Enhanced Quality Metrics for Assessing RNA Derived From Archival Formalin-Fixed Paraffin-Embedded Tissue Samples
Bibliographic record
Abstract
Formalin-fixed paraffin-embedded (FFPE) tissues provide an important resource for toxicogenomic research. However, variability in the integrity or quality of RNA obtained from archival FFPE specimens can lead to unreliable data and wasted resources, and standard protocols for measuring RNA integrity do not adequately assess the suitability of FFPE RNA. The main goal of this study was to identify improved methods for evaluating FFPE RNA quality for whole-genome sequencing. We examined RNA quality metrics conducted prior to RNA-sequencing in paired frozen and FFPE samples with varying levels of quality based on age in block and time in formalin. RNA quality was measured by the RNA integrity number (RIN), a modified RIN called the paraffin-embedded RNA metric, the percentage of RNA fragments >100-300 nucleotides in size (DV100-300), and 2 quantitative PCR-based methods. This information was correlated to sequencing read quality, mapping, and gene detection. Among fragmentation-based methods, DV and PCR-based metrics were more informative than RIN or paraffin-embedded RNA metric in determining sequencing success. Across low- and high-quality FFPE samples, a minimum of 80% of RNA fragments >100 nucleotides (DV100 > 80) provided the best indication of gene diversity and read counts upon sequencing. The PCR-based methods further showed quantitative reductions in amplifiable RNA of target genes related to sample age and time in formalin that inform input quantity of FFPE RNA for sequencing. These results should aid in screening and prioritizing archival FFPE samples for retrospective analyses of gene expression.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".