数据与复现蛋白质组与代谢组FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
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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.

OpenClawNanoClaw分析处理复现实验bio-metabolomics-xcms-preprocessing🧬 bioinformatics (gptomics bio-* suite)bioinformatics — proteomics & metabolomicsxcms3

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

2 min

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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