QC & Data Quality Assessment
Assessment of sample quality, pooled-QC reproducibility, feature detection, coefficient of variation, filtering results, and overall analytical performance before downstream statistical analysis.
Data Analysis & Bioinformatics
From complex mass spectrometry data to interpretable biological results
Integrated statistical analysis, compound annotation, visualization, and quality assessment help investigators move from complex datasets to clear, biologically meaningful results.
Analysis services
Analysis matched to the study design and data type
Our analysis workflows extend beyond generating lists of significant features. We assess data quality, apply appropriate statistical models, characterize patterns across samples and features, evaluate annotation confidence, and place findings in biological context.
Multivariate & Exploratory Analysis
PCA, clustering, heatmaps, and related exploratory analyses are used to identify sample relationships, outliers, and coordinated patterns associated with biological or experimental groups.
Differential & Statistical Analysis
Fold change, t-tests, ANOVA, empirical-Bayes moderated statistics, post-hoc comparisons, multiple-testing correction, and selected machine-learning approaches are used as appropriate to the study design and analytical questions.
Annotation & Compound Classification
Integration of MS/MS and accurate-mass annotations, confidence-level reporting, and compound-class summaries for interpretable chemical context.
Pathway & Biological Interpretation
Pathway-oriented analysis, compound-class patterns, and interpretation of coordinated biological responses rather than isolated feature lists alone.
Reporting & Literature Context
Structured reports combine results, figures, QC summaries, annotation information, statistical interpretation, and literature context to support follow-up and publication.
Example outputs
Graphics designed to reveal structure, effect, and biological pattern
These illustrative examples represent the types of visual outputs commonly used in Core reports. Final figures are generated from the specific study design and data.
PCA & sample-level structure
Visualize separation among experimental groups, identify outliers, and evaluate whether major sources of variance correspond to the study design.
Heatmaps & feature clustering
Summarize coordinated abundance patterns, identify clusters with similar responses, and connect feature-level results to broader biological trends.
Differential analysis
Combine effect size and statistical evidence to prioritize features, with appropriate distinction between exploratory uncorrected results and FDR-controlled findings.
Annotation confidence & chemical context
Separate high-confidence spectral annotations from lower-confidence accurate-mass or formula matches and summarize results by compound class where possible.
Deliverables
Results organized for review and follow-up
Analysis results are provided in a structured report with supporting figures, statistical tables, annotation information, methods, and links to interactive or underlying data resources where applicable.
Summary of major findings
Statistical tables and figures
Annotation and pathway results
Methods and supporting data resources
Need analysis support for an existing or planned study?
Analysis can be incorporated into Core-generated metabolomics, lipidomics, proteomics, and targeted datasets, or discussed as a stand-alone bioinformatics need when appropriate.