta-analyses. To correctly estimate the generalization accuracy across subjects, repeated measurements need to be blocked, all of them either into the training or test set. PLoS One. HARMONIES: A hybrid approach for microbiome networks inference via exploiting sparsity. 2013;2:e01202. 31(1), 69 (2015). Alterations in intestinal microbiota correlate with susceptibility to type 1 diabetes. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 2016;32:25203 academic.oup.com. In contrast, species from other genera, for example, Lactobacillus, Bacteroides, or Fusobacteria, appear predictive of several diseases, although species and subspecies belonging to these vary in terms of their disease specificity (Additional file 1: Figure S15). Disease-specific classifiers would also be of clinical relevance when applied to a general population: due to large differences in disease prevalence, a model for CRC (a condition with low prevalence) misclassifying many type 2 diabetes (T2D) patients (high prevalence) would in the general population detect many more (false) T2D cases than true CRC cases, and thus have very low precision. 2016;11:e0155362journals.plos.org. Meta-analyses also represent a powerful and efficient approach to leverage existing scientific data to both reaffirm existing findings and generate new hypotheses . . 4). Wang Q, Garrity GM, Tiedje JM, Cole JR. The guideline aims to be a user-friendly . Stat. 2015;3:7. As such, alternative methods of normalization or transformation of raw abundance values remain necessary to compare species co-abundances across samples of varying sequencing depths. Compositionally-aware methods can be further sub-categorized into correlation-based methods (e.g. Absolute model weights are shown as a dot plot on top, grouped by genus (including only genera with unambiguous NCBI taxonomy annotation). Although the analyses presented here are focused on human gut metagenomic datasets with disease prediction tasks, SIAMCAT is not restricted to these. Gastroenterology. 2019;8:e46923. Quantitative PCR assays for mouse enteric flora reveal strain-dependent differences in composition that are influenced by the microenvironment. Additionally, low read count samples that were less than 1M reads were discarded from this analysis to prevent inclusion of under-sampled genomes. Metagenomic analysis of gut microbial communities from a Central Asian population. 2014;15:38292 Elsevier. To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Hawinkel S, Mattiello F, Bijnens L, Thas O. Available from: https://github.com/zellerlab/siamcat (2021). In short, the function uses the normalization parameters of the discovery dataset to normalize the external data in a comparable way and then makes predictions by averaging the results of the application of all models of the repeated cross-validation folds to the normalized external data. Influence of feature selection cutoff and normalization method on classification accuracy. Taxonomic profiles generated using the RDP classifier [83] on the basis of 16S rRNA gene sequencing data were downloaded from a recent meta-analysis by Duvallet et al. 11(1), 116 (2020). Nature. If these dependent samples are randomly split in a standard cross-validation procedure, so that some could end up in the training set and others in the test set, it is effectively estimated how well the model generalizes across time points (from the same individual) rather than across individuals. CAS J.W. Code for Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox. Faust, K. et al. The label defining the sample groups for comparison is then derived from a user-specified meta-variable or an additional vector. Google Scholar. As many biological and technical factors beyond the primary phenotype of interest can influence microbiome composition [1], microbiome association studies are often at a high risk of confounding, which can lead to spurious results [11, 77,78,79]. Effects of rare microbiome taxa filtering on statistical analysis. In our exploration of more than 7000 different parameter combinations per classification task (see the Methods section), we found the Elastic Net logistic regression algorithm to yield the highest cross-validation accuracies on average, albeit requiring the input data to be appropriately normalized (see Fig. f Visualization of the main selected model weights (predictors corresponding to mOTUs, see the Methods section for the definition of cutoffs) by genus and disease. Gut. We proposed a resilience score to approximate the tendency of modules of gut bacterial species detected from the healthy microbiome network to remain in the same community in the gut microbiome associated with different diseases. iMeta, e13 (2022). While some datasets contained both UC and CD patients [5, 27, 30], other datasets contained only CD cases [28, 29]. PLoS Comput. Connor, N., Barbern, A. Evol. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. GZ., SS., and P.B. Le Goallec A, Tierney BT, Luber JM, Cofer EM, Kostic AD, Patel CJ. 2c) directly quantified the effect of confounding by country on the disease-association statistic. Zenodo. 2015;211:1927 academic.oup.com. PLoS One. Assume \(\frac{d_j}{r_i}\) is 0.80,0.90,0.60,0.70,0.85,0.75,0.90,0.35,0.40 for \(j=1, \dots , K\), respectively, module i has a resilience score of 0.75 (the median). Cell. Microbiome. 4d and the Methods section). In many cases, modules of high resilience werepopulated by members of the microbiota within the same clade. Nature. Diabetes. Salazar G, Paoli L, Alberti A, Huerta-Cepas J, Ruscheweyh H-J, Cuenca M, et al. Human genetics shape the gut microbiome. 