Abstract 3050: Quality control samples for future population-based microbiome studies
Bibliographic record
Abstract
Abstract Introduction: There is a critical need for complex microbiome quality control standards representing population-based samples for microbial community profiling and analysis in large scale epidemiologic studies. Methods: We developed standard quality control samples from five volunteers with different phenotypes, comprising one obese female, one healthy male, one male on a low-carb diet, one infant, and one male with Crohn’s Disease, and evaluated their microbial metagenomic profiles within three laboratories at two different timepoints. To quantify the percentage of microbiome variability explained by donors, laboratory and sequencing run, a distance-based coefficient of determination R2 was estimated using a permutational multivariate analysis of variance. In addition, we calculated the intraclass correlation coefficients (ICC) for the relative abundance of the most abundant species, two alpha diversity metrics (i.e., observed number of species and Shannon index) and the first principal coordinates of three beta diversity matrices (i.e., Bray-Curtis, Jaccard and Aitchison) to estimate the accuracy of fecal microbial profiles between the three different laboratories as well as within the laboratories. Results: The variability introduced by the phenotype of the donors explained 82.7% to 95.3% of the overall variability, which was higher than the variability introduced by the laboratories (1.8% to 3.1%) and the sequencing runs (0.6% to 1.7%) - the residual percent variance explained varied between 2.2% and 12.4%. Observations based on principal coordinates analysis showed that samples clustered by donor and not by laboratory or sequencing runs. The five donor clusters were well separated and very distinct. Based on the comparison of species relative abundances, each donor displayed very different microbial profiles; and the microbial profiles of each donor were comparable between the three different laboratories and the two sequencing runs in each laboratory. The reproducibility within and between the laboratories was good to excellent for most diversity metrics (ICCs higher than 0.97) and species relative abundances (range, ICCs=0.70-0.99); however, the reproducibility of the observed number of species was moderate (ICC=0.64 for the first laboratory, ICC=0.78 for the second laboratory, ICC=0.81 for the third laboratory, and ICC=0.42 between the laboratories). Conclusions: These standard quality control samples can be used as a reference in future population-based epidemiologic studies to pool or meta-analyze microbiome data. Citation Format: Semi Zouiouich, Smriti Karwa, Yunhu Wan, Andrew Chan, Joseph Petrosino, Emma Allen-Vercoe, Rob Knight, Jianxin Shi, Mitchell Gail, Christian Abnet, Emily Vogtmann, Rashmi Sinha. Quality control samples for future population-based microbiome studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3050.
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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 | 0.086 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".