The use of variational autoencoders to characterise the heterogeneous subpopulations that arise due to antibiotic treatment
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) is a persistent threat to global agriculture and healthcare systems. One of the challenges towards development of robust antimicrobials to date has been the limitation posed by low resolution bacterial sequencing technologies. The recent development of Bacterial Single Cell RNA sequencing protocols has provided an unprecedented opportunity in AMR research as it now enables researchers to probe bacterial populations at single cell resolution. In this study, we apply a Bayesian Variational Autoencoder, MrVI, to data generated by one such Bacterial Single Cell RNA sequencing protocol, BacDrop, and use it characterise changes in gene expression levels before and after antibiotic perturbation. Through the use of MrVI, we were able to find distinct DNA damage and heat shock response subpopulations. We also determined that each of the subpopulations could be mapped back to its respective antibiotic treatments, providing more precise insight into their mechanisms of resistance. These preliminary results indicate the potential that this new window into intracellular bacterial communication provides, and motivate the continued exploration of models to unveil the mechanisms underlying AMR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".