Skip to contents

Predomics - Interpretable machine learning for omics data

DALL-E3’s view of PredOmics
DALL-E3’s view of PredOmics

The predomics package offers access to an original Machine Learning framework implementing several heuristics that allow discovering sparse and interpretable models in large datasets. These models are efficient and adapted for classification and regression tasks in metagenomics and other datasets with commensurable variables. We introduce the custom BTR (BIN, TER, RATIO) languages that describe different types of associations between variables. Moreover, in the same framework we implemented several state-of-the-art methods (SOTA) including RF, ENET and SVM. The predomics package started in 2015 and has evolved quickly since. A major improvement came in 2023. The package comes also with predomicsapp, a R Shiny application for easy training and exploration of results.

Badges

R PackageLicense: GPL v3 GitHub issuesGitHub forks GitHub stars

Table of Contents

  1. Installation
  2. Usage
  3. Features
  4. Screenshots
  5. Technologies
  6. License
  7. Authors
  8. FAQs

Overview

We introduce here the predomics package, which is designed to search for simple and interpretable predictive models from omics data and more specifically metagenomics. These models, called BTR (for Bin/Ter/Ratio) are based on a novel family of languages designed to represent the microbial interactions in microbial ecosystems. Moreover, in this package we have proposed four different optimization heuristics that allow to discover some of the best predictive models. A model in predomics is a set of indexes from the dataset (i.e. variables) along with the respective coefficients belonging to the ternary set {-1, 0, 1} and an intercept of the form (A + B + C - K - L - M < intercept). The number of variables in a model, also known as model size, sparsity or parsimony, can vary in a range provided as a parameters to the experiment.

In predomics we have impemented the following types of object:

  • model, which can be tested with isModel(), is a list, which contains information on the features, the languages, the fitting scores etc.
  • population of models, which can be tested with isPopulation() is a list of model objects.
  • model collection, which can be tested with isModelCollection() is a list of population of models objects. They are grouped by model size.
  • classifier, which can be tested with isClf() is a set of parameters, which defines a learner ready to be run. This object will contain also all the information used during the training. This object is passed over to most of the functions for easy handling of parameters.
  • experiment, which can be tested with isExperiment() is a top level object, which contains a classifier object along with the learned models organized as a model collection object.

All these objects can be quickly viewed with the printy() function. Other existing functions allow conversion from one object type to the other as for instance modelCollectionToPopulation(). An experiment can be explored using the digest() routine along with many other functions implemented in more than 18K lines of code that compose this package.

Heuristics

In this package we have proposed four different heuristics to search for the best predictive models.

  • Terga1 and terga2. These algorithms are based on genetic algorithms. Here we introduce the notion of a population of models, which is a set of individuals/models that can be mutated, crossed, evolved and selected for many generations (epochs). The main difference between terga1 and terga2 is that the former will evolve individuals of a fixed model-size, while the latter can evolve models of different model-size in larger populations. There are also other differences in the core of these algorithms.
  • Terbeam. This algorithm consists of using a beam search approach. In computer science, beam search is a heuristic search algorithm that explores a graph by expanding a subset of the most promising nodes. Beam search is an optimization of best-first search that reduces its memory requirements. Here we use a window of a given size (a, b) for two consecutive model-size (k, k+1). The best models of model-size k are used to generate combinations of model-size k+1 and the best ones are kept for the next round. The size of the windows is fixed in parameters at the beginning of the experiment. The results can be also considered as a population of models. This method shows the best performance so far for a given budget of time.
  • Terda. This algorithm is based on a standard logistic regression approach. After learning a linear model with real-valued weights with the GlmNet package, these weights are rounded to either {-1, 0, 1}.
  • Metal. The four algorithms presented above complement each other in terms of model space epxloration. For instance, terbeam is specialized in small models, terda in bigger ones and terga will make sure to explore random combinations that are not only composed of features which in smaller-sparisty settings yield good results. We devised another algorithm named metal (fusion), which is a meta-heuristic based on the terga2 engine. The initial population is seeded by sets of models found by either terbeam, terda or terga2 and is next combined and evolved together. Metal has also the capacity to evolve models with multiple languages in the same time. This is particularly of interest when we wish to discover automatically the set of rules that predicts best the studied condition.

Predomics languages

A predomics model is coded in R as a S3 object, which contains a certain number of attributes among which the learner (algorithm) that generated it but also the language that is used. The languages we have proposed in the current version are the following.

  • Bin/bininter. Let us take the following exemple (A + B + C < intercept). In a bin/bininter language we have only two coefficients from the binary set {0, 1}. Features that do not appear in the model have the coefficient 0, while the others have the coefficient 1. The difference between bin and bininter is that the intercept for the former is set to zero.
  • Ter/teriter. If we take the example above (A + B + C - K - L - M < intercept) we can see that in this model of size k=6, we have coefficients from the ternary set {-1, 0, 1}. Features that do not appear in the model have the coefficient 0 while the others have 1 or -1. Models that have only positive or only negative coefficients are not considered as they would be bin models. The difference between ter and terinter is that the intercept for the former is set to zero.
  • Ratio. In the ratio language the intercept plays the role of a multiplication factor and the model is the form (A+B+C)/(K+L+M) < intercept.
  • State of the art (SOTA). We have implemnted in predomics three state-of-the-art algorithms used today in the field of metagenomics, random forest (rf), support vector machines (svmlin, svmrad), respectively using a linear and a radial kernel and finally the Elastic-Net Regularized Generalized Linear Models (glmnet). Most of the parameteres of these algorithms are left by default and some are optimized using internal cross-validation techniques.

Contact

If you have any questions or feedback, please contact us at: