Paper reading (六十)：Multidomain analyses of a intestinal cleanout perturbation experiment

论文题目：Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment

scholar 引用：26

页数：29

发表时间：2017.08

发表刊物：PLOS computational biology

作者：Julia Fukuyama ,...,Susan P. Holmes

摘要：

Our work focuses on the stability, resilience, and response to perturbation of the bacterial communities in the human gut. Informative flash flood-like disturbances that eliminate most gastrointestinal biomass can be induced using a clinically-relevant iso-osmotic agent. We designed and executed such a disturbance in human volunteers using a dense longitudinal sampling scheme extending before and after induced diarrhea. This experiment has enabled a careful multidomain analysis of a controlled perturbation of the human gut microbiota with a new level of resolution. These new longitudinal multidomain data were analyzed using recently developed statistical methods that demonstrate improvements over current practices. By imposing sparsity constraints we have enhanced the interpretability of the analyses and by employing a new adaptive generalized principal components analysis, incorporated modulated phylogenetic information and enhanced interpretation through scoring of the portions of the tree most influenced by the perturbation. Our analyses leverage the taxa-sample duality in the data to show how the gut microbiota recovers following this perturbation. Through a holistic approach that integrates phylogenetic, metagenomic and abundance information, we elucidate patterns of taxonomic and functional change that characterize the community recovery process across individuals. We provide complete code and illustrations of new sparse statistical methods for high-dimensional, longitudinal multidomain data that provide greater interpretability than existing methods.

正文组织架构：

1. Introduction

2. Materials and methods

2.1 Ethics statement

2.2 Experimental design

2.3 Participants and sampling protocol

2.4 Sample processing and DNA extraction

2.5 16S rRNA gene sequencing

2.6 Incorporating phylogenetic information

3. Results

3.1 Between-subject variation

3.2 IIOD effect highlighted by adaptive gPCA

3.3 Diagnostics through resilience prediction

3.4 Data and code availability

4. Discussion

4.1 Immediate response to IIOD is a transient community shift followed by recovery of pre-perturbation state

4.2 Response to IIOD perturbation differs among RSVs: Bacteroides blooms while ruminococcus lags behind

4.3 Perturbation-associated GO functional terms include both directly-relevant and genomically-linked terms

4.4 Statistical regularization improves interpretability and facilitates multidomain analysis

4.5 Implications and limitations of the study results

5. Supporting information

正文部分内容摘录：

we pursued the following study aims:

1) determine whether and how quickly the gut microbiota demonstrates resilience after IIOD perturbation,

2) elucidate patterns of taxonomic and functional change that characterize the community recovery process,

3) develop statistical methods for the examination of multidomain data that provide greater biological interpretability than existing methods.

1. Biological Problem: What biological problems have been solved in this paper?

multidomain analysis

2. Main discoveries: What is the main discoveries in this paper?

Our analyses leverage the taxa-sample duality in the data to show how the gut microbiota recovers following this perturbation.
Through a holistic approach that integrates phylogenetic, metagenomic and abundance information, we elucidate patterns of taxonomic and functional change that characterize the community recovery process across individuals.
Immediate response to IIOD is a transient community shift followed by recovery of pre-perturbation state.
Response to IIOD perturbation differs among RSVs: Bacteroides blooms while ruminococcus lags behind
Statistical regularization improves interpretability and facilitates multidomain analysis
We characterized both the composition and functional potential of the gut community in eight individuals, analyzing the data with these new statistical methods and demonstrated improvements over current practice.

3. ML(Machine Learning) Methods: What are the ML methods applied in this paper?

principal components analysis
adaptive generalized principal components analysis (agPCA) and tree-based sparse linear discriminant analysis (LDA)
we have developed a new method which we call adaptive generalized PCA (gPCA)
After preliminary preprocessing, we have 419 samples and have measured 2611 RSVs and 2798 genes across these samples.

4. ML Advantages: Why are these ML methods better than the traditional methods in these biological problems?

incorporated modulated phylogenetic information and enhanced interpretation through scoring of the portions of the tree most influenced by the perturbation
Adaptive gPCA and tree-based discriminant analysis offer more flexible and interpretable incorporation of information regarding phylogenetic relatedness among observed RSVs than existing methods.
In contrast, adaptive gPCA allows more fine-tuned control of the extent to which phylogeny is reflected in the analysis and offers explanations of the ordination axes in terms of closely related RSVs
Adaptive gPCA defines a family of projections of the data which interpolate between DPCoA (which emphasizes structure at a coarse phylogenetic level) and PCA (which does not take into account the phylogeny), which is equivalent to considering all phylogenetic information to be contained at the finest taxonomic scale.
This family of projections corresponds to putting tree-structured priors of different strengths on the data, and the strength of this prior can be estimated from the data. In practice, this leads to low-dimensional representations of the data which are interpretable at a finer phylogenetic scale than those resulting from DPCoA.

5. Biological Significance: What is the biological significance of these ML methods’ results?

Bray-Curtis dissimilarity was computed between all possible sample pairs and MDS was used to obtain a low-dimensional representation of these dissimilarities.

6. Prospect: What are the potential applications of these machine learning methods in biological science?

We provide complete code and illustrations of new sparse statistical methods for high-dimensional, longitudinal multidomain data that provide greater interpretability than existing methods.

7. Mine Question(Optional)

adaptive generalized PCA (gPCA)

adaptive generalized principal components analysis (agPCA)

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