Disentangling microbial associations from hidden environmental and technical factors via latent graphical models

Zd, Kurtz; Bonneau, Richard; Müller, Cl.

doi:10.1101/2019.12.21.885889

Cited by 20 publications

(29 citation statements)

References 83 publications

(79 reference statements)

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“…Extending to cross-domain network analysis is thus an important future development goal. Likewise, environmental factors, such as chemical gradients and temperature, as well as batch effects are known to influence microbial abundances and composition and thus bias network estimation [ 117 , 122 , 123 ]. In the current version, we assume that the user has already corrected the microbiome data for these latent influences.…”

Section: Discussionmentioning

confidence: 99%

NetCoMi: network construction and comparison for microbiome data in R

Peschel

Müller

Mutius

et al. 2020

Briefings in Bioinformatics

254

205

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Motivation Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data. Results Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi’s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children’s rooms between samples from two study centers (Ulm and Munich). Availability R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi. Contact Tel:+49 89 3187 43258; stefanie.peschel@mail.de Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

NetCoMi: network construction and comparison for microbiome data in R

Peschel

Müller

Mutius

et al. 2020

Briefings in Bioinformatics

254

205

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“…However, transformations such as asinh and clr may be preferred since they are faster to compute than vst, while providing similar statistical properties. The resulting shrinkage correlation estimates can then also serve as input for more involved direct microbial network inference workflows that account for transitive correlations, adjust for additional covariates or model latent effects ( 14 , 39 , 50 , 51 ).…”

Section: Discussionmentioning

confidence: 99%

Shrinkage improves estimation of microbial associations under different normalization methods

Badri

Kurtz²,

Bonneau

et al. 2020

NAR Genomics and Bioinformatics

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Estimation of statistical associations in microbial genomic survey count data is fundamental to microbiome research. Experimental limitations, including count compositionality, low sample sizes and technical variability, obstruct standard application of association measures and require data normalization prior to statistical estimation. Here, we investigate the interplay between data normalization, microbial association estimation and available sample size by leveraging the large-scale American Gut Project (AGP) survey data. We analyze the statistical properties of two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis workflows and log-ratio transformations. We show that shrinkage estimation, a standard statistical regularization technique, can universally improve the quality of taxon–taxon association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as downstream data analysis examples, we show that variance-stabilizing and log-ratio approaches enable the most taxonomically and structurally coherent estimates. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

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“…However, transformations such as asinh and clr may be preferred since they are faster to compute than vst, while providing similar statistical properties. The resulting shrinkage correlation estimates can then also serve as input for more involved direct microbial network inference workflows b a that account for transitive correlations, adjust for additional covariates, or model latent effects (12,37,47,48).…”

Section: Discussionmentioning

confidence: 99%

Shrinkage improves estimation of microbial associations under different normalization methods

Badri

Kurtz²,

Bonneau

et al. 2018

Preprint

Self Cite

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Consistent estimation of associations in microbial genomic survey count data is fundamental to microbiome research. Technical limitations, including compositionality, low sample sizes, and technical variability, obstruct standard application of association measures and require data normalization prior to estimating associations. Here, we investigate the interplay between data normalization and microbial association estimation by a comprehensive analysis of statistical consistency. Leveraging the large sample size of the American Gut Project (AGP), we assess the consistency of the two prominent linear association estimators, correlation and proportionality, under di↵erent sample scenarios and data normalization schemes, including RNA-seq analysis work flows and log-ratio transformations. We show that shrinkage estimation, a standard technique in high-dimensional statistics, can universally improve the quality of association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as examples of exploratory data analysis, we show that variance-stabilizing and log-ratio approaches provide for the most consistent estimation of taxonomic and structural coherence. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

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Disentangling microbial associations from hidden environmental and technical factors via latent graphical models

Cited by 20 publications

References 83 publications

NetCoMi: network construction and comparison for microbiome data in R

NetCoMi: network construction and comparison for microbiome data in R

Shrinkage improves estimation of microbial associations under different normalization methods

Shrinkage improves estimation of microbial associations under different normalization methods

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