BI
Data & Repro
bio-metabolomics-lipidomics
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FreedomIntelligence/OpenClaw-Medical-SkillsView
Data & Repro
BI
bio-workflows-proteomics-pipeline
Maintainer FreedomIntelligence · Last updated April 1, 2026
DDA/DIA proteomics: search → quantification → differential abundance.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-workflows-proteomics-pipeline
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- proteins <- read.delim('proteinGroups.txt', stringsAsFactors = FALSE) cat('Loaded', nrow(proteins), 'protein groups\n').
- proteins <- proteins[proteins$Potential.contaminant!= '+' & proteins$Reverse!= '+' & proteins$Only.identified.by.site!= '+', ] cat('After filtering:', nrow(proteins), 'proteins\n').
Source Doc
Excerpt From SKILL.md
Complete R Workflow
library(limma)
library(ggplot2)
library(pheatmap)
## Extract LFQ intensities
lfq_cols <- grep('^LFQ\\.intensity\\.', colnames(proteins), value = TRUE)
intensities <- proteins[, lfq_cols]
rownames(intensities) <- proteins$Majority.protein.IDs
colnames(intensities) <- gsub('LFQ\\.intensity\\.', '', colnames(intensities))
## === 2. LOG2 TRANSFORM & NORMALIZE ===
intensities[intensities == 0] <- NA
log2_int <- log2(intensities)
Use cases
- Use bio-workflows-proteomics-pipeline in research workflows aligned with this subject area.
- Follow the upstream documentation for the full working procedure.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- proteomics/data-import - Load MS data formats
- proteomics/proteomics-qc - Quality control before analysis
- proteomics/quantification - Normalization methods
- proteomics/differential-abundance - Statistical testing details
- proteomics/ptm-analysis - Phosphoproteomics and other PTMs
- data-visualization/specialized-omics-plots - Volcano plots
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