The impact of sample size and tissue type on the reproducibility of gene co-expression networks
Bibliographic record
Abstract
Identifying relationships between genes facilitates the comparison of different cell types at the transcriptomic level. Gene expression data such as RNA-seq can be used to construct co-expression networks, which is one means in systems biology to describe the coordinated expression patterns among genes across samples. Currently, there is no consensus as to the number of samples required to construct a reproducible gene co-expression network. Indeed, irreproducibility of gene expression experiments is a major challenge, and small sample sizes tend to be one of the major causes. However, recommending a single sample size that applies to all scenarios may not be practical. As such, we utilize a systematic, quantitative approach to study the effect of sample size on the reproducibility of constructing large, fully-connected gene co-expression networks using several correlation-based measures or mutual information. This approach does not require synthetic datasets that are constructed based on oversimplified assumptions nor is it dependent on known functional annotations. Further, we describe two similarity measures to measure consistency and use them to determine if the biological variance present within samples impacts the rate at which the networks will stabilize and compare to networks with randomly reassigned nodes. Our results show that the required number of samples to construct consistent co-expression networks could be influenced by the tissue type used to construct the networks as well as the similarity measure used to measure consistency.
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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 | 0.032 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".