bio-proteomics-quantification
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-proteomics-quantification --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: MSnbase 2.28+, numpy 1.26+, pandas 2.2+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Protein Quantification **"Quantify proteins from my mass spec data"** → Extract protein abundances from MS data using label-free (LFQ, spectral counting), isobaric labeling (TMT, iTRAQ), or metabolic labeling (SILAC) approaches. - R: `MSstats::dataProcess()` for feature-to-protein summarization - Python: `pandas` for MaxLFQ-style normalization and ratio calculation - R: `MSnbase` for isobaric tag reporter ion extraction ## Label-Free Quantification (LFQ) ### Intensity-Based (MaxLFQ Algorithm) ```python import pandas as pd import numpy as np def maxlfq_normalize(intensities): '''Simplified MaxLFQ normalization''' log_int = np.log2(intensities.replace(0, np.nan)) # Median centering per sample sample_
- Version Compatibility
- Label-Free Quantification (LFQ)
- Intensity-Based (MaxLFQ Algorithm)
- Spectral Counting
- TMT/iTRAQ Quantification
- Python TMT Processing
- SILAC Quantification
- MSstats Workflow (R)
- Related Skills
What does the bio-proteomics-quantification skill do?
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-proteomics-quantification --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.