From Data to Value in Hours
Our AI-powered analysis automates each step, rapidly delivering results as an interactive web application that can be explored and shared across all stakeholders.
- Identify Relevant Clinical Variables
Clinical information (e.g., age, sex, BMI, drug use) is used to identify the most relevant molecular changes. We compare AI-based predictions with supplied metadata to detect and correct labeling errors and select the minimal set of relevant clinical variables to preserve statistical power in subsequent steps. - Assess Quality
We perform quality control (QC) on each sample to identify technical errors and use a data-driven approach to detect samples that differ substantially from others in the experiment, ensuring more reliable downstream insights. - Normalize Across Samples
To reveal true biological variation, we detect and reduce subtle technical noise that can arise during sample processing. We normalize metabolite intensities, filter out erroneous metabolites, impute missing values and adjust for known batch effects. - Find Differentially Abundant Metabolites
Depending on your experimental design and research question, we select the most appropriate, statistically rigorous approach. We account for technical and biological variability and automatically fit an appropriate linear regression model to each metabolite. - Pinpoint Affected Pathways
We perform enrichment analysis using GSEA method across all major metabolite pathway databases, clustering similar pathways to explore how groups of metabolites are affected, even when individual metabolites are not significantly altered. - Build Diagnostic Classifiers
We identify the minimal set of biomarkers that best differentiate sample groups. By evaluating multiple machine learning algorithms and cross-validation strategies, we assess classifier accuracy for potential diagnostic, prognostic, or treatment applications.
Additional Value Out-of-the-Box
Metabolomic data contains a wealth of valuable information that often goes unexplored. To ensure you do not miss key insights, these analyses are included as part of our standard workflow.
- Metabolite Functional Analysis
We group metabolites by functional categories and perform a statistical evaluation. This method effectively highlights statistically significant changes in concrete biological functions providing a system-level view. - Systemic Signal Drift Correction
There are time-dependent changes in the measured signal intensity during prolonged analytical runs. We use advanced modeling methods to accurately capture the temporal drift trend and adjust the intensities for reliable downstream analysis. - Stratified Diagnostic Models
Stratified diagnostic models in metabolomics are employed when significant covariate-specific differences in the underlying metabolome preclude the use of a single, unified model. By separately training models for distinct cohorts, such as men and women, we achieve greater diagnostic accuracy.
Get in touch to see how our intelligent analysis platform can redefine your multi-omics results.
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