PepC: proteomics software for identifying differentially expressed proteins based on spectral counting
Bibliographic record
Abstract
UNLABELLED: Identifying biologically significant changes in protein abundance between two conditions is a key issue when analyzing proteomic data. One widely used approach centers on spectral counting, a label-free method that sums all the tandem mass spectra for a protein observed in an analysis. To assess the significance of the results, we recently combined the t-test and G-test, with random permutation analysis, and we validated this approach biochemically. To automate the statistical method, we developed PepC, a software program that balances the trade-off between the number of differentially expressed proteins identified and the false discovery rate. This tool can be applied to a wide range of proteomic datasets, making data analysis rapid, reproducible and easily interpretable by proteomics specialists and non-specialists alike. AVAILABILITY AND IMPLEMENTATION: The software is implemented in Java. It has been added to the Trans Proteomic Pipeline project's 'Petunia' web interface, but can also be run as a command line program. The source code is GNU Lesser General Public License and the program is freely available on the web. http://sashimi.svn.sourceforge.net/viewvc/sashimi/trunk/trans_proteomic_pipeline/src/Quantitation/Pepc.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".