菜单
Use when after batch correction of metabolomics data when you need to identify and remove compounds whose signal in background/blank samples exceeds a tolerable contamination level.
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
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | background-contamination-threshold-assessment |
| description | Use when after batch correction of metabolomics data when you need to identify and remove compounds whose signal in background/blank samples exceeds a tolerable contamination level. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3695","edam_topics":["http://edamontology.org/topic_3172","http://edamontology.org/topic_3375"],"tools":["mzQuality","R","SummarizedExperiment","mzQualityDashboard"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/jasms.5c00073","title":"mzquality"}] |
| evidence_spans | ["mzQuality requires a specific format for the input data.","mzQuality requires a specific format for the input data","library(mzQuality)","knitr::rmarkdown, library(mzQuality)","The `buildExperiment` function will then take the data and create an experiment object that can be used for analysis.","Internally, mzQuality uses Bioconductors' *SummarizedExperiment* object to store the data"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_mzquality_cq","doi":"10.1021/jasms.5c00073","title":"mzquality"}],"dedup_kept_from":"coll_mzquality_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/jasms.5c00073","all_source_dois":["10.1021/jasms.5c00073"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Assess and filter metabolomics compounds based on background signal contamination levels relative to study samples. This skill applies a user-configurable background percentage threshold to flag compounds with excessive signal in blank/negative control samples, preventing false-positive identifications and ensuring reportable compound reliability.
After batch correction of metabolomics data when you need to identify and remove compounds whose signal in background/blank samples exceeds a tolerable contamination level. Apply this skill when your SummarizedExperiment object contains assays with both study sample and background sample measurements, and you must decide which compounds are sufficiently clean to report.
Within the mzQuality doAnalysis workflow, set the backgroundPercentage threshold parameter (default and recommended: 40) to define the maximum tolerable background signal as a percentage of study sample signal. The function automatically calculates the ratio of background signal to study sample signal for each compound. Compounds exceeding this threshold are flagged by setting their use column in rowData to FALSE, indicating they are unreliable for reporting. The threshold filters compounds based on signal contamination alone; combine this filter with RSDQC and QC presence thresholds for comprehensive compound reliability assessment. Extract flagged compounds and generate diagnostic plots to verify that removed compounds genuinely show elevated background noise rather than true low-abundance metabolites.
exp <- doAnalysis(exp = exp, removeOutliers=TRUE, removeBadCompounds=TRUE, backgroundPercentage=40); reportable_compounds <- exp[rowData(exp)$use, ]
exp[rowData(exp)$use, exp$use] and verify the remaining compound count decreases relative to input; tabulate the removed compounds with their background percentages to confirm they exceed the set threshold.use in both the rowData and colData slots of the SummarizedExperiment. These contain either a TRUE or FALSE value, indicating if the compound or sample is"