About Gradient Biotech
A multi-area scientific research platform developed by Gradient Biotech LLC
Gradient Biotech brings study management, reproducible compute pipelines, interactive result exploration, and analysis into one shared application across research domains. Researchers can move from raw data to insight without switching between disconnected notebooks, viewers, and job trackers.
Our mission
We build tools that help teams move from raw data to reproducible insight. Every analysis run records parameters, pipeline versions, status, and artifacts so work can be reviewed, repeated, and shared. Gradient Biotech is designed for exploratory and translational research workflows where provenance and transparency matter.
Research areas
Work is organized into six research areas. Each area has its own data types, pipeline workflows, and analysis views while sharing users, organizations, and platform services.
Computational Biology
Transcriptomics from single cells to spatial maps
The Computational Biology area is a web workspace for transcriptomic analysis. Upload single-cell, bulk, or spatial datasets; run guided QC, clustering, differential expression, enrichment, and biomarker pipelines; explore results interactively; and assemble publication-ready figures — with full provenance for every step.
- Study workspaces with datasets, sample metadata, design tables, and contrasts
- Single-cell RNA-seq QC: genes/cells detected, mitochondrial fraction, doublet scores
- Normalization, scaling, PCA, UMAP, and Leiden/Louvain clustering
- Wilcoxon differential expression with ranked gene tables and volcano views
- GO and pathway enrichment from differential expression results
- Biomarker discovery with mRMR feature selection and cross-validated classifiers
- Spatial transcriptomics domain clustering for Visium-class `.h5ad` datasets
- Interactive spot viewer with gene expression overlays
Cardiology
Multimodal cardiovascular and physiological signal research
The Cardiology area is a web workspace for multimodal cardiovascular and physiological signal research. Upload ECG, RR intervals, blood pressure, PPG, and respiration recordings; run validated preprocessing and metrics pipelines; link data to subjects; compare cohorts; and generate AI-assisted interpretations grounded in computed metrics.
- Experiment containers for cardiovascular signal studies
- ECG upload in WFDB, CSV, and EDF waveform formats
- ECG preprocessing with R-peak detection, RR intervals, and signal quality index
- Time-domain, frequency-domain, and nonlinear HRV metrics
- Full ECG analysis pipeline chaining preprocess and HRV in one run
- RR-only CSV upload for Holter and wearable exports
- Arrhythmia burden metrics: irregularity percentage, pause burden, and beat flags
- PPG preprocessing with pulse peak detection and inter-beat intervals
Immunology
Immune cell states, signaling networks, and repertoire dynamics
The Immunology area is a research workspace for immune-system datasets and translational immunology studies. Build on computational-biology single-cell and spatial workflows, add immune-specific annotation and state scoring, organize disease cohorts, and prepare for repertoire, cytokine, and communication analyses with reproducible run records.
- Study containers for autoimmune, infectious disease, vaccine, allergy, and immunotherapy research
- Immune cell annotation and fine-grained immune hierarchy workflows
- Immune state scoring for exhaustion, activation, effector function, cytotoxicity, and regulatory suppression
- CITE-seq, VDJ, spatial, ATAC, FCS, cytokine, bulk RNA-seq, and single-cell dataset metadata
- Disease context and cohort metadata for timepoints, medication, vaccination, infection history, and response labels
- Canonical home for immune repertoire analysis shared with oncology workflows
- Canonical home for immune cell-cell communication and cytokine network methods shared with oncology
- Run history for immune analysis parameters, artifacts, model versions, and reproducible outputs
Neurology
Brain connectivity, EEG, and fMRI in one neuroscience workspace
The Neurology area is a web workspace for brain connectivity, functional imaging, and electrophysiology research. Upload connectivity matrices, EEG, or fMRI data; compute graph-theoretic network metrics; explore interactive brain visualizations; compare cohorts with Network-Based Statistics (NBS); and generate AI-assisted interpretations grounded in computed outputs.
