Additional file 2 of Analysis of early childhood intestinal microbial dynamics in a continuous-flow bioreactor
Bibliographic record
Abstract
Supplementary Material 2: Figure 1. Relative abundance of ASVs identified in early childhood fecal sample and fecal-derived chemostat culture using DADA2 non-pooled or pseudo-pooled inference methods. DADA2 method aims to accurately reconstruct the exact amplicon sequence variants (ASVs) truly present in a sample from the noisy amplicon sequencing reads [80]. By default (non-pooled method), DADA2 parameters are set to achieve high accuracy by reducing the number of spurious ASV outputs and increasing the specificity. However, the tradeoff for that high specificity is that sensitivity, particularly to rare variants, is reduced by the default non-pooled method. The pseudo-pooled sample inference method allows information to be shared across related samples in a dataset and is particularly effective in longitudinal and inoculation experiments in which samples are taken repeatedly from the same source. This method is expected to improve sensitivity and provide a more accurate description of ASVs at very low frequencies without demanding high computational time. The alternative full pooled sample inference method explicitly infers ASVs across the dataset. This method is also expected to improve sensitivity and provide a more accurate description of ASVs at very low frequencies, at a cost of higher computational time which extends proportionally as a function of the number of samples squared. Here we compare the total number and relative abundance of ASVs identified in two early childhood fecal samples and fecal-derived chemostat cultures using DADA2 non-pooled, pseudo-pooled or full-pooled inference methods. We observe that the pseudo-pooled inference method increased the number of ASVs identified on fecal samples and fecal-derived chemostat cultures. Pseudo-pooling of NS0 samples allowed the identification of additional 81 – 89 ASVs, while in NS1 samples, we observed 41 – 80 new ASVs identified. As expected, most new variants discovered by this method are present in low abundance. In contrast, the pseudo-pooled method does not affect the presence and relative proportion of high abundant ASVs, suggesting high specificity. Full-pooling of NS0 samples allowed identification of an additional 19 – 31 ASVs, while in NS1 samples, this approach returned a loss of 85 ASVs and a gain of 28 new ASVs identified. As expected, most variation in ASVs discovered by these methods were present in low abundance. These results indicate that the pseudo-pooled method increased DADA2 sensitivity, preserved specificity, and allowed identification of rare variants in both fecal samples and fecal-derived chemostat cultures. We observed no clear benefit to the full pooling inference method that was proportional to the increased computational time it required. All figures in this manuscript, except this one, have been generated using the pseudo-pooled inference method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.739 | 0.152 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".