A two-stage normalization method for robust differential expression analysis in microarray experiments
Bibliographic record
Abstract
In this research, we introduce an approach to improve the reliability of genetic data analysis. Consistency of the results obtained from microarray data analysis strongly relies on elimination of non-biological variations during data normalization process. Instability in Housekeeping Gene (HKG) expression after performing common normalization methods might be an indication of inefficiency potentially resulting in sampling bias in differential expression analysis. This research aims to reduce the sampling bias in microarray experiments proposing a two-stage normalization algorithm. Proposed approach consists of non-linear Quantile normalization at the first stage and linear HKG based normalization at the second stage. We tested the efficiency of the two-stage normalization method using publicly available microarray datasets obtained from the experiments mainly in the field of reproductive biology. Results show that combined Robust Multiarray Average (RMA) and HKG normalization method reduces the sampling bias in experiments when variations in HKG expression is observed after RMA normalization.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".