Process mass spectrometry data with XCMS and matchms for metabolomics analysis.
npx skills add https://github.com/inflexa-ai/inflexa --skill metabolomics-inflexa-ai
Please help me install this Agent Skill. Skill: metabolomics Source: https://github.com/inflexa-ai/inflexa/tree/main/skills/metabolomics Command: npx skills add https://github.com/inflexa-ai/inflexa --skill metabolomics-inflexa-ai
This skill addresses the complexity of processing raw mass spectrometry data, providing a reproducible pipeline for untargeted and targeted metabolomics, lipidomics, and pathway interpretation.
Use the metabolomics skill to process the raw mzML files in the current directory and generate a feature intensity matrix with statistical annotations.
High-intent search queries and answers about installing and using this skill.
You can process raw mass spectrometry data by automating peak picking, retention time alignment, and gap filling using industry-standard XCMS workflows to generate a reproducible feature matrix for untargeted metabolomics.
Yes, the metabolomics workflow requires R-based XCMS for peak detection alongside Python-based matchms for spectral matching and feature quantification, supporting comprehensive lipidomics data interpretation.
Metabolomics differential expression analysis utilizes robust normalization, log-transformation, and statistical testing including PCA, PLS-DA, and t-tests to identify significant metabolic shifts between control and treatment groups.
To convert mzML files into a normalized feature intensity matrix, you apply standardized preprocessing and statistical rigor pipelines that automate peak detection, alignment, and gap filling for mass spectrometry data.
Yes, the metabolomics pipeline supports both untargeted and targeted workflows for mass spectrometry data interpretation, enabling comprehensive processing from raw files to pathway mapping and statistical annotations.
Pathway mapping and clinical study data interpretation require dependencies including pymzml and MSnbase to handle raw mzML files, spectral matching, and feature quantification within the XCMS processing pipeline.
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galeepIdentify over-represented pathways and gene sets in gene lists.
Zeyuyang-0420Process mass spectrometry data and calculate spectral similarities with Python libraries.
ovachieverAnalyze LC-MS/MS mass spectrometry data via Python bindings to OpenMS.
