Providing a stable methodological basis for comparing transcript abundance of developing embryos using microarrays
Bibliographic record
Abstract
High throughput methods deliver large amount of data serving to describe the physiological treatment that is being studied. In the case of microarrays, there would be a clear benefit to integrate the published data sets. However, the numerous methodological discrepancies between microarray platforms make this comparison impossible. This incompatibility is magnified when considering the peculiar context of transcript management in early embryogenesis. The total RNA content is known to profoundly fluctuate during development. In addition, the mRNA population is subjected to poly(A) tail shortening and elongating events, a characteristic of stored and recruited messengers. These intrinsic factors need to be considered when interpreting any transcript abundance profiles during early development. As a consequence, many methodological details affect microarray platform performances and prevent compatibility. In an effort to maximize our microarray platform performance, we determined the various sources of variation for every one of the main steps leading to the production of microarray data. The five main steps involved in sample preparation were evaluated, as well as conditions for post-hybridization validation by qRT-PCR. These determinations were essential for the implementation of standardized procedures for our Research Network but they can also provide insight into the compatibility issues that the microarray community is now facing.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".