Common Errors in the Implementation and Interpretation of Microarray Studies
Bibliographic record
Abstract
In Brief Microarray analysis is used to tackle transplant-related problems as diverse as diagnosing rejection, predicting graft loss, and determining who can safely be removed from immunosuppression. Highly accurate predictions seem to be the norm. Unfortunately, many of these studies are flawed, either through questionable experimental design or improper validation methods. In addition, results are often presented in a misleading manner which exaggerates their true worth. In this paper, we describe the most common and serious errors and misrepresentations. As “big data”, high-dimensional datasets, and complex analyses dominate the literature, the ability to review, interpret, and critically evaluate the studies becomes more difficult and remote. Reeves and colleagues provide a guide to understanding proper methods and data flow for microarray studies, and serves as an outline for critiquing these and other studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.182 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".