Comparing Microarray Versus RT-PCR Assessment of Renal Allograft Biopsies: Similar Performance Despite Different Dynamic Ranges
Bibliographic record
Abstract
In renal allografts, assessing gene expression can add relevant diagnostic information to histopathology. Results can be expressed as single genes or gene sets, representing pathogenesis-based transcript sets (PBTs): cytotoxic T-cell-associated, interferon gamma- induced or decreased kidney parenchymal transcripts. Two technology platforms are available: RT-PCR and microarrays. We compared RT-PCR, U133plus2.0 microarrays and histopathology in 86 biopsies. We compared 13 potentially diagnostic genes as RT-PCR probes to microarray-derived PBTs, 'mini'-PBTs (small sets of 3-5 transcripts) and a histology classifier. Most RT-PCR probes (10/13) correlated well with the corresponding microarray probe sets (r > 0.8). Exceptions included FASLG and CD8B1 microarray probe sets, which were not performing on microarrays but were detectable by RT-PCR most likely due to differences in sensitivity. In general, RT-PCR showed greater dynamic range, detecting small changes in normal kidneys, but RT-PCR and microarrays gave similar results in abnormal kidneys. Individual transcripts or mini-PBTs assessed by either platform correlated well with one another, with microarray PBTs and the histology classifier. Thus, microarrays and RT-PCR assessments agree strongly with one another and histopathology in assessing transplant inflammation, particularly, when results are expressed as PBTs or mini-PBTs. The dynamic range of both platforms was sufficient to detect the relevant changes in rejection.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".