Research Article: Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition

Date Published: July 1, 2019

Publisher: Public Library of Science

Author(s): Jaron Thompson, Renee Johansen, John Dunbar, Brian Munsky, Fuzhong Wu.


Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearson’s correlation coefficients of.636 and.676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.

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Microbial communities mediate essential functions in diverse ecosystems. While the microbiome controls many interesting macroscopic properties, elucidating the relationship between specific microbes and ecosystem functions remains a complex problem in ecology. Recent advances in DNA sequencing technology make it easy to acquire metagenomic data representing the taxonomic profile of bacteria and fungi in microbial communities. This opens the door to deciphering which components of the microbiome can drive changes in macroscopic properties. However, analysis of metagenomic microbial data poses several difficulties. The data are typically high dimensional (many taxa) with a small number of samples collected in each study. Additionally, sequencing results are noisy and yield sparse data sets [1].

Random forest and neural network regression models are examples of supervised machine learning algorithms. In contrast to unsupervised machine learning algorithms, these methods require a subset of the data called a training set to develop a mathematical relationship between features and target variables. A feature represents a model variable and the target is the variable the model predicts. For regression problems, the target variable is a continuous scalar, and for classification problems, the target is a discrete label. A sample is a single set of features paired with a target variable, which, in the context of the present case study, represents a bacterial community profile paired with DOC. To assess model performance, predicted target variables using features from a held-out set of test data are compared to known target variables. In this study, prediction performance is measured using Pearson’s correlation coefficient, which quantifies the linear correlation between predicted and true target variables, and for which a value of one indicates a perfect positive linear correlation. In general, our regression model assumes that targets and features are related to one another by
where x∈RM is a vector M features, y∈R is the corresponding true value of the target variable, M(θ,x) is some mathematical operation (or model) from RM to R, θ∈RNθ are model parameters, and ε is the prediction error.

Our feed forward neural network regression model was trained with 257 community samples to predict level of DOC (Fig 1A). Our model was tested with a held out set of 51 test samples which yielded a Pearson’s correlation coefficient of .636 between true and predicted DOC (Fig 1B) and a mean squared error of .565. The random forest regression model was trained and tested with identical sets of data used with the neural network model. Test results using the random forest regression model yielded a Pearson’s correlation coefficient of .676 (Fig 1D) and a mean squared error of .516. A scatter plot of the prediction error using the neural network model versus the prediction error with identical test samples using the the random forest model are positively correlated with a Pearson’s correlation coefficient of 0.781 (Fig 1E).

While random forest outperformed the neural network for prediction tasks in this study, both methods can be used to predict DOC entirely from microbial community profiles and to provide measures of feature importance. The random forest method is relatively easy to implement, and performs well with little adjustment to model hyper-parameters. Sensitivity analyses with the data set in this study (Fig 5) shows that the random forest model is less sensitive to sample size of the training data set, which makes random forest an attractive machine learning model for analysis of microbiome data. A benefit of the neural network model is that it provides more easily interpreted results for feature selection, which include the direction in which taxa affect environmental variables. The site correlations determined by the neural network and indicator taxa analysis show perfect agreement in sign among the entire set of taxa. Furthermore, because ground truth for which taxa drive changes in environmental variables is not known, the joint set of selected features from random forest, neural network, and indicator taxa approaches provides greater confidence than the set from one method alone (feature selection results are included in the supporting information S4 Dataset).




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