A Comparison of Parametric and Semi-Parametric Models for Microarray Data Analysis
Bibliographic record
Abstract
Microarray technology has revolutionized genomic studies by enabling the study of differential expression of thousands of genes simultaneously. Parametric, nonparametric and semi-parametric statistical methods have been proposed for gene selection within the last sixteen years. In an effort to find the “gold standard", the performance of some common parametric and nonparametric methods have been compared in terms of power to select differentially expressed genes and other desirable properties. However, no such comparisons have been conducted between parametric and semi-parametric models. In this study, we compared a semi-parametric model based on copulas with a parametric model (the quantitative trait analysis or QTA model) in terms of power and the ability to control the Type I error rate. In addition, we proposed a simple algorithm for choosing an optimal copula. The two approaches were applied to a publicly available melanoma cell lines dataset for validation. Both methods performed well in terms of power but the copula approach was notably the better. In terms of the Type I error rate control, the two methods were comparable. More methods for selecting an optimal copula for gene expression data need to be developed, as the proposed procedure is limited to copulas that permit both negative and positive dependence only.
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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 | 0.033 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".