Sensitivity of transcriptomics: Different samples and methodology alter conclusions in Gulf pipefish (<i>Syngnathus scovelli</i>)
Bibliographic record
Abstract
Transcriptome analysis has become a central tool in evolutionary and functional genomics. However, variation among biological samples and analysis techniques can greatly influence results, potentially compromising insights into the phenomenon under study. Here, we evaluate differences in the brain transcriptome between female and male Gulf pipefish (Syngnathus scovelli). We perform comparisons between results from entire pipelines for brain transcriptome assembly, quantification, and analysis. We also offer a unique biological comparison between two sampling instances (Redfish Bay: n = 15, Port Lavaca: n = 7). Our results demonstrate crucial shortcomings with current experimental approaches. We found high variation within our results that was driven by both technical differences between pipelines and biological differences between pipefish samples. In our analysis of highly expressed genes, we found that the choice of methods influenced the degree of contamination or noise included in the identified genes. Notably, genes identified within the same pipeline were more similar than any other comparison. Our differential expression analysis revealed that both methodology and sampling location influenced the quantity and consistency of statistically significant transcripts. In the context of these results, we offer modifications to current practices that may increase the robustness of transcriptome-based conclusions. In particular, the use of a reference-guided assembly and an increase in sample sizes are likely to improve resistance to noise or error.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".