Fig. 2: Microbial species associated with health status or different disease phenotypes.
From: Faecal microbiome-based machine learning for multi-class disease diagnosis
The top 50 microbial species contributing to the random forest multi-class classifier were clustered by taxonomy, and different phenotypes were clustered using hierarchical clustering. Associations were coloured by direction of effect (red, positive; blue, negative; p < 0.05), with associations significant at FDR < 0.05 marked with a plus (positive correlations) or minus (negative correlations), respectively. The nominal significance (p-value) of associations was calculated by MaAsLin 2, and the false discovery rate (FDR) was computed by Benjamini–Hochberg correction. CA colorectal adenomas, CD Crohn’s disease, CRC colorectal cancer, CVD cardiovascular disease, IBS-D diarrhoea-dominant irritable bowel syndrome, PACS post-acute COVID-19 syndrome, UC ulcerative colitis. Source data are provided as a Source Data file.
