Fig 1.
Classification accuracies are robust to degradation from species-level relative abundance to presence/absence profiles in shotgun datasets.
Results obtained on 25 case-control studies for host phenotype classification from human microbiomes. (A) Number of case and control samples across the different studies. (B) AUC and (C) AUPRC scores using RF as back-end classifiers on species-level taxonomic profiles. Comparison between relative abundance (in blue) and presence/absence (in red) profiles highlighted negligible differences and no statistical differences in none of the studies (see S1 Fig for AUC scores and S2 Table for p-values). Metrics of comparison in terms of AUC, AUPRC, precision, recall, and F1 are summarized in S2 Fig and S2 Table is represented a comparison between AUC and AUPCR scores. (D) Number of statistically significant taxa from relative abundance (in blue) and presence/absence (in red) profiles.
Table 1.
Summary of the 25 classification tasks derived from metagenomic datasets for case-control prediction.
ACDV: Atherosclerotic cardiovascular disease, AD: Alzheimer’s disease, BD: Behcet’s disease, CRC: Colorectal cancer, IBD: irritable bowel disease, T1D: Type 1 diabetes, T2D: Type 2 diabetes. We additionally considered the HMP_2012 dataset [10] for body site discrimination between gut (N = 414) and oral (N = 147) samples.
Fig 2.
Classification accuracies are robust to degradation from genus-level relative abundance to presence/absence profiles in 16S rRNA datasets.
Results obtained on 30 case-control studies for host phenotype classification from human microbiomes. (A) Number of case and control samples across the different studies. (B) AUC and (C) AUPRC scores using RF as back-end classifiers on species-level taxonomic profiles. Comparison between relative abundance (in blue) and presence/absence (in red) profiles highlighted negligible differences and no statistical differences in none of the studies (see S5 Table for p-values) as found also in shotgun datasets (see Fig 1). Metrics of comparison in terms of AUC, AUPRC, precision, recall, and F1 are summarized in S5 Table. (C) Number of statistically significant taxa from relative abundance (in blue) and presence/absence (in red) profiles.
Fig 3.
Classification accuracies are not impacted when relative abundances are thresholded up to 0.001%.
Results on the 25 case-control shotgun studies by comparing the baseline (i.e., species-level relative abundance profiles) with the presence/absence profiles generated by thresholding at different relative abundance values (ranging from 0% to 0.1%). (A) Difference in AUC between the presence/absence and the relative abundance RF classification result. A positive value indicates that presence/absence outperforms relative abundance data. AUC scores at different thresholds are summarized in S2 Table. (B) Difference in number of statistically significant taxa (numbers summarized in S7 Table).
Fig 4.
Classification results are more impacted to relative abundance degradation at coarser taxonomic resolution.
Results on the 25 case-control shotgun studies by comparing the baseline (i.e., relative abundance profiles) with the presence/absence profile generated by thresholding at 0.0% and varying taxonomic resolution from species to order level. Difference in AUC between the presence/absence and the relative abundance RF classification result. A positive value indicates that presence/absence outperforms relative abundance data.
Fig 5.
Findings in terms of stability of the classification accuracy are robust to the classifier choice.
Differences in terms of AUC between presence/absence and relative abundance profiles for the 25 case-control shotgun datasets at varying classification algorithms. ENet: Elastic Net; LSVM: SVM with linear kernel; SVM: SVM with RBF kernel; RFs: Random Forests.
Fig 6.
Degradation of relative abundance profiles does not impact LODO classification.
Results in terms of leave-one-dataset-out (LODO) validation on 10 CRC shotgun datasets. (A) AUC scores using RF as back-end classifiers on species-level relative abundance (in pink) and presence/absence profiles generated at different threshold values. (B) Difference in AUC between species and other taxonomic-level resolutions. A negative value indicates that species-level outperforms the comparison level. (C) Difference in AUC between presence/absence and relative abundance classification results at varying taxonomic levels.