Summary of contributions to GAW15 Group 16: Processing/normalization of expression traits
Bibliographic record
Abstract
Here, we summarize the contributions to group 16 of Genetic Analysis Workshop 15, held in Florida, U.S.A. The theme of this group was preprocessing of expression quantitative trait loci (eQTL) studies using the Affymetrix platform. The objective of the Genetic Analysis Workshop 15 problem 1 dataset was to use transcript levels that are measured using DNA microarrays as quantitative traits and localize the genes or other features of the DNA that control gene expression by quantitative trait loci linkage analyses. All contributors of this group used the microarray expression profiles (problem 1) data. Various approaches and questions were examined to investigate the effects of preprocessing methods and/or gene filtering on the interpretation of data, specifically on heritability estimates of gene expression and on linkage results. In addition, some contributors focused on the statistical issues involved in large-scale genetic analyses of quantitative traits that account for or build composite phenotypes from a large number of correlated traits. Since the true eQTLs are not known in the problem 1 data, results from the 11 studies cannot be fully evaluated for the methods employed. However, several common trends were found. All reports concluded that preprocessing statistical analyses may have an important impact on eQTL analyses and on the identification of cis-/trans-regulators and/or major biological pathways.
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 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 | 0.001 |
| 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".