Table 4.
Differentially abundant taxa.
| Taxa lineage | Code | Number of connections | Relabund% | Obese/Lean | Obese/Normal | Lean/Normal | All BSC |
|---|---|---|---|---|---|---|---|
| Firmicutes;Clostridiaceae;SMB53 | AJ | 29 | 0.01 | X | |||
| Anaeroplasmataceae;Anaeroplasma | AS | 10 | 0.11 | X | X | ||
| Actinobacteria;Coriobacteriaceae;Collinsella | BE | 74 | 0.01 | X | |||
| Actinobacteria; Microbacteriaceae;Microbacterium | CC | 109 | 0.01 | X | X | ||
| Firmicutes;Peptococcaceae;g__ | CK | 56 | 0.03 | X | |||
| Firmicutes; Mogibacteriaceae;Mogibacterium | DL | 76 | 0.35 | X | |||
| Bacteroidetes;Bacteroidales;RF16 | EF | 23 | 1.17 | X | |||
| Fibrobacteraceae;Fibrobacter | EH | 9 | 2.36 | X | |||
| Firmicutes;Lactobacillales;Other | EI | 19 | 0.00 | X | |||
| Firmicutes;Clostridiales;f__EtOH8 | EJ | 5 | 0.01 | X | |||
| Spirochaetaceae;Treponema | FR | 15 | 2.67 | X | |||
| Firmicutes;Peptostreptococcaceae;g__ | HK | 38 | 0.01 | X | |||
| Verrucomicrobia;RFP12;g__ | HS | 23 | 0.03 | X | X | X | |
| Actinobacteria;Micrococcaceae;g__ | IC | 102 | 0.03 | X | |||
| Bacteroidetes;Paraprevotellaceae;Other | IF | 17 | 0.01 | X | |||
| Bacteroidetes;Paraprevotellaceae;g__ | JF | 19 | 1.21 | X | X | X | |
| Firmicutes;Bacillaceae;Bacillus | JH | 88 | 0.02 | X | X | ||
| Firmicutes;Streptococcaceae;Streptococcus | MI | 27 | 0.85 | X | |||
| Cyanobacteria;YS2;f__;g__ | NG | 25 | 0.40 | X | |||
| Firmicutes;Ruminococcaceae;Oscillospira | NK | 65 | 0.55 | X | X | ||
| Firmicutes;Clostridiales;Other;Other | PI | 3 | 0.26 | X | |||
| Bacteroidetes;Bacteroidales;f__;g__ | QE | 69 | 16.24 | X | X | ||
| Firmicutes;Veillonellaceae;g__ | RK | 74 | 0.22 | X | X | X | |
| Actinobacteria;Nocardiaceae;Rhodococcus | TC | 119 | 0.01 | X | X | ||
| Firmicutes;Lachnospiraceae;Epulopiscium | UJ | 28 | 0.20 | X | |||
| Firmicutes;Erysipelotrichaceae;Eubacterium | YL | 48 | 0.16 | X | X |
Taxa identified in whole group and pairwise comparisons (padj < 0.05) using linear binomial distribution in DESeq. Network connectivity is estimated by numbers of connections in correlation network.