Evidence report

LC-MS metabolomics · method sensitivity · simulation

When method choice changes the evidence.

An anonymous project demonstration of how analytical findings can be stress-tested before a biomarker result is advanced. The question is not only whether a signal is significant, but whether the inference remains credible under realistic noise and estimator choices.

Explore the resultsSee the decision logic
TargetedLC-MS metabolomics
QCpredefined data checks
9regression approaches compared
Multistress-test scenarios
Anonymousmodified demonstration values

Study design

From measured metabolites to an evidence decision.

The anonymous example combines an illustrative targeted-metabolomics analysis with modified data-driven simulations. This separates a result that looks convincing in one dataset from a method that behaves predictably under controlled conditions.

01 / QC

Clean and transform

Features above a predefined missingness threshold were removed. Remaining values were imputed, log2 transformed, and quantile normalized.

02 / ESTIMATE

Compare methods

Regression method 1 was compared with eight robust approaches across the retained metabolite-level models.

03 / STRESS TEST

Vary the conditions

Cohort scale, noise inflation, and the density of differential signals were changed across a structured simulation grid.

04 / DECIDE

Balance the evidence

False-positive control, power, bias, MSE, variance, and interval coverage were considered together.

Interactive simulation explorer

Change the scenario. Watch the method trade-offs.

The plotted values are modified for this anonymous demonstration while preserving the analytical pattern. Select the performance measure, signal density, and added noise. Click method labels to show or hide a series.

Statistical power by cohort scale

Sparse signals · lower noise

PROPORTION

Inference check

A stronger result controls error without hiding the trade-off.

The null simulations test whether nominal significance behaves as expected. The decision panel translates the modified findings into a practical sensitivity-analysis question.

Type I error calibration

Illustrative mean false-positive rate under the null. The cyan marker indicates the nominal 0.05 level; displayed values are modified for this anonymous example.

0.030.050.07

One estimator showed mild inflation, while Regression method 4 was slightly conservative. Calibration should be read together with power and interval coverage.

Decision lens

Select the dataset condition that most closely matches the analysis under review.

Biological insight add-on

From top regulated candidates to testable cancer hypotheses.

This interface links the top regulated metabolites to plausible cancer-relevant biology while masking every metabolite name and cancer type. Select an anonymous candidate to review the proposed biological interpretation.

AI-assisted pathway mapping with expert reviewHypothesis-generating
Biological interpretation

Candidate 1 · energy metabolism

Candidate 1 is consistently up-regulated across the retained analyses. Its masked biochemical class is compatible with altered energy metabolism reported in cancer biology, but the association does not establish tumor origin or mechanism.

Observed pattern

Up-regulated with a stable direction across defensible regression methods and sensitivity conditions.

Cancer relevance

Altered substrate use and energy handling can accompany cancer-cell growth and adaptation to changing tissue conditions.

Evidence gap

The masked identity prevents pathway-specific inference, and a circulating signal may reflect host response, diet, organ function, or treatment effects.

Next validation

Confirm Candidate 1 in an independent cohort, reveal its identity only within the controlled review, and test the proposed pathway with targeted quantitation.

The AI-assisted layer organizes plausible biological links. Expert review is required to check pathway specificity, confounding, literature support, and whether the statistical signal is suitable for experimental follow-up.

What this demonstrates

Independent review turns a model output into an evidence judgment.

A single preferred method is rarely the whole answer. Review has to connect preprocessing, estimator behavior, uncertainty, and the biological claim being made.

01
Preprocessing and QC risk

Check filtering, missing-data handling, normalization, and sensitivity to analytical choices.

02
Stability across methods

Compare estimates and conclusions under defensible standard and robust alternatives.

03
Inference calibration

Examine false-positive control, power, bias, estimation error, and confidence interval behavior.

04
Evidence gaps and next validation

State what is reliable, what needs correction, and what should be tested next.

Demonstration scope

Visible limitations without disclosing the original study.

This public-facing report is an anonymous analytical demonstration. Sample counts, feature counts, disease identity, labels, and exact outputs are withheld or modified.

Anonymous project basis

The example represents a targeted LC-MS metabolomics method-comparison project. It retains the general analysis workflow and qualitative method behavior while removing or altering study-specific information.

Important scope limitations
  • The displayed values are illustrative and must not be interpreted as patient-level, clinical, or client results.
  • Simulation findings depend on the data-generating mechanism, cohort scale, noise structure, and effect pattern chosen for the review.
  • The biological map is hypothesis-generating and does not establish pathway activity, mechanism, biomarker validity, or clinical utility.
  • No universal best regression method is implied; conclusions must be checked on the dataset under review.
How to read the recommendation

Regression method 2 is shown as a practical sensitivity analysis when moderate heterogeneity or outlier influence is plausible. Its interval behavior still needs to be reported. A real review would compare defensible methods on the project dataset, document whether the conclusion changes, and separate statistical stability from biological plausibility.

All study-identifying and exact empirical details have been removed or modified for this website demonstration.