- Study containers for connectivity, EEG, and fMRI projects
- Connectivity matrix upload in CSV, NPZ, NumPy, and MATLAB `.mat` formats
- Brain Connectivity Toolbox graph metrics: clustering, modularity, and efficiency
- Small-worldness, characteristic path length, and centrality measures
- Network heatmap, graph layout, and 3D brain views when atlas coordinates exist
- EEG preprocessing: bandpass, notch, re-reference, and bad-channel handling
- Optional ICA for artifact reduction in EEG recordings
- EEG band power, spectral entropy, and PLV/coherence/wPLI connectivity
Oncology
Tumor microenvironment, immune response, mutations, and outcomes
The Oncology area is a research workspace for tumor cohorts and translational oncology studies. Organize samples, clinical endpoints, mutation and repertoire data; run tumor microenvironment, immune phenotype, communication, mutation, and survival workflows; and interpret computed results with provenance.
- Study containers for tumor cohorts, treatment arms, response groups, and timepoints
- Sample and clinical metadata management with patient/sample identifiers
- Tumor microenvironment composition analysis from expression-derived cell states
- Cell-cell communication using ligand-receptor scoring and pathway aggregation
- Immuno-oncology profiling with deconvolution, TIDE-like phenotype, exhaustion, and repertoire metrics
- Mutation landscape analysis with TMB, signatures, oncoprint summaries, and pathway enrichment
- Survival analysis with Kaplan-Meier, log-rank tests, Cox regression, and longitudinal trajectories
- Grounded analytical interpretation tied to oncology pipeline outputs and cited metrics
Pathology
Computational pathology and whole-slide imaging research
The Pathology area is a web workspace for computational pathology and whole-slide imaging research. Upload tissue slides or region images; generate tile manifests for browser viewing; run tissue detection, cell segmentation, and spatial quantification; inspect overlays in the slide viewer; and track reproducible pipeline runs.
- Study containers for whole-slide imaging and region-image projects
- Slide upload for TIFF, OME-TIFF, PNG, JPEG, and scanner-format files
- Multi-level tile generation with thumbnail and pyramid manifests for web viewing
- Tissue detection producing masks, region bounding boxes, and focus QC summaries
- Cell and nucleus segmentation with overlay PNG and structured cell tables
- Spatial quantification: cell density heatmaps across tissue regions
- Nearest-neighbor distance statistics between detected cells
- Infiltration phenotype scoring and region-level metric summaries
How work moves through the platform
- Choose a research area — Each domain has dedicated dataset types, pipeline workflows, and analysis views under one shared organization and user model.
- Create a study — Organize datasets, sample metadata, subjects, design variables, and analysis runs in a reproducible study container.
- Run pipelines — Launch compute jobs for QC, clustering, differential expression, graph metrics, HRV, tissue segmentation, survival analysis, and cohort summaries.
- Explore and interpret — Review dashboards, interactive plots, slide viewers, run artifacts, and grounded summaries tied to recorded outputs.
Platform architecture
Gradient Biotech splits responsibilities across two backend services and a unified web interface. Metadata APIs handle users, organizations, studies, datasets, uploads, and run records. A separate analysis service runs compute pipelines asynchronously and stores result artifacts. The frontend coordinates both services with job polling and interactive viewers.
- Metadata service — study and dataset management, file upload metadata, run history, and shared user/organization models
- Analysis service — scientific pipelines, async jobs, and downloadable artifacts for every research area
- Web application — dashboards, pipeline launchers, explorers, and interpretation views in the browser
What the platform provides
- One shared workspace — users, organizations, datasets, jobs, and provenance in a single application
- Area-specific pipelines — transcriptomics, cardiovascular signals, brain connectivity, whole-slide imaging, tumor cohorts, and immune repertoire workflows
- Reproducible runs — parameters, versions, status, and output artifacts captured on every job
- Interactive scientific outputs — UMAPs, brain networks, slide viewers, differential expression tables, waveforms, HRV metrics, survival curves, and cohort comparisons
- Grounded interpretation — summaries tied to structured analysis outputs and cited metrics, not free-form generated claims
- Documentation — getting-started guides and methods references for each research area
Getting started
Open the research dashboard to choose a research area and create your first study, or browse the documentation for area-specific workflows and pipeline details.
Contact
Questions about Gradient Biotech may be sent to contact@gradientbio.tech.