Comparing regression-based approaches for identifying microbial functional groups

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., , , # 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 ...,