A comprehensive evaluation of multicategory classification methods for microbiomic data
 Alexander Statnikov^{1, 2}Email author,
 Mikael Henaff^{1},
 Varun Narendra^{1},
 Kranti Konganti^{7},
 Zhiguo Li^{1},
 Liying Yang^{2},
 Zhiheng Pei^{2, 3, 5},
 Martin J Blaser^{2, 4, 6},
 Constantin F Aliferis^{1, 3, 8} and
 Alexander V Alekseyenko^{1, 2}Email author
DOI: 10.1186/20492618111
© Statnikov et al.; licensee BioMed Central Ltd. 2013
Received: 10 January 2013
Accepted: 13 March 2013
Published: 5 April 2013
Abstract
Background
Recent advances in nextgeneration DNA sequencing enable rapid highthroughput quantitation of microbial community composition in human samples, opening up a new field of microbiomics. One of the promises of this field is linking abundances of microbial taxa to phenotypic and physiological states, which can inform development of new diagnostic, personalized medicine, and forensic modalities. Prior research has demonstrated the feasibility of applying machine learning methods to perform body site and subject classification with microbiomic data. However, it is currently unknown which classifiers perform best among the many available alternatives for classification with microbiomic data.
Results
In this work, we performed a systematic comparison of 18 major classification methods, 5 feature selection methods, and 2 accuracy metrics using 8 datasets spanning 1,802 human samples and various classification tasks: body site and subject classification and diagnosis.
Conclusions
We found that random forests, support vector machines, kernel ridge regression, and Bayesian logistic regression with Laplace priors are the most effective machine learning techniques for performing accurate classification from these microbiomic data.
Keywords
Microbiomic data Machine learning Classification Feature selectionBackground
Advances in lowcost, highthroughput DNA sequencing technologies have enabled the studies of the composition of microbial communities at unprecedented throughput levels. Such studies are particularly interesting for biomedicine because for every human cell in the body there are about ten microbial cells in the gut alone [1]. These microbial symbionts contribute a metagenome to human biology and interact with the human host to perform a multitude of functions ranging from basic metabolism to immune system development. Therefore, it is conceivable that the study of microbial compositions will yield important clues in understanding, diagnosing, and treating diseases by inferring the contribution of each constituent of microbiota to various disease and physiological states.
A typical microbiomic study relies on a marker gene (or a group of markers) that can be used for the identification and quantitation of the microbes present in a given specimen. A good marker gene needs to have three essential properties: (i) it must be present in all of the microbes that we try to identify, (ii) its sequences should be conserved in members of the same species, and (iii) the interspecies difference in the gene sequence should be sufficiently significant to allow for taxonomical discrimination. The 16S rRNA gene is commonly used in microbiomic studies as a marker gene to generate human microbiota surveys. For every sample in a dataset, a human microbiota survey contains hundreds of thousands or millions of DNA sequences from the underlying microbial community. Abundances of operational taxonomic units (OTUs), extracted from the highthroughput sequencing data using upstream bioinformatic processing pipelines, can serve as input features for machine learning algorithms.
A necessary prerequisite for the creation of successful microbiomicsbased models is a solid understanding of the relative strengths and weaknesses of available machine learning and related statistical methods. Prior work by Knights et al. took an excellent first step in this direction and established the feasibility of creating accurate models for classification of body sites and subject identification [2]. The present work extends prior research by: (i) addressing diagnostic/personalized medicine applications in addition to classification of body sites and subjects, (ii) evaluating a large number of machine learning classification and feature/OTU selection methods, (iii) using more powerful multicategory classifiers based on a oneversusrest scheme [3, 4], (iv) measuring classification accuracy by a metric that is insensitive to prior distribution of classes, and (v) performing formal statistical comparison among classifiers. The present study thus allows determination of the classifiers that perform best for microbiomic data among the many available alternatives. It also allows identification of the best performing combinations of classification and feature/OTU selection algorithms across most microbiomic datasets.
We undertook a rigorous comparison of 18 major machine learning methods for multicategory classification, 5 feature/OTU selection methods, and 2 accuracy metrics using 8 datasets spanning 1,802 human samples and various classification tasks: body site and subject classification and diagnosis. We focused here on supervised classification methods because unsupervised methods (such as clustering and principal component analysis), which are designed to reveal structure of the data, provide visual summaries, and help quality control, are not optimal (and depending on application may also be completely inadvisable) for predictively linking the data to specific response variables/phenotypes [5, 6]. We found that random forests, support vector machines, kernel ridge regression, and Bayesian logistic regression with Laplace priors are the most effective machine learning techniques in performing accurate classification from microbiomic data.
Methods
Datasets and data preparatory steps
Characteristics of microbiomic datasets used in this study
Dataset  Number of samples  Number of features (OTUs)  Number of classes  Classification task and samples per class  Max. prior probability of a class (%) 

