菜单
Use when you have a QC-annotated feature table (samples × features with QC sample identifiers) from LC-MS untargeted metabolomic profiling and observe systematic signal drift across the run sequence or between batch blocks.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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| name | qc-sample-batch-drift-correction |
| description | Use when you have a QC-annotated feature table (samples × features with QC sample identifiers) from LC-MS untargeted metabolomic profiling and observe systematic signal drift across the run sequence or between batch blocks. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3799","edam_topics":["http://edamontology.org/topic_3172","http://edamontology.org/topic_0091"],"tools":["MetCorR","R","OUKS"],"techniques":["LC-MS","GC-MS"]} |
| derived_from | [{"doi":"10.1021/acs.jproteome.1c00392","title":"Omics Untargeted Key Script"}] |
| evidence_spans | ["New QC-GAM method (MetCorR) with associated scripts were introduced.","R based open-source collection of scripts called :red_circle:*OUKS*","R ≥4.1.2"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_omics_untargeted_key_script_cq","doi":"10.1021/acs.jproteome.1c00392","title":"Omics Untargeted Key Script"}],"dedup_kept_from":"coll_omics_untargeted_key_script_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.jproteome.1c00392","all_source_dois":["10.1021/acs.jproteome.1c00392"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Correct run-order and batch-dependent signal drift in LC-MS untargeted metabolomic feature tables using QC samples as reference points and Generalized Additive Models (GAM). This skill removes systematic variation introduced during sample acquisition while preserving biological signals.
Apply this skill when you have a QC-annotated feature table (samples × features with QC sample identifiers) from LC-MS untargeted metabolomic profiling and observe systematic signal drift across the run sequence or between batch blocks. Use it after integration and imputation steps, before annotation or filtering, to ensure that downstream statistical comparisons are not confounded by instrumental signal decay or batch effects.
Load the QC-annotated feature table into R along with sample metadata (run order and batch identifiers). Execute the MetCorR QC-GAM algorithm by fitting Generalized Additive Models on QC sample intensities, with run order and batch as smooth terms to estimate correction factors for each feature. The algorithm models batch-dependent signal drift by using QC samples as reference points across the run sequence, then applies fitted correction factors to all sample features. Output the corrected feature table in CSV format, preserving sample and feature identifiers. Success is indicated by visual flattening of intensity trajectories in diagnostic plots (e.g., RLA-plot comparing before/after correction).
library(MetCorR); data(example_intensity); data(example_meta); out <- MetCorR(method=2, int_data=example_intensity, order=example_meta$order, class=example_meta$class, batch=example_meta$batch, qc_label="QC")