DOI: 10.5061/dryad.n8pk0p366
title: Data from: Comparing regression-based approaches for identifying microbial functional groups

description: Microbial communities are composed of functionally integrated taxa, and
 identifying which taxa contribute to a given ecosystem function is
 essential for predicting community behaviors. This study compares the
 effectiveness of a previously proposed method for identifying ``functional
 taxa,'' Ensemble Quotient Optimization (EQO), to a potentially
 simpler approach based on the Least Absolute Shrinkage and Selection
 Operator (LASSO). In contrast to LASSO, EQO uses a binary prior on
 coefficients, assuming uniform contribution strength across taxa. Using
 synthetic datasets with increasingly realistic structure, we demonstrate
 that EQO's strong prior enables it to perform better in low-data
 regime. However, LASSO’s flexibility and efficiency can make it preferable
 as data complexity increases. Our results detail the favorable conditions
 for EQO and emphasize LASSO as a viable alternative.
description: # Comparing regression-based approaches for identifying microbial
 functional groups
 [https://doi.org/10.5061/dryad.n8pk0p366](https://doi.org/10.5061/dryad.n8pk0p366) ## Description of the data and file structure The data was generated from the RUN_ME.m script in the attached code for plotting the figures shown in the article. ### Files and variables #### File: data\_figure2.mat **Description:** Data used to generate figure 2. ##### Variables * data: A data structure with fields: * LASSOMapA: A structure with field 'accuracies' -- a matrix representing the accuracy heatmap for LASSO under binary ground truth * EQOMapA: A structure with field 'accuracies' -- a matrix representing the accuracy heatmap for EQO under binary ground truth * LASSOMapB: A structure with field 'accuracies' -- a matrix representing the accuracy heatmap for LASSO under non-binary ground truth * EQOMapB: A structure with field 'accuracies' -- a matrix representing the accuracy heatmap for EQO under non-binary ground truth * accuracyGapChange: A structure with field 'accuracies' -- a matrix representing the heatmap of accuracy gap between EQO and LASSO under non-binary ground truth #### File: data\_figure3.mat **Description:** Data used to generate figure 3. ##### Variables * data: A data structure with fields: * varyFracReal: Accuracies of LASSO and EQO under varying fraction of real taxa * varyPhylo: Accuracies of LASSO and EQO under varying strength of phylo-dependency #### File: data\_figureS2.mat **Description:** Data used to generate figure S2. ##### Variables * data: A data structure with fields: * LASSO: LASSO accuracies under different noise level * EQO: EQO accuracies under different noise level #### File: data\_figureS3.mat **Description:** Data used to generate figure S3. ##### Variables * data: A data structure with fields: * LASSO: LASSO accuracies and std's under different cutoff values * EQO: EQO accuracies and std's under different cutoff values ## Code/software Import data into MATLAB to view it. Download scripts, navigate to the directory containing RUN_ME.m and run RUN_ME.m (code files under Zenodo) in MATLAB to generate the resulted figures (uploaded to Zenodo as supplemental files) from the computed data, or from scratch.