Costello Body Habitat (CBH)  552  6,979  6  Classify body habitats: Skin (357), Oral Cavity (46), External Auditory Canal (44), Hair (14), Nostril (46), Feces (45)  64.7 
Costello Subject (CS)  140  2,543  7  Classify 7 subjects by microbiota (20/20/20/20/20/20/20)  14.3 
Costello Skin Sites (CSS)  357  4,793  12  Classify skin sites: external nose (14), forehead (32), glans penis (8), labia minora (6), axilla (28), pinna (27), palm (64), palmar index finger (28), plantar foot (64), popliteal fossa (46), volar forearm (28), umbilicus (12)  17.9 
Fierer Subject (FS)  104  1,217  3  Classify 3 subjects by microbiota (40/33/31)  38.5 
Fierer Subject x Hand (FSH)  98  1,217  6  Classify by subject and left/right hand (20/18/17/14/16/13)  20.4 
Blaser Psoriasis (BP)  151  13,503  3  Classify as Control (49), Psoriasis Normal (51), Psoriasis Lesion (51)  33.8 
Pei Diagnosis (PDX)  200  74,018  4  Classify as Normal (28), Reflux Esophagitis (36), Barrett's Esophagus (84), Esophageal Adenocarcinoma (52)  42.0 
Pei Body Site (PBS)  200  74,018  4  Classify body site: Oral Cavity (51), Esophagus (51), Stomach (48), Stool (50)  25.5 
A major data preparatory step in the analysis of human microbiota gene surveys is the extraction of operational taxonomic units (OTUs) that serve as input features for machine learning algorithms. An OTU is a cluster of sequences of nonhuman origin that is constructed based on nucleotide similarity between the sequences. The necessity to use sequence similaritybased OTUs is motivated by two major considerations: (i) good reference databases may not be available for finegrained taxonomic classification of sequences, and (ii) sequencing errors introduced by the technologies are effectively controlled when sequences are aggregated into similaritybased clusters.
Parameter  Value  Description 

otu picking method  uclust  uclust, creates ‘seeds’ of sequences which generate clusters based on percent identity. 
clustering algorithm  furthest  Clustering algorithm for mothur otu picking method. Valid choices are: furthest, nearest, average. 
max cdhit memory  400  Maximum available memory to cdhitest (via the program’s M option) for cdhit OTU picking method (units of Mbyte) 
refseqs fp  None  Path to reference sequences to search against when using m blast, m uclust_ref, or m usearch_ref 
blast db  None  Preexisting database to blast against when using m blast 
similarity  0.97  Sequence similarity threshold (for cdhit, uclust, uclust_ref, or usearch) 
max e value  1.00E10  Max Evalue when clustering with BLAST 
prefix prefilter length  None  Prefilter data so seqs with identical first prefix_prefilter_length are automatically grouped into a single OTU 
trie prefilter  FALSE  Prefilter data so seqs which are identical prefixes of a longer seq are automatically grouped into a single OTU 
prefix length  50  Prefix length when using the prefix_suffix otu picker 
suffix length  50  Suffix length when using the prefix_suffix otu picker 
optimal uclust  FALSE  Pass the optimal flag to uclust for uclust otu picking. 
exact uclust  FALSE  Pass the exact flag to uclust for uclust otu picking. 
user sort  FALSE  Do not assume input is sorted by length 
suppress presort by abundance uclust  FALSE  Suppress presorting of sequences by abundance when picking OTUs with uclust or uclust_ref 
suppress new clusters  FALSE  Suppress creation of new clusters using seqs that don’t match reference when using m uclust_ref or m usearch_ref 
suppress uclust stable sort  FALSE  Do not pass stablesort to uclust 
max accepts  20  Max_accepts value to uclust and uclust_ref 
max rejects  500  Max_rejects value to uclust and uclust_ref 
word length  12  W value to usearch, uclust, and uclust_ref. Set to 64 for usearch. 
stepwords  20  Stepwords value to uclust and uclust_ref 
suppress uclust prefilter exact match  FALSE  Do not collapse exact matches before calling uclust 
In summary, we started with raw DNA sequencing data, removed human DNA sequences, defined OTUs over microbial sequences, and quantified relative abundance of all sequences that belong to each OTU. These relative abundances, further rescaled to the range [0, 1], served as input features for machine learning algorithms. The number of OTUs in each dataset is provided in Table 1. We emphasize that the OTUs were constructed without knowledge of classification labels and thus do not bias performance of machine learning techniques.
The OTU tables and sample labels for all datasets used in the study are provided in Additional files 1, 2, 3, 4, 5, 6, 7 and 8.
Machine learning algorithms for classification
We used 18 machine learning multicategory classification algorithms from the following seven algorithmic families: support vector machines, kernel ridge regression, regularized logistic regression, Bayesian logistic regression, random forests, knearest neighbors, and probabilistic neural networks. These machine learning methods were chosen because of their extensive and successful applications to many datasets from other genomic domains. Since all the classification tasks were multicategory (that is, with three or more classes) and most of the employed classifiers (except for random forests, knearest neighbors, and probabilistic neural networks) are designed for binary classification problems (that is, with two classes), we adopted a oneversusrest approach for the latter methods. Specifically, we trained separate binary classifiers for each class against the rest and then classified new samples by taking a vote of the binary classifiers and choosing the class with the ‘strongest’ vote. The oneversusrest approach for classification is known to be among the best performing methods for multicategory classification for other types of data, including microarray gene expression [3, 4]. Random forests, knearest neighbors, and probabilistic neural networks methods can solve multicategory problems natively and were applied directly.
Support vector machines (SVMs) are a class of machine learning algorithms that perform classification by separating the different classes in the data using a maximal margin hyperplane [13]. To learn nonlinear decision boundaries, SVMs implicitly map the data to a higher dimensional space by means of a kernel function, where a separating hyperplane is then sought. The superior empirical performance of SVMs in many types of highthroughput biomedical data can be explained by several theoretical reasons: for example, SVMs are robust to high variabletosample ratios and large numbers of features, they can efficiently learn complex classification functions, and they employ powerful regularization principles to avoid overfitting [3, 14]. Extensive literature on applications in text categorization, image recognition and other fields also show the excellent empirical performance of this classifier in many other domains. SVMs were used with linear kernel, polynomial kernel, and a radial basis function (RBF, also known as Gaussian) kernel.
Kernel ridge regression (KRR) adds the kernel trick to ridge regression. Ridge regression is linear regression with regularization by an L_{2} penalty. Kernel ridge regression and SVMs are similar in dealing with nonlinearity (by using the kernel trick) and model regularization (by using an L_{2} penalty, also called the ridge). The difference lies in the loss function: the SVMs use a hinge loss function, while ridge regression uses squared loss [15].
Regularized Logistic Regression adds regularization by an L_{1} or L_{2} penalty to the logistic regression (abbreviated as L1LR and L2LR, respectively) [16, 17]. Logistic regression is a learning method from the class of general linear models that learns a set of weights that can be used to predict the probability that a sample belongs to a given class [18]. The weights are learned by minimizing a loglikelihood loss function. The model is regularized by imposing an L_{1} or L_{2} penalty on the weight vector. An L_{2} penalty favors solutions with relatively small coefficients, but does not discard any features. An L_{1} penalty shrinks the weights more uniformly and can set weights to zero, effectively performing embedded feature selection.
Bayesian logistic regression (BLR) is another method from the class of general linear models that finds the maximum a posteriori estimate of the weight vector under either Gaussian or Laplace prior distributions, using a coordinate descent algorithm [19, 20]. Gaussian priors tend to favor dense weight vectors, whereas Laplace priors lead to sparser solutions; in this way they perform a similar purpose as imposing an L_{1} penalty on the coefficients.
Random forests (RF) is a classification algorithm that uses an ensemble of unpruned decision trees, each built on a bootstrap sample of the training data using a randomly selected subset of features [21]. The random forest algorithm possesses a number of appealing properties, making it wellsuited for classification of microbiomic data: (i) it is applicable when there are more predictors than observations; (ii) it performs embedded feature selection and it is relatively insensitive to the large number of irrelevant features; (iii) it incorporates interactions between predictors: (iv) it is based on the theory of ensemble learning that allows the algorithm to learn accurately both simple and complex classification functions; (v) it is applicable for both binary and multicategory classification tasks; and (vi) according to its inventors, it does not require much fine tuning of parameters and the default parameterization often leads to excellent classification accuracy [21].
Knearest neighbors (KNN) algorithm treats all objects as points in mdimensional space (where m is the number of features) and given an unseen object, the algorithm classifies it by a vote of K nearest training objects as determined by some distance metric, typically Euclidian distance [15, 22].
Probabilistic Neural Networks (PNN) belong to the family of Radial Basis Function (RBF) neural networks [22], and are composed of an input layer, a hidden layer consisting of a pattern layer and a competitive layer, and an output layer (see [23, 24]). The pattern layer contains one unit for each object in the training dataset. Given an unseen training object, each unit in the pattern layer computes a distance from this object to objects in the training set and applies a Gaussian density activation function. The competitive layer contains one unit for each classification category, and these units receive input only from pattern units that are associated with the classification category to which the training object belongs. Each unit in the competitive layer sums over the outputs of the pattern layer and computes a probability of the object belonging to a specific classification category. Finally, the output unit corresponding to the maximum of these probabilities outputs ‘1’, while those remaining output ‘0’.
Parameters and software implementations of the classification algorithms
Method  Parameter  Value  Software implementation 

