Permutation tests to assess sex differences in omics data
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Bibliographic record
Abstract
It is common to sex-stratify analyses of omics data and to report effects as 'sex-specific' when they are significant in only one sex. However, when analysing hundreds or thousands of molecules, this approach will yield many spurious 'sex-specific' effects if not supported by significant interactions. I illustrate this problem using an RNA sequencing dataset showing almost no significant sex by treatment interactions, but where sex-stratified analyses yield hundreds of 'sex-specific' effects of treatment. These 'sex-specific' effects could be spurious or could be real but not show interactions due to low statistical power. To distinguish these possibilities, I describe permutation tests, which provide an intuitive way to determine if a pattern of observations differs from what would be expected due to chance. For this dataset, assigning sex at random often generates more 'sex-specific' effects than the real data, demonstrating that there is little evidence of sex differences. Next, I simulate an RNA sequencing dataset that includes genes modelled to have sex-specific effects of a condition. As expected, analysis of this simulated dataset yields both significant interactions and sex-specific effects in sex-stratified analyses. While stratified analyses detect a higher number of sex-specific effects than the analysis of interactions, they erroneously identify genes not modelled to show sex-specific effects more often than interactions. A permutation test confirms that the number of sex-specific effects observed in the simulated dataset is greater than expected due to chance. Permutation tests can be applied to omics studies of sex differences, simultaneously providing (i) a clear and simple demonstration of the problems of sex-stratified analyses, and (ii) additional evidence of sex-specific effects where these are present. R code is provided for permutations, simulations, and plots to visualize potential sex-specific effects, which can be adapted to other types of data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it