3d). Microbiome association network, colored by module resilience. The human gut is colonised by a complex microbial ecosystem, collectively called the gut microbiota, which plays a pivotal role in key biological processes such as metabolic interactions and host immune responses. MATH A predictive index for health status using species-level gut microbiome profiling. By hyperparameter, we mean configuration parameters of the workflow, such as normalization parameters, tuning parameters controlling regularization strength, or properties of the external feature selection procedure in contrast to model parameters fitted during the actual training of the ML algorithms. Mass Medical Soc; 2016;375:23692379. Available from: https://github.com/zellerlab/siamcat_paper (2021). Future efforts in development of experimental and computational methods are necessary to address issues of microbiome compositionality. Lianmin Chen, Valerie Collij, Jingyuan Fu, Bhusan K. Kuntal, Pranjal Chandrakar, Sharmila S. Mande, Roktaek Lim, Josephine Jill T. Cabatbat, Pan-Jun Kim, Braden T. Tierney, Yingxuan Tan, Chirag J. Patel, Musfiqur Sazal, Vitalii Stebliankin, Giri Narasimhan, Zhanshan (Sam) Ma, Lianwei Li & Nicholas J. Gotelli, Efrat Muller, Yadid M. Algavi & Elhanan Borenstein, Scientific Reports Periodontology 2000 83(1), 1425 (2020). Qin, J. et al. However, we found that ML models have substantial problems with type I error control (>2-fold increase in FPR) and disease specificity (>2.5-fold elevated FPR) when naively transferred across studies. In contrast, the correct procedure implemented in SIAMCAT excludes the data in the test fold when calculating single-feature AUROC values; instead, AUROC values are calculated on the training fold only. iNAP: An integrated network analysis pipeline for microbiome studies. Article Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. Nature. A guide to enterotypes across the human body: Meta-analysis of microbial community structures in human microbiome datasets. As examples of disease-specific markers, Parvimonas spp. Biol. Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, et al. However, all such ML meta-analyses are limited by biological and clinical differences between studies [91], which will have to be addressed by better reporting standards [100]. All taxonomic and functional profiles used as input for the presented analyses are available in a Zenodo repository (see either https://doi.org/10.5281/zenodo.4454489 [110]), and the code to reproduce the analysis can be found in the dedicated GitHub repository (https://github.com/zellerlab/siamcat_paper [111]). The first step in microbiome research is to understand the advantages and limitations of specific HTS methods. 1. PLoS One. Microbiol. Google Scholar. For external validation testing, we completely removed repeated measurements in order not to bias the estimation of classification accuracy. MATH 2019;364:11335 science.sciencemag.org. Gupta et al. To determine the optimal AUROC across input types (shown in Fig. To address them, we introduce the control augmentation strategy, which greatly improved the cross-study portability of ML models. 2018;15:9628 nature.com. In conjunction with utilizing a compositionally aware correlation method, we employ various pre-processing steps to help mitigate challenges commonly associated with metagenomic correlation-based analyses. a Incorrectly setup machine learning workflows can lead to overoptimistic accuracy estimates (overfitting): the first issue arises from a naive combination of feature selection on the whole dataset and subsequent cross-validation on the very same data [80]. Non-invasive mapping of the gastrointestinal microbiota identifies children with inflammatory bowel disease. Linking the human gut microbiome to inflammatory cytokine production capacity. We found some disease-enriched predictors to be very specific for a single disease, such as Veillonella spp. Observations of significant differences in alpha diversity measures between healthy and diseased datasets are in line with previous studies that have used alpha-diversity measures as an indicator of disease-associated microbiome dysbiosis49,50. 1994;309:13515 bmj.com. Then, the features with the highest AUROC values were selected for model training (number depending on the cutoff). Cell Host Microbe. Nat Med. While several statistical analysis tools have been developed specifically for microbiome data, they are generally limited to testing for differential abundance of microbial taxa between groups of samples and do not allow users to evaluate their predictivity as they do not comprise full ML workflows for biomarker discovery [14,15,16]. processed the data and preformed all analysis. Google Scholar. Schubert AM, Rogers MAM, Ring C, Mogle J, Petrosino JP, Young VB, et al. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. and Y.Y. In particular, as there often lacks widely accepted reference standard and adopted protocol, methods and techniques utilized to analyze microbiome data is widely left open to interpretation and researchers can only inform themselves of the nuances between methods and select the method that best fits their data, needs, and available resources. Google Scholar. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Yu J, Zhao L, Zhao R, Long X, Coker OO, Sung JJY. Fan, Y. By focusing our analysis on microbial networks, we show that microbial interactions can extend approaches to stratify between microbiome associated disease phenotypes beyond differential abundances. Proton pump inhibitors alter the composition of the gut microbiota. Previous studies that applied ML to microbiome data [17,18,19,20] have compared and discussed the performance of several learning algorithms. and grant no. d When dependent observations (here by sampling the same individuals at multiple time points) are randomly assigned to cross-validation partitions, effectively the ability of the model to generalize across time points, but not across subjects, is assessed. G.Z. 1c, e). J.W. Microbiome. Gut. Gut. ABSTRACT Compositional and functional alterations to the gut microbiota during aging are hypothesized to potentially impact our health. Get the most important science stories of the day, free in your inbox. Google Scholar. 1 for the definition of boxplots). Epidemiol. 2008(10), P10008 (2008). A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type. McMurdie, P. J. 2018;24:107080. For each dataset, we determined the distances between all pairs of samples within a class as well as all pairs of samples between classes and then calculated an AUROC value based on these two distributions. Zenodo. Co-occurrence networks were constructed for each phenotype, and community modules in each network were identified utilizing the Leiden algorithm. 12(1), 112 (2021). supervised the work.
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