Rare microbes from diverse Earth biomes dominate community activity

Sachdeva, Rohan; Campbell, Barbara J.; Heidelberg, John F.

doi:10.1101/636373

Cited by 7 publications

(8 citation statements)

References 61 publications

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“…Dirichlet-multinomial modelling (DMM) an approach that can be applied efficiently in many settings. Specifically, we report that DMM is much more sensitive than the competing approaches we exam- metabolites (Lynch & Neufeld, 2015;Sachdeva et al, 2019). Indeed, for some data, DMM identified many times more true positives than certain competing methods (up to approximately eight times more in extreme cases; Figure 3).…”

Section: Discussionmentioning

confidence: 85%

“…Through analysis of simulated data spanning the variation in attributes expected across many scientific domains, we report that new computational statistical techniques have made Dirichlet‐multinomial modelling (DMM) an approach that can be applied efficiently in many settings. Specifically, we report that DMM is much more sensitive than the competing approaches we examined, making DMM particularly well suited to identification of subtle shifts in relative abundance among features, such as what might be required in the study of rare, but consequential, microbes or metabolites (Lynch & Neufeld, ; Sachdeva et al, ). Indeed, for some data, DMM identified many times more true positives than certain competing methods (up to approximately eight times more in extreme cases; Figure ).…”

Section: Discussionmentioning

confidence: 99%

“…replication, number of observations, and so on; Figure ). Notably, included in simulated data, were those emulating the results of high‐throughput sequencing of microbial assemblages, as these are analytically challenging due to their dimensionality, high among‐replicate variation, and extreme rank‐abundance skew—often several microbial taxa are orders of magnitude more abundant than the numerous marginal taxa that typically compose the bulk of biodiversity within a sample (e.g., see Lynch & Neufeld, ; Sachdeva, Campbell, & Heidelberg, ). Our primary analytical goal was to measure the sensitivity and accuracy of DMM for comparing feature relative abundance between compositions and to compare the performance of DMM with competing approaches.…”

Section: Introductionmentioning

confidence: 99%

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Dirichlet‐multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Harrison

Calder

Shastry

et al. 2020

Molecular Ecology Resources

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Molecular ecology regularly requires the analysis of count data that reflect the relative abundance of features of a composition (e.g., taxa in a community, gene transcripts in a tissue). The sampling process that generates these data can be modelled using the multinomial distribution. Replicate multinomial samples inform the relative abundances of features in an underlying Dirichlet distribution. These distributions together form a hierarchical model for relative abundances among replicates and sampling groups. This type of Dirichlet‐multinomial modelling (DMM) has been described previously, but its benefits and limitations are largely untested. With simulated data, we quantified the ability of DMM to detect differences in proportions between treatment and control groups, and compared the efficacy of three computational methods to implement DMM—Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo. We report that DMM was better able to detect shifts in relative abundances than analogous analytical tools, while identifying an acceptably low number of false positives. Among methods for implementing DMM, HMC provided the most accurate estimates of relative abundances, and VI was the most computationally efficient. The sensitivity of DMM was exemplified through analysis of previously published data describing lung microbiomes. We report that DMM identified several potentially pathogenic, bacterial taxa as more abundant in the lungs of children who aspirated foreign material during swallowing; these differences went undetected with different statistical approaches. Our results suggest that DMM has strong potential as a statistical method to guide inference in molecular ecology.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dirichlet‐multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Harrison

Calder

Shastry

et al. 2020

Molecular Ecology Resources

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“…Microbes are the most widespread microscopic organisms and affect many key biological processes including metabolic function and immune function ( Qu et al, 2019 ; Sachdeva et al, 2019 ). There are many microbes in the human tissues, for example, skin ( Fredricks, 2001 ), gut ( Grenham et al, 2011 ), and lung ( Cole, 1989 ).…”

Section: Introductionmentioning

confidence: 99%

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

Chen

Sun

et al. 2021

Front. Microbiol.

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Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.

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“…Microbes are the most abundant microscopic organisms on Earth and control many major biological and chemical processes (Ley et al, 2006;Qu J. et al, 2019;Sachdeva et al, 2019). Normal microbial flora are beneficial for the host health (McFarland, 2000;Langella and Martín, 2019;Qu J. et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

et al. 2020

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Microbes with abnormal levels have important impacts on the formation and development of various complex diseases. Identifying possible Microbe-Disease Associations (MDAs) helps to understand the mechanisms of complex diseases. However, experimental methods for MDA identification are costly and time-consuming. In this study, a new computational model, RNMFMDA, was developed to find possible MDAs. RNMFMDA contains two main processes. First, Reliable Negative MDA samples were selected based on Positive-Unlabeled (PU) learning and random walk with restart on the heterogeneous microbe-disease network. Second, Logistic Matrix Factorization with Neighborhood Regularization (LMFNR) was developed to compute the association probabilities for all microbe-disease pairs. To evaluate the performance of the proposed RNMFMDA method, we compared RNMFMDA with five state-of-the-art MDA prediction methods based on five-fold cross-validations on microbes, diseases, and MDAs. As a result, RNMFMDA obtained the best AUCs of 0.6332, 0.8669, and 0.9081, respectively for the three five-fold cross validations, significantly outperforming other models. The promising prediction performance may be attributed to the following three features: highly quality negative MDA sample selection, LMFNR-based MDA prediction model, and various biological information integration. In addition, a few predicted microbe-disease pairs with high association scores are worthy of further experimental validation.

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Rare microbes from diverse Earth biomes dominate community activity

Cited by 7 publications

References 61 publications

Dirichlet‐multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Dirichlet‐multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

RNMFMDA: A Microbe-Disease Association Identification Method Based on Reliable Negative Sample Selection and Logistic Matrix Factorization With Neighborhood Regularization

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