Microdiversity of the Vaginal Microbiome is Associated with Preterm Birth

Liao, Jingqiu; Shenhav, Liat; Serrano, Myrna G.; Zhu, Bin; Buck, Gregory A.; Korem, Tal

doi:10.1101/2023.01.13.523991

Cited by 9 publications

(10 citation statements)

References 101 publications

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“…(d-f) Results of applying TEMPTED to ASVs in the vaginal microbiome during pregnancy (141 term and 49 preterm) 4,5 . (g-i) Results of applying TEMPTED to MAGs in the vaginal microbiome during pregnancy (111 term and 35 preterm) 4,19 . (d , g) Subject loadings separating pregnancies resulting in term and preterm deliveries in Component 1.…”

Section: Resultsmentioning

confidence: 99%

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Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies

Shi

Martino

Han

et al. 2023

Preprint

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Complex dynamics of microbial communities underlie their essential roles in health and disease, but our understanding of these dynamics remains incomplete. To bridge this gap, longitudinal microbiome data are being rapidly generated, yet their power is limited by technical challenges in design and analysis, such as missing temporal samples, complex correlation structure, and high dimensionality. Here, we present TEMPoral TEnsor Decomposition (TEMPTED), the only time-informed dimensionality reduction method that extracts the underlying microbial dynamics while overcoming the statistical challenges posed by this type of data. TEMPTED facilitates beta-diversity analysis at both sample- and subject-level and enables the transfer of the learned low-dimensional representation from training data to unseen test data. In data-driven simulations, TEMPTED enables host phenotype classification at 90% accuracy compared to random using existing methods. In real data we show that TEMPTED deconvolutes the dynamics of the vaginal microbiome during pregnancy, allowing for the detection of microbial signatures associated with term and preterm births that are reproducible across datasets.

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Section: Resultsmentioning

confidence: 99%

“…Next, we applied TEMPTED to two independent studies that longitudinally sample the vaginal microbiome during pregnancy: one study sequenced shotgun metagenomics and used metagenome-assembled genomes (MAGs) 4,19 and while the second sequenced 16S rRNA genes and used ASVs 4,5 ( Fig. S4, S5 ).…”

Section: Resultsmentioning

confidence: 99%

Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies

Shi

Martino

Han

et al. 2023

Preprint

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“…The biological relevance of α diversity to the microbiome has been extremely well documented across a wide range of scenarios (e.g., refs. [13][14][15][16][17][18][19][20][21] ). Specifically, we reanalyzed data from a study of the vaginal microbiome and preterm birth, and examined the first time point from each of the 40 individuals included in the study 13 (Methods).…”

Section: Clr-transformed Sparse Features Can Be Associated With Clini...mentioning

confidence: 99%

Compositional transformations can reasonably introduce phenotype-associated values into sparse features

Austin,

Korem

2024

Preprint

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It was recently argued1that an analysis of tumor-associated microbiome data2is invalid because features that were originally very sparse (genera with mostly zero read counts) became associated with the phenotype following batch correction1. Here, we examine whether such an observation should necessarily indicate issues with processing or machine learning pipelines. We focus on the centered log ratio (CLR) transformation, which is often recommended for analysis of compositional microbiome data3. The CLR transformation has similarities to Voom-SNM4,5, the batch-correction method brought into question1,2, yet is a sample-wise operation that cannot, in itself, “leak” information or invalidate downstream analyses. We show that because the CLR transformation divides each value by the geometric mean of its sample, common imputation strategies for missing or zero values result in transformed features that are associated with the geometric mean. Through analyses of both synthetic and vaginal microbiome datasets we demonstrate that when the geometric mean is associated with a phenotype, sparse and CLR-transformed features will also become associated with it. We re-analyze features highlighted by Gihawi et al.1and demonstrate that the phenomena of sparse features becoming phenotype-associated can also be observed after a CLR transformation. While we do not intend to validate tumor-associated microbiome signatures2or evaluate other concerns regarding their detection and analysis1,6, we conclude that as phenotype-associated features that were initially sparse can be created by a sample-wise transformation that cannot artifactually inflate machine learning performance, their detection is not independently sufficient to demonstrate an analytic issue in machine learning pipelines. However, as was also previously noted by others, features transformed with sample-wise operations such as the CLR transformation should be interpreted with caution.

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“…For prediction models, external validation in an independent study is imperative for a robust assessment of the generalizability of the model to new populations 1,2 . The ability to train generalizable models across datasets also offers an opportunity for increased sample size and power in settings where data from many smaller studies is already available, such as studies of the vaginal microbiome in preterm birth [3][4][5][6][7][8] , or the gut microbiome in colorectal cancer 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

Austin,

Kav,

Park

et al. 2024

Preprint

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Every step in common microbiome profiling protocols has variable efficiency for each microbe. For example, different DNA extraction kits may have different efficiency for Gram-positive and -negative bacteria. These variable efficiencies, combined with technical variation, create strong processing biases, which impede the identification of signals that are reproducible across studies and the development of generalizable and biologically interpretable prediction models. "Batch-correction" methods have been used to alleviate these issues computationally with some success. However, many make strong parametric assumptions which do not necessarily apply to microbiome data or processing biases, or require the use of an outcome variable, which risks overfitting. Lastly and importantly, existing transformations used to correct microbiome data are largely non-interpretable, and could, for example, introduce values to features that were initially mostly zeros. Altogether, processing bias currently compromises our ability to glean robust and generalizable biological insights from microbiome data. Here, we present DEBIAS-M (Domain adaptation with phenotype Estimation and Batch Integration Across Studies of the Microbiome), an interpretable framework for inference and correction of processing bias, which facilitates domain adaptation in microbiome studies. DEBIAS-M learns bias-correction factors for each microbe in each batch that simultaneously minimize batch effects and maximize cross-study associations with phenotypes. Using benchmarks of HIV and colorectal cancer classification from gut microbiome data, and cervical neoplasia prediction from cervical microbiome data, we demonstrate that DEBIAS-M outperforms batch-correction methods commonly used in the field. Notably, we show that the inferred bias-correction factors are stable, interpretable, and strongly associated with specific experimental protocols. Overall, we show that DEBIAS-M allows for better modeling of microbiome data and identification of interpretable signals that are reproducible across studies.

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Microdiversity of the Vaginal Microbiome is Associated with Preterm Birth

Cited by 9 publications

References 101 publications

Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies

Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies

Compositional transformations can reasonably introduce phenotype-associated values into sparse features

Processing-bias correction with DEBIAS-M improves cross-study generalization of microbiome-based prediction models

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