SVM, Linear default  C (penalty parameter)  1  libsvm [25, 26] (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) 
SVM, Linear optimized  C (penalty parameter)  optimized over (0.01, 0.1, 1, 10, 100)  
SVM, Polynomial  C (penalty parameter)  optimized over (0.01, 0.1, 1, 10, 100)  
q (polynomial degree)  optimized over (1, 2, 3)  
SVM, RBF  C (penalty parameter)  optimized over (0.01, 0.1, 1, 10, 100)  
γ (determines RBF width)  optimized over (0.01, 0.1, 1, 10, 100)/number of variables  
KRR, Polynomial  λ (ridge)  optimized over (10^{10}, 10^{9}, …, 1)  
q (polynomial degree)  optimized over (1, 2, 3)  
KRR, RBF  λ (ridge)  optimized over (10^{10}, 10^{9}, …, 1)  
γ (determines RBF width)  optimized over (0.01, 0.1, 1, 10, 100)/number of variables  
KNN, default K = 1  K (number of neighbors)  1  Matlab Statistics Toolbox (http://www.mathworks.com) 
KNN, default K = 5  K (number of neighbors)  5  
KNN, optimized  K (number of neighbors)  optimized over (1, …, 50)  
PNN  σ (spread)  optimized over (0.01, 0.02, …, 1)  Matlab Neural Network Toolbox (http://www.mathworks.com) 
L2LR, default  C (penalty parameter)  1  liblinear [16, 17] (http://www.csie.ntu.edu.tw/~cjlin/liblinear/) 
L2LR, optimized  C (penalty parameter)  optimized over (0.01, 0.1, 1, 10, 100)  
L1LR, default  C (penalty parameter)  1  
L1LR, optimized  C (penalty parameter)  optimized over (0.01, 0.1, 1, 10, 100)  
BLR, Gaussian priors  v (variance)  automatically determined in the software by crossvalidation  
BLR, Laplace priors  v (variance)  automatically determined in the software by crossvalidation  
RF, default  ntree (number of trees)  500  R package randomForest (cran.rproject.org/) 
mtry (number of variables sampled at each split)  $\sqrt{\mathit{number}\phantom{\rule{0.5em}{0ex}}\mathit{of}\phantom{\rule{0.5em}{0ex}}\mathit{variables}}$  
RF, optimized  ntree (number of trees)  optimized over (500, 1000, 2000)  
mtry (number of variables sampled at each split)  $\mathit{optimized}\phantom{\rule{0.25em}{0ex}}\mathit{over}\phantom{\rule{0.25em}{0ex}}\left(0.5,1,2\right)\phantom{\rule{0.5em}{0ex}}\times \phantom{\rule{0.5em}{0ex}}\sqrt{\mathit{number}\phantom{\rule{0.25em}{0ex}}\mathit{of}\phantom{\rule{0.25em}{0ex}}\mathit{variables}}$ 
Parameters of machine learning classification algorithms
where x and y are samples with sequence abundances and q and γ are kernel parameters. Table 3 describes the parameter values for each classifier.
Model/parameter selection and accuracy estimation strategy
For model/parameter selection and accuracy estimation, we used nested repeated 10fold crossvalidation [29, 30]. The inner loop of crossvalidation was used to determine the best parameters of the classifier (that is, values of parameters yielding the best accuracy for the validation dataset). The outer loop of crossvalidation was used for estimating the accuracy of the classifier that was built using the previously found best parameters by testing with an independent set of samples. To account for variance in accuracy estimation, we repeated this entire process (nested 10fold crossvalidation) for 10 different splits of the data into 10 crossvalidation testing sets and averaged the results [29].
Feature/operational taxonomic unit (OTU) selection methods
We used the following feature selection techniques in an effort to improve classification accuracy, alleviate the ‘curse of dimensionality’ and improve interpretability by determining which OTUs were predictive of the different responses:

Random forestbased backward elimination procedure RFVS [31]: We applied the varSelRF implementation of the RFVS method (http://cran.rproject.org/web/packages/varSelRF) with the recommended parameters: ntree = 2000, mtryFactor = 1, nodesize = 1, fraction.dropped = 0.2 (a parameter denoting fraction of OTUs with low importance values to be discarded during the backward elimination procedure), and c.sd = 0 (a factor that multiplies the standard deviation of error for stopping iterations and choosing the best performing subset of OTUs). We refer to this method as ‘RFVS1.’

