PROTEOMICS · METABOLOMICS · BIOSTATISTICS

Proteomics and metabolomics data analysis for biomarker studies.

Already have omics data? QuantiraOmics provides expert statistical analysis, biological interpretation and candidate prioritization—from quality control to a clear plan for what to validate next.

Discuss Your Dataset See Services & Deliverables

What the analysis covers quality control · statistics · interpretation

Three parts of a biomarker data analysis

Data quality Preprocessing Batch effects

Data preparation & QC

We examine data quality and prepare the dataset using choices appropriate for the platform, study design, and scientific question.

Sample-level and feature-level quality checks
Missing-data and normalization strategy
Batch effects and outlier assessment
Documented preprocessing workflow

Outcome: an analysis-ready dataset with key risks documented.

Study design Effect sizes Multiple testing

Statistical analysis

We select statistical methods that match the design and quantify the strength, uncertainty, and stability of the findings.

Differential and multivariable analysis
Covariate and confounder assessment
Effect sizes and multiple-testing control
Outlier and sensitivity analysis

Outcome: statistical results that can be traced and challenged.

Candidate ranking Biological context Validation

Biomarker interpretation

We connect the statistical findings with biological context and determine which candidates have enough evidence to advance.

Evidence-strength and candidate ranking
Biological and pathway context
Shortlisting for follow-up studies
Validation requirements and limitations

Outcome: a prioritized candidate list and next-step plan.

Services

Data analysis and biostatistics for
proteomics and metabolomics studies.

QuantiraOmics works with data generated by your laboratory, core facility, or service provider. Choose a complete analysis, biomarker-focused interpretation, or independent review of existing results.

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Start here · Complete analysis

Omics Data Analysis & Biostatistics

Expert statistical analysis of existing proteomics, metabolomics, glycomics, and targeted LC-MS/MS datasets, tailored to the study question and design.

Data cleaning, preprocessing, normalization, and quality control
Univariate, multivariable, and differential analysis with multiple-testing control
Figures, result tables, reproducible R workflow, and scientific report

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Biomarker-focused

Biomarker Candidate Analysis

Determine which candidate signals are statistically credible, biologically relevant, and strong enough to take into a follow-up study.

Effect sizes, candidate ranking, and evidence-strength assessment
Biological interpretation, pathways, and multivariable panels where justified
Validation criteria, sample size, study design, and go/no-go recommendations

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Existing results

Independent Review & Reanalysis

An independent assessment of an existing dataset, analysis, report, or manuscript when the results need to be checked, corrected, or strengthened.

Review of preprocessing, models, assumptions, and scientific claims
Reanalysis using appropriate conventional and robust statistical methods
Evidence gaps, correction priorities, and publication-focused recommendations

Analysis Examples

Omics analysis and biostatistics in practice.

Explore seven anonymized, interactive examples showing how complex data and analytical choices are converted into clear scientific conclusions.

Metabolomics Robust statistics Simulation

When regression method choice changes the evidence

An interactive methods case comparing standard and robust regression across cohort scales, signal densities, and realistic noise conditions.

Type I error, power, and false-positive control
Bias, variance, MSE, and interval coverage
Method stability under realistic contamination

Open interactive methods case

Proteomics Biomarker discovery Targeted verification

From discovery proteomics to a verified candidate shortlist

An anonymized tissue-proteomics case showing how paired discovery data were filtered, interpreted, and taken into targeted verification in an independent sample set.

Paired-tissue design preserved in discovery analysis
Candidates prioritized by effect, biology, and assay suitability
Targeted SRM/MRM tested independent evidence

Open interactive proteomics case

Proteomics Data workflow Reproducibility

From search results to a reproducible proteomics analysis

An interactive workflow case showing how heterogeneous proteomics outputs are standardized, resolved to proteins, quantified, statistically analyzed, and prepared for transparent reuse.

Search outputs converted into consistent evidence structures
Protein inference and quantification decisions documented
Expression results packaged for review and publication

Open interactive workflow case

Proteomics Disease progression Tissue validation

From proteome-wide screening to a progression-linked pathway

An anonymized tissue-proteomics case showing how ordered histological grades, pathway context, and an independent tissue method were combined to assess progression-associated candidates.

Protein abundance compared across ordered tissue grades
Pathway-linked candidates prioritized beyond single hits
Independent tissue evidence tested progression and localization

Open interactive progression case

Proteomics Data integration Targeted LC–MS

Integrating discovery and targeted proteomics

An anonymized mixed-species case showing how complementary proteomics datasets were quality-controlled, separated, integrated, and interpreted to distinguish shared from system-specific responses.

Discovery, enrichment, and targeted datasets harmonized
Species-specific peptides separated and orthologs mapped
Pathway findings checked against independent evidence

Open interactive integration case

Biomarker proteomics Longitudinal analysis Targeted LC–MS

Tissue-to-biofluid biomarker translation

An anonymized biomarker-proteomics case showing how tissue discovery signals were prioritized, verified with a multiplex targeted assay, and followed across serial biofluid samples.

Discovery evidence prioritized candidates for follow-up
Targeted measurements connected tissue and biofluid evidence
Longitudinal profiles distinguished early, persistent, and matrix-specific signals

Open interactive translation case

Glycomics LC–MS/MS Evidence review

From glycan assignments to reviewable evidence

An interactive glycomics case showing how composition assignments, chromatographic profiles, and structural claims were checked before biological interpretation.

Identification and retention evidence reconciled
Composition-level profile differences reviewed
Structural limits and next validation steps made explicit

Open interactive glycomics case

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About QuantiraOmics

Data analysis with scientific context.

QuantiraOmics is a Germany-based consultancy providing data analysis and biostatistics for proteomics, metabolomics, glycomics, and targeted LC-MS/MS studies. The work combines statistical analysis, LC-MS/MS domain knowledge, biological interpretation, and biomarker validation planning.

Scientific leadership: Senior scientific oversight across omics analysis, biostatistics, LC-MS/MS interpretation and regulated research environments.

Who we work with

Academic groupsBiotech startupsCROs & service labsPharma R&D

Discuss Your Dataset →

Start a project

Do you have proteomics or metabolomics data to analyze?

Send the dataset type, study design, and scientific question. QuantiraOmics will review what you have, define the appropriate analysis, and explain the expected outputs before the project begins.

Email: info@quantiraomics.com

Location: Germany · Available internationally

Focus: Proteomics · Metabolomics · Biostatistics · Biomarker Analysis