Shrinkage improves estimation of microbial associations under different normalization methods

Badri, Michelle; Kurtz, Zachary; Bonneau, Richard; Müller, Christian L.

doi:10.1101/406264

Cited by 16 publications

(10 citation statements)

References 55 publications

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“…However, such data require spike-ins and are currently rarely available. Badri et al (2018) have investigated normalization strategies and their effect in correlation analysis but for a single time point, while Metwally et al (2018) proposed three normalization strategies that ignore the compositionality data problem. No method for longitudinal compositional data analysis has been proposed as yet.…”

Section: Discussionmentioning

confidence: 99%

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Bodein

Chapleur²,

Droit

et al. 2019

Front. Genet.

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Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

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Section: Discussionmentioning

confidence: 99%

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Bodein

Chapleur²,

Droit

et al. 2019

Front. Genet.

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“…As our experience with anaerobic digestion (see Appendix B) demonstrates, uGLAD can be successfully used as a tool for generating insight into growth dynamics of organisms in a digester and (hopefully) into domain structure in many other applications. Our algorithm works with any input, including: ASVs filtered by frequency, ASVs rolled up to higher taxonomy levels (species, genus, family), ASVs abundance normalized in various ways [2]. We use networkx package to visualize the graphs, presenting positive correlations in green and negative in red, with edge weights corresponding to the strength of the correlation.…”

Section: Discussionmentioning

confidence: 99%

uGLAD: Sparse graph recovery by optimizing deep unrolled networks

Shrivastava¹,

Chajewska²,

Abraham³

et al. 2022

Preprint

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Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models are used for domain exploration and structure discovery in poorly understood domains. This work introduces a novel technique to perform sparse graph recovery by optimizing deep unrolled networks. Assuming that the input data X ∈ R M ×D comes from an underlying multivariate Gaussian distribution, we apply a deep model on X that outputs the precision matrix Θ, which can also be interpreted as the adjacency matrix. Our model, uGLAD 1 , builds upon and extends the state-ofthe-art model GLAD [42] to the unsupervised setting. The key benefits of our model are (1) uGLAD automatically optimizes sparsity-related regularization parameters leading to better performance than existing algorithms. (2) We introduce multitask learning based 'consensus' strategy for robust handling of missing data in an unsupervised setting. We evaluate model results on synthetic Gaussian data, non-Gaussian data generated from Gene Regulatory Networks, and present a case study in anaerobic digestion.

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“…For ease of interpretability, we leverage the taxonomic tree information rather than phylogeny in our aggregation framework. To investigate potential human host-microbiome interactions, we re-analyze two human gut datasets, one cohort of HIV patients (Gut (HIV)), available in (Rivera-Pinto et al, 2018), comprising p = 539 OTUs and n = 152 samples, and the other a subset of the American Gut Project data (Gut (AGP)) (McDonald, 2018), provided in (Badri et al, 2020), comprising p = 1387 OTUs present in at least 10% of the n = 6266 samples. To study niche partitioning in terrestrial ecosystems, we use the Central Park soil dataset (Ramirez et al, 2014), as provided by Washburne et al (2017), which consists of p = 3379 OTUs and n = 580 samples with a wide range of soil property measurements.…”

Section: Data Collectionmentioning

confidence: 99%

Tree-Aggregated Predictive Modeling of Microbiome Data

Bien

Yan

Simpson

et al. 2020

Preprint

Self Cite

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

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Shrinkage improves estimation of microbial associations under different normalization methods

Cited by 16 publications

References 55 publications

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

uGLAD: Sparse graph recovery by optimizing deep unrolled networks

Tree-Aggregated Predictive Modeling of Microbiome Data

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