The RFVS procedure as described above, except that c.sd = 1 (denoted as ‘RFVS2’): This method differs from RFVS1 in that it performs statistical comparison to return the smallest subset of OTUs with classification accuracy that is not statistically distinguishable from the nominally best one.

The SVMbased recursive feature elimination method SVMRFE [32]: To be comparable with the RFVS method, we used the fraction of OTUs that are discarded in the iterative SVM models equal to 0.2. This variable selection method was optimized separately for the employed accuracy metrics. We used implementation of SVMRFE on top of the libSVM library [25, 26].

A backward elimination procedure based on univariate ranking of OTUs with KruskalWallis oneway nonparametric ANOVA [3] (denoted as ‘KW’): Similarly to SVMRFE and RFVS, we performed backward elimination by discarding 20% of the OTUs at each iteration. This variable selection method was optimized separately for the employed accuracy metrics. We used implementation of this variable selection procedure on top of the libSVM library [25, 26] and Matlab Statistics Toolbox.
We emphasize that all feature selection methods were applied during crossvalidation utilizing only the training data and splitting it into smaller training and validation sets, as necessary. This ensures integrity of the model accuracy estimation by protecting against overfitting.
Accuracy metrics
We used two classification accuracy metrics: the proportion of correct classifications (PCC) and relative classifier information (RCI). The first is easy to interpret and simplifies statistical testing, but is sensitive to the prior class probabilities and does not fully describe the actual difficulty of the classification problem. For example, in the CBH dataset where 357 out of 552 samples are drawn from the skin, a trivial classifier that would always predict the class of maximum prior probability would still obtain PCC = 0.647. On the other hand, a trivial classifier would achieve only PCC = 1/7 = 0.143 in the CS dataset where there are 20 samples from each of the 7 individuals that we want to classify.
The RCI metric is an entropybased measure that quantifies how much the uncertainty of the decision problem is reduced by the classifier, relative to classifying by simply using the prior probabilities of each class [33]. As such, it corrects for differences in prior probabilities of the diagnostic categories, as well as the number of categories.
The values of both metrics range from 0 to 1, where 0 indicates worst and 1 indicates best classification performance.
Statistical comparison among classifiers
To test whether the differences in accuracy between the nominally best method (that is, the one with the highest average accuracy) and all remaining algorithms are nonrandom, we need a statistical comparison of the observed differences in accuracies. We used random permutation testing, as described in [34]. For every algorithm X, other than the nominally best algorithm Y, we performed the following steps: (I) we defined the null hypothesis (H_{0}) to be algorithm X is as good as Y, that is, the accuracy of the best algorithm Y minus the accuracy of algorithm X is zero; (II) we obtained the permutation distribution of Δ_{ XY }, the estimator of the true unknown difference between accuracies of the two algorithms under the null hypothesis, by repeatedly swapping the accuracy measures of X and Y at random for each of the datasets and crossvalidation testing sets; (III) we computed the cumulative probability (P value) of Δ_{ XY } being greater than or equal to the observed difference ${\widehat{\Delta}}_{\mathit{XY}}$ over 10,000 permutations. This process was repeated for each of the 10 data splits, and the P values were averaged. If the resulting P value was smaller than 0.05, we rejected H_{0} and concluded that the data support that algorithm X is not as good as Y in terms of classification accuracy, and this difference is not due to sampling error. The procedure was run separately for PCC and RCI accuracy metrics.
Results and Discussion
Classification without feature/operational taxonomic unit (OTU) selection
Classification accuracy without feature/operational taxonomic unit (OTU) selection, measured by proportion of correct classifications (PCC)
Classifier  CBH  CS  CSS  FS  FSH  BP  PDX  PBS  Averages  P values 

