Data from: 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.