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Research Areas

Computational BiologySingle-cell, bulk RNA-seq, spatial, and biomarkers
CardiologyCardiovascular signals, cohorts, and interpretationImmunologyImmune cell states, signaling networks, and repertoire dynamicsNeurologyBrain connectivity, functional imaging, and electrophysiologyOncologyTumor microenvironment, immune response, mutations, and survivalPathologyWhole-slide imaging, tissue quantification, and spatial pathology

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Research areas

Research areas are the top-level organizational unit in Gradient Biotech. Each area has its own data types, analysis pipelines, and views, all under one shared platform.

Switching areas

Use the Research Areas dropdown in the top navigation bar. Selecting an area takes you to that area's dashboard. Area-specific menu items appear on the right side of the navbar while you are in that area.

Computational Biology

Covers transcriptomic and genomic studies: single-cell RNA-seq, bulk RNA-seq, spatial transcriptomics, and biomarker discovery. Work is organized into studies. Each study has six sections:

  1. Data — upload datasets, map metadata, define experimental design and contrasts
  2. Explore — QC charts, UMAP embeddings, spatial previews, gene inspection
  3. Analyze — run pipeline steps (clustering, DE, enrichment, biomarker, spatial)
  4. Interpret — cluster annotations, pathway enrichment, AI-assisted summaries
  5. Figures — multi-panel figure canvas with PDF export
  6. Runs — full job history, parameters, snapshots, stale-output management

Cardiology

Covers cardiovascular signal research: ECG, RR-interval, blood pressure, and PPG recordings. Work is organized into studies. Studies contain datasets and pipeline runs for signal preprocessing, HRV computation, cohort comparison, and AI-assisted interpretation.

Immunology

Covers immune profiling research: immune composition, annotation and state scoring, TCR/BCR repertoire analysis, cell-cell communication, cytokine signaling, trajectory and spatial immune workflows, disease cohort comparison, and multi-modal profiling. Work is organized into studies with datasets, run history, analytical interpretation, and reproducible reports.

Neurology

Covers brain connectivity, functional imaging, and electrophysiology research. Work is organized into studies. Upload connectivity matrices, EEG, or fMRI data; run graph metrics, functional connectivity