Quantira Analytics supports biotech, pharma, CRO, and academic teams with advanced omics data analysis, robust biostatistics, biomarker strategy, and publication-ready scientific reporting.
Quantira connects experimental mass spectrometry awareness with statistical discipline and translational biomarker thinking — the combination needed when figures may influence manuscripts, partner discussions, or validation decisions.
We combine mass spectrometry knowledge, statistical modeling, and translational interpretation — so your analysis moves beyond p-values into actionable scientific evidence.
End-to-end analysis of proteomics, metabolomics, glycomics, and targeted LC-MS/MS datasets.
Statistical workflows designed for noisy, high-dimensional, small-sample, or outlier-prone datasets.
From candidate discovery to validation planning — prioritizing signals that support real decisions.
Many analytics providers generate plots. Fewer understand the experimental context, LC-MS/MS constraints, statistical assumptions, and how to frame a defensible biological claim. Quantira is built for that bridge.
Interpretation informed by Orbitrap, QTOF, Triple Quad, DIA/DDA, targeted workflows, and LC-MS/MS data behavior.
R-based scripts, structured outputs, QC checks, and transparent reporting for reusable, auditable analysis.
Figures, result tables, methods text, interpretation paragraphs, and reviewer-style methodological justification.
Not just "what is significant?" — but "what is robust, interpretable, and worth validating next?"
Each project can be structured around practical outputs your team can immediately use for reporting, manuscripts, partner discussions, or internal decisions.
Volcano plots, PCA, heatmaps, boxplots, forest plots, QC visuals, and export-ready SVG/PNG formats.
Excel-ready differential abundance tables, ranked biomarkers, annotations, FDR values, and effect-size summaries.
Structured R scripts, clear output folders, run metadata, and transparent preprocessing or modeling steps.
Decision-oriented summaries, methods/results text, limitations, and next-step recommendations.
Flexible, expert-level support for organizations generating complex biological data that need stronger statistical interpretation, sharper figures, or external scientific review.
Fast, high-quality analytics support when internal headcount is limited but investor or partner expectations are high.
Support for translational biomarker programs, exploratory omics studies, and cross-functional evidence packages.
Advanced data interpretation, statistical reporting, and client-facing scientific deliverables that differentiate your offer.
Analysis and visualization support for manuscripts, grants, dissertations, and collaborative research projects.
Representative project types illustrating scope, methodology, and deliverable format. Results are illustrative — real project outputs are shared under confidentiality.
Structured preprocessing, transformation, normalization, PCA, differential abundance analysis, and visualization for patient-derived metabolomics or proteomics datasets.
Designed and implemented a simulation framework to compare OLS and robust regression under outliers, sample-size changes, and signal-strength scenarios.
Custom workflow connecting chromatographic signals, MS/MS fragments, annotation tables, and publication-style visualizations for glycan characterization.
Define the biological question, dataset structure, comparison groups, assumptions, and success criteria.
Inspect data files, metadata, missingness, batch structure, assay characteristics, and potential limitations.
Agree on preprocessing, normalization, statistical models, visualization outputs, and interpretation scope.
Run reproducible scripts, generate QC outputs, figures, tables, and interim interpretation notes.
Final report, code, figures, result tables, and optional manuscript-ready methods/results text.
We do not just analyze omics data. We help scientific teams understand which signals are robust enough to trust, report, and validate.
Quantira Analytics is positioned as specialist support for life science teams that need more than routine plotting: experimental context, biostatistical rigor, biomarker logic, and clean scientific communication.
Send a brief project description and dataset type. We will define the best analytical strategy, expected deliverables, and realistic timeline together.