GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package
Bibliographic record
Abstract
Abstract Background: Network-based analysis of gene expression through co-expression networks can be used to investigate modular interactions occurring between genes toward different biological functions. An extended description of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.Results: Here we present GWENA, a new R package that integrates gene co-expression network construction and a whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performances, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. We successfully prioritized a gene whose involvement was unknown in the muscle development and growth. We also gave new insight about the variations in patterns of co-expression as the already known age-dependent loss of connectivity was found coupled to a genes interactions reorganization leading to the expression of other functions involved in aging. Conclusion: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as conditions comparison, it eases the understanding of genes interactions involved in diseases or phenotypes. Going beyond actual packages to perform co-expression analysis, GWENA includes new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".