Real-time monitoring of aRNA production during T7 amplification to prevent the loss of sample representation during microarray hybridization sample preparation
Bibliographic record
Abstract
Gene expression analysis performed through comparative abundance of transcripts is facing a new challenge with the increasing need to compare samples of known cell number, such as early embryos or laser microbiopsies, where the RNA contents of identical cellular inputs can by nature be variable. When working with scarce tissues, the success of microarray profiling largely depends on the efficiency of the amplification step as determined by its ability to preserve the relative abundance of transcripts in the resulting amplified sample. Maintaining this initial relative abundance across samples is paramount to the generation of physiologically relevant data when comparing samples of different RNA content. The T7 RNA polymerase (T7-IVT) amplification is widely used for microarray sample preparation. Characterization of the reaction's kinetics has clearly indicated that its true linear phase is of short duration and is followed by a nonlinear phase. This second phase leads to modifications in transcript abundance that biases comparison between samples of different types. The impact assessment performed in this study has shown that the standard amplification protocol significantly lowers the quality of microarray data, rendering more than half of differentially expressed candidates undetected and distorting the true proportional differences of all candidates analyzed.
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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 | 0.001 |
| 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".