MIPMLP - Microbiome Pre-processing Machine Learning Pipeline.

Abstract

16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML. We checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification. We show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand alone version at https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac.il/Home ImportanceMicrobiome composition has been proposed as a biomarker (mic-marker) for multiple diseases. However, a clear analysis of the optimal way to represent the gene sequence counts is still lacking. We propose a simple and straight forward method that significantly improves the accuracy of mic-marker studies. This method can be of use to merge two of the most important advances in biology in the last decade: Microbiome analysis, and the introduction of machine learning methods to biological studies.