Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Quality control and filtering

statTarget

Correct signal drift and analyze molecular-profile data from single- or multi-batch studies.

Platform-independentR packageOpen source
Access toolSuggest an update

Overview

statTarget provides QC-based random-forest signal correction, batch handling and statistical analysis, with graphical workflow options.

What can this tool do?

QC-based drift correctionBatch-effect correction

Common uses

  • Evaluate analytical drift across injections.
  • Prepare corrected metabolomics tables for statistical comparisons.

Scientific fit

Primary function

Quality control and filtering

Data compatibility

Inputs

Feature or peak table, Sample metadata

Outputs

Feature or peak table, Statistical results, Human-readable report

Input formats

Other, Abundance table, QC and injection metadata

Output formats

Other, Corrected abundance tables and statistics

Scope and considerations

Designed for

  • Evaluate analytical drift across injections.

Not designed for

  • Recovering a biological effect that is completely confounded with acquisition batch.

Important requirements

  • Abundance and sample metadata, including injection order and appropriate QC information for QC-based correction.

Considerations

  • Inspect corrected QC behavior and retained biological variation; correction assumptions differ between methods.

Access and documentation

Primary access

LGPL-3-or-later package; see the package license and vignettes.

Evidence and review

Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. Luan H, Ji F, Chen Y, et al. statTarget: A streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data. Anal Chim Acta 1036, 66-72 (2018). doi:10.1016/j.aca.2018.08.002

Software and documentation citations

Related tools

NOREVAComplementary to · Normalize metabolomics data and compare the performance of alternative normalization strategies.

Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-09-08 · Entry update

    Audit core publications across existing tools

    View change · Entry at this revision
  2. 2026-09-07 · First committed entry

    Add 50 documented metabolomics resources

    View change · Entry at this revision

Full GitHub history