SVM, Linear C = 1  0.920  0.911  0.583  0.940  0.598  0.354  0.468  0.695  0.684  0.022 
SVM, Linear optimized C  0.920  0.911  0.622  0.980  0.585  0.383  0.485  0.709  0.699  0.038 
SVM, Poly  0.920  0.911  0.622  0.980  0.585  0.383  0.484  0.709  0.699  0.036 
SVM, RBF  0.909  0.904  0.575  0.973  0.575  0.379  0.451  0.700  0.683  0.021 
KRR, Poly  0.913  0.918  0.581  0.954  0.598  0.377  0.482  0.709  0.692  0.027 
KRR, RBF  0.923  0.904  0.618  0.967  0.632  0.366  0.467  0.709  0.698  0.030 
KNN, K = 1  0.496  0.360  0.195  0.451  0.305  0.249  0.419  0.291  0.346  0.002 
KNN, K = 5  0.713  0.339  0.188  0.397  0.281  0.331  0.393  0.300  0.368  0.001 
KNN, optimized K  0.714  0.377  0.192  0.325  0.273  0.340  0.409  0.379  0.376  0.001 
PNN  0.743  0.321  0.216  0.522  0.332  0.325  0.167  0.247  0.359  0.000 
L2LR, C = 1  0.934  0.939  0.628  0.982  0.628  0.380  0.515  0.725  0.716  0.084* 
L2LR, optimized C  0.933  0.938  0.623  0.978  0.618  0.383  0.502  0.725  0.712  0.067* 
L1LR, C = 1  0.929  0.801  0.559  0.975  0.700  0.422  0.384  0.673  0.680  0.018* 
L1LR, optimized C  0.928  0.903  0.561  0.981  0.690  0.445  0.412  0.692  0.702  0.039 
RF, default  0.932  0.955  0.673  0.999  0.744  0.508  0.424  0.730  0.746  0.270* 
RF, optimized  0.938  0.956  0.689  0.994  0.760  0.523  0.423  0.735  0.752   
BLR, Laplace priors  0.927  0.927  0.634  0.962  0.622  0.387  0.452  0.727  0.705  0.042 
BLR, Gaussian priors  0.921  0.736  0.480  0.966  0.631  0.354  0.410  0.635  0.642  0.008 
Classification accuracy without feature/operational taxonomic unit (OTU) selection, measured by relative classifier information (RCI)
Classifier  CBH  CS  CSS  FS  FSH  BP  PDX  PBS  Averages  P values 

SVM, Linear C = 1  0.769  0.918  0.674  0.882  0.749  0.158  0.228  0.602  0.623  0.165* 
SVM, Linear optimized C  0.771  0.915  0.674  0.958  0.751  0.157  0.241  0.607  0.634  0.294* 
SVM, Poly  0.771  0.915  0.674  0.958  0.751  0.162  0.241  0.607  0.635  0.299* 
SVM, RBF  0.689  0.907  0.631  0.942  0.731  0.156  0.202  0.561  0.602  0.059* 
KRR, Poly  0.765  0.927  0.671  0.911  0.758  0.157  0.230  0.612  0.629  0.206* 
KRR, RBF  0.774  0.913  0.675  0.935  0.759  0.163  0.242  0.598  0.632  0.265* 
KNN, K = 1  0.344  0.329  0.377  0.163  0.355  0.167  0.074  0.078  0.236  0.003 
KNN, K = 5  0.178  0.359  0.277  0.102  0.203  0.056  0.092  0.062  0.166  0.002 
KNN, optimized K  0.337  0.402  0.354  0.028  0.207  0.089  0.122  0.196  0.217  0.003 
PNN  0.325  0.292  0.411  0.236  0.342  0.041  0.070  0.089  0.226  0.002 
L2LR, C = 1  0.772  0.941  0.670  0.964  0.778  0.161  0.236  0.628  0.644  0.575* 
L2LR, optimized C  0.782  0.939  0.680  0.958  0.778  0.163  0.228  0.624  0.644  0.626* 
L1LR, C = 1  0.769  0.825  0.635  0.949  0.779  0.163  0.191  0.565  0.610  0.089* 
L1LR, optimized C  0.798  0.910  0.664  0.960  0.790  0.174  0.209  0.599  0.638  0.439* 
RF, default  0.767  0.957  0.671  0.998  0.803  0.173  0.087  0.618  0.634  0.253* 
RF, optimized  0.784  0.962  0.681  0.994  0.805  0.225  0.098  0.625  0.647   
BLR, Laplace priors  0.759  0.932  0.679  0.922  0.770  0.166  0.090  0.619  0.617  0.085* 
BLR, Gaussian priors  0.744  0.759  0.496  0.930  0.736  0.077  0.014  0.529  0.536  0.008 
Notably, we obtain different results depending on which performance metric we use. In both cases, random forests with optimized parameters is the nominally best performing classifier. When using PCC as a performance metric, the nominally best performing classifier is statistically significantly better than all other classifiers except for L_{2}regularized logistic regression and random forests with default parameters. However, when using RCI as a performance metric, the only classifiers which are statistically significantly worse than the nominally best performing classifier are the KNNbased methods, PNN and BLR with Gaussian priors. Therefore, several classifiers which are significantly worse when using the somewhat naive PCC metric are comparable when using the more descriptive RCI metric (Figure 1a,b).
Classification with feature/operational taxonomic unit (OTU) selection
Classification accuracy with feature/operational taxonomic unit (OTU) selection, measured by proportion of correct classifications (PCC)
Classifier  Best FS Method  CBH  CS  CSS  FS  FSH  BP  PDX  PBS  Averages  P values 

