Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipid…
bio-metabolomics-xcms-preprocessing
维护者 FreedomIntelligence · 最近更新 2026年4月1日
XCMS3 workflow for LC-MS/MS metabolomics preprocessing. Covers peak detection, retention time alignment, correspondence (grouping), and gap filling. Use when processing raw LC-MS data into a feature table for untargeted metabolomics.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-metabolomics-xcms-preprocessing
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Requires Bioconductor 3.18+ ,支持 xcms 4.0+ 、 MSnbase 2.28+。
- raw_files <- list.files('raw_data',pattern = '\\.(mzML|mzXML)$',full.names = TRUE)。
原始文档
SKILL.md 摘录
Load Raw Data
Goal: Import raw LC-MS files into R for downstream peak detection and alignment.
Approach: Read mzML/mzXML files into an OnDiskMSnExp object using MSnbase for memory-efficient access.
"Process my raw LC-MS data into a feature table" → Detect chromatographic peaks, align retention times across samples, group corresponding peaks, and fill missing values to produce a sample-by-feature intensity matrix.
library(xcms)
library(MSnbase)
## Create OnDiskMSnExp object
raw_data <- readMSData(raw_files, mode = 'onDisk')
## Check data
raw_data
table(msLevel(raw_data))
适用场景
- 适合在processing raw LC-MS data into feature table ,用于 untargeted metabolomics时使用。
不适用场景
- Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。
上游相关技能
- metabolite-annotation - Identify metabolites
- normalization-qc - Normalize feature table
- statistical-analysis - Differential analysis
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