Bibliographic record
Abstract
Microarray technology is extensively used in biological research. The applied technologies vary greatly between laboratories, and outstanding questions remain regarding the degree of correlation among approaches. Recently, there has been a drive toward ensuring high-quality microarray data by the implementation of MIAME (Minimal Information About a Microarray Experiment) guidelines and an emphasis on ensuring public-availability to all datasets. However, despite its current widespread use and availability, very little is known about the extent to which application of the different technologies influences the outcome of transcriptional profiles and differential expression. The results among the handful of published studies are conflicting. Here, we present a comprehensive evaluation encompassing different reporter systems (short oligonucleotides, long oligonucleotides and cDNAs), labelling techniques and hybridization protocols. We used four oligonucleotide and two cDNA platforms to compare gene expression between two sample types. We determined the overall consistency (reproducibility) within each platform, and correlation among replicates within and between technologies. We find that the top performing platforms show low levels of technical variability that result in an increased ability to detect differential expression. Most importantly, we show the top four platforms are highly correlated with biological, rather than technological, differences accounting for the majority of variation in the data.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".