SVM, Linear C = 1  SVMRFE  0.900  0.941  0.610  0.965  0.719  0.524  0.558  0.759  0.747  0.319* 
SVM, Linear optimized C  SVMRFE  0.952  0.935  0.631  0.985  0.754  0.534  0.553  0.761  0.763  0.535* 
SVM, Poly  SVMRFE  0.950  0.929  0.633  0.987  0.742  0.528  0.551  0.754  0.759  0.460* 
SVM, RBF  SVMRFE  0.941  0.918  0.617  0.987  0.693  0.518  0.523  0.727  0.741  0.179* 
KRR, Poly  KW  0.909  0.933  0.623  0.949  0.749  0.547  0.514  0.713  0.742  0.199* 
KRR, RBF  KW  0.929  0.939  0.634  0.970  0.737  0.537  0.504  0.714  0.745  0.248* 
KNN, K = 1  RFVS2  0.930  0.760  0.563  0.971  0.623  0.421  0.443  0.596  0.663  0.011 
KNN, K = 5  RFVS2  0.930  0.724  0.529  0.943  0.656  0.434  0.434  0.609  0.657  0.009 
KNN, optimized K  RFVS2  0.935  0.754  0.552  0.963  0.648  0.422  0.432  0.620  0.666  0.011 
PNN  RFVS2  0.906  0.781  0.560  0.956  0.623  0.130  0.449  0.604  0.626  0.006 
L2LR, C = 1  ALL  0.934  0.939  0.628  0.982  0.628  0.380  0.515  0.725  0.716  0.047 
L2LR, optimized C  KW  0.921  0.948  0.650  0.836  0.739  0.499  0.464  0.711  0.721  0.089* 
L1LR, C = 1  RFVS1  0.922  0.818  0.589  0.968  0.706  0.449  0.395  0.687  0.692  0.020 
L1LR, optimized C  RFVS1  0.934  0.909  0.611  0.993  0.710  0.442  0.418  0.697  0.714  0.048 
RF, default  RFVS1  0.954  0.950  0.704  0.991  0.745  0.550  0.479  0.746  0.765   
RF, optimized  RFVS1  0.954  0.950  0.695  0.996  0.746  0.548  0.479  0.741  0.764  0.498* 
BLR, Laplace priors  SVMRFE  0.946  0.929  0.639  0.991  0.759  0.521  0.537  0.739  0.758  0.465* 
BLR, Gaussian priors  KW  0.926  0.856  0.557  0.980  0.728  0.525  0.426  0.701  0.713  0.043 
Classification accuracy with feature/ operational taxonomic unit (OTU) selection, measured by relative classifier information (RCI)
Classifier  Best FS Method  CBH  CS  CSS  FS  FSH  BP  PDX  PBS  Averages  P values 

SVM, Linear C = 1  SVMRFE  0.719  0.952  0.691  0.929  0.813  0.334  0.337  0.674  0.681  0.191* 
SVM, Linear optimized C  SVMRFE  0.852  0.946  0.723  0.971  0.840  0.314  0.325  0.653  0.703   
SVM, Poly  SVMRFE  0.845  0.941  0.716  0.969  0.840  0.316  0.323  0.644  0.699  0.369* 
SVM, RBF  SVMRFE  0.813  0.925  0.683  0.972  0.813  0.286  0.290  0.611  0.674  0.089* 
KRR, Poly  SVMRFE  0.759  0.939  0.683  0.931  0.800  0.297  0.290  0.626  0.666  0.061* 
KRR, RBF  SVMRFE  0.807  0.935  0.687  0.944  0.801  0.297  0.316  0.633  0.677  0.097* 
KNN, K = 1  RFVS2  0.830  0.779  0.657  0.939  0.736  0.168  0.251  0.510  0.609  0.015 
KNN, K = 5  RFVS2  0.774  0.744  0.625  0.884  0.736  0.153  0.224  0.522  0.583  0.008 
KNN, optimized K  RFVS2  0.829  0.773  0.652  0.914  0.736  0.179  0.221  0.531  0.604  0.014 
PNN  RFVS2  0.726  0.798  0.629  0.907  0.730  0.167  0.227  0.516  0.587  0.012 
L2LR, C = 1  ALL  0.772  0.941  0.670  0.964  0.778  0.161  0.236  0.628  0.644  0.027 
L2LR, optimized C  SVMRFE  0.780  0.940  0.692  0.837  0.811  0.234  0.257  0.612  0.645  0.034 
L1LR, C = 1  RFVS1  0.742  0.836  0.642  0.934  0.771  0.183  0.213  0.584  0.613  0.011 
L1LR, optimized C  RFVS1  0.786  0.914  0.696  0.985  0.784  0.166  0.238  0.598  0.646  0.033 
RF, default  RFVS1  0.840  0.952  0.712  0.982  0.819  0.266  0.213  0.648  0.679  0.179* 
RF, optimized  RFVS1  0.842  0.956  0.714  0.994  0.810  0.264  0.216  0.649  0.681  0.196* 
BLR, Laplace priors  SVMRFE  0.822  0.932  0.692  0.982  0.824  0.317  0.318  0.640  0.691  0.313* 
BLR, Gaussian priors  RFVS2  0.761  0.855  0.625  0.968  0.770  0.208  0.202  0.570  0.620  0.018 
For many methods, there is a significant improvement using feature/OTU selection prior to performing classification. For both accuracy metrics, there is no statistically significant difference between the performance of SVMs, kernel ridge regression and random forests. The improvement in performance due to feature selection is especially pronounced in the case of KNN and PNN, which is consistent with the general understanding that these methods are sensitive to a large number of irrelevant features. However, KNN and PNN are still among the worst performing classifiers (Figure 1c,d).
Number of features/operational taxonomic units (OTUs) selected on average across ten data splits and ten crossvalidation training sets
Dataset  No OTU selection  Optimized for PCC  Optimized for RCI  

SVMRFE  KW  RFVS1  RFVS2  SVMRFE  KW  
CBH  6979  259  1191  20  50  285  1359 
CS  2543  469  370  215  805  474  370 
CSS  4793  896  935  51  211  1166  1262 
FS  1217  9  8  8  8  9  8 
FSH  1217  101  128  38  108  126  142 
BP  13503  453  416  37  204  1223  1276 
PDX  74018  5127  8308  136  492  8164  7400 
PBS  74018  2633  4347  89  687  4568  12188 
Conclusions
In this work, we conducted a thorough evaluation to identify the most accurate machine learning algorithms for multicategory classification from microbiomic data. We evaluated 18 algorithms for multicategory classification, 5 feature selection methods and 2 accuracy metrics on 8 different classification tasks with human microbiomic data. We found that for the most part, SVMs, random forests, kernel ridge regression, and Bayesian logistic regression with Laplace priors provided statistically similar levels of classification accuracy. On the other hand, we also found that Knearest neighbors and probabilistic neural networks were significantly outperformed by the other techniques.
The results of this work also highlight the large variation in difficulty across the different classification tasks. Tasks that involve classifying body sites, body habitats or subjects yield much higher accuracy rates than those which involve predicting the correct diagnosis, which are arguably more useful for reallife clinical applications. However, considering that the use of microbiomics for disease diagnosis has so far been relatively unexplored, the fact that we can still produce predictions that are better than random is encouraging.
The present results are relevant to the extent that the datasets employed are representative of the characteristics of microbiomic datasets in common use. We believe that we provided a dataset catalogue with broadly relevant characteristics. Of course, analysts when using the present benchmark comparison results to inform their analyses, should consider the degree of similarity of their datasets to the datasets in the study.
Finally, we mention that the results of this work may not be limited to microbiomic applications, and they might also apply to other similar classification tasks with nextgeneration DNA sequencing data. For example, classification with metagenomic surveys, in which the input features correspond to abundances of genes or gene families from different organisms, would be an interesting direction for future work.
Abbreviations
 BLR:

Bayesian logistic regression (machine learning method)
 KNN:

Knearest neighbors (machine learning method)
 KRR:

kernel ridge regression (machine learning method)
 L1LR:

regularized logistic regression by an L_{1} penalty (machine learning method)
 L2LR:

regularized logistic regression by an L_{2} penalty (machine learning method)
 OTU:

operational taxonomic unit
 PCC:

proportion of correct classifications (classification accuracy metric)
 PNN:

probabilistic neural networks (machine learning method)
 QIIME:

Quantitative Insights Into Microbial Ecology
 RCI:

relative classifier information (classification accuracy metric)
 RF:

random forests (machine learning method)
 SVM:

support vector machine (machine learning method)
Declarations
Acknowledgements
This research was supported in part by grants UH2 AR05750601S1 and UH3 CA140233 from the Human Microbiome Project and 1UL1 RR029893 from the National Center for Research Resources, National Institutes of Health, by the Diane Belfer Program in Human Microbial Ecology, and by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development. The authors acknowledge Efstratios Efstathiadis and Eric Peskin for providing access and support with high performance computing and Yingfei Ma for contribution to preparation of the PDX and PBS datasets. The authors also are grateful to Dan Knights and Rob Knight for contributing data from their study [2] and providing the technical details for reproducing their findings.
Authors’ Affiliations
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