Fig. 4: Performance (Sparsity (Spa.), Sensibility (Sens.), Specificity (Spe.)) of the feature selection methods for providing a sparse and reliable subset of elements across our two scenarios.
From: A systematic benchmark of integrative strategies for microbiome-metabolome data
A Performance of univariate feature selection methods considering microorganisms as covariates across our three settings. Metabolites were log-transformed before running the methods. Performances were calculated on 100 replicates. For CODA-LASSO in the Konzo scenario, we adapted the simulation setting, selecting 300 species and 600 metabolites to accommodate running times of the method (See supplementary methods). B Performance of multivariate feature selection methods. Metabolites were log-transformed before running the methods. sPLS-Reg1 and sPLS-Reg2 correspond to the sPLS-Reg with X = microbiome and X = metabolome, respectively.
