Direct estimation of differential networks under high‐dimensional nonparanormal graphical models
Bibliographic record
Abstract
Abstract In genomics, it is often of interest to study the structural change of a genetic network between two phenotypes. Under Gaussian graphical models, the problem can be transformed to estimating the difference between two precision matrices, and several approaches have been recently developed for this task such as joint graphical lasso and fused graphical lasso. However, the multivariate Gaussian assumptions made in the existing approaches are often violated in reality. For instance, most RNA‐Seq data follow non‐Gaussian distributions even after log‐transformation or other variance‐stabilizing transformations. In this work, we consider the problem of directly estimating differential networks under a flexible semiparametric model, namely the nonparanormal graphical model, where the random variables are assumed to follow a multivariate Gaussian distribution after a set of monotonically increasing transformations. We propose to use a novel rank‐based estimator to directly estimate the differential network, together with a parametric simplex algorithm for fast implementation. Theoretical properties of the new estimator are established under a high‐dimensional setting where grows with almost exponentially fast. In particular, we show that the proposed estimator is consistent in both parameter estimation and support recovery. Both synthetic data and real genomic data are used to illustrate the promise of the new approach. The Canadian Journal of Statistics 48: 187–203; 2020 © 2019 Statistical Society of Canada
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".