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Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.
Passed 14 of 21 test cases with the skill, and 5 of 21 without it.
and 9 more in the full report
11 fixed6 passed either way8 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which primary literature citation forms the algorithmic basis for the s0-based fudge factor correction used in this proteomics workflow?
| Checked | Without skill | With skill |
|---|---|---|
| Cites Giai Gianetto et al. (2016) for the s0-based variance stabilization algorithm. | Missed | Passed |
The instructions your model reads when the skill loads.
This skill performs differential expression analysis on label-free quantitative (LFQ) intensity data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.
proteinGroups.txt
.raw intensity columnsReversePotential contaminant / ContaminantOnly identified by sitemedian - shift × stdshift = 1.8scale = 0.3test cases passed, vs 5 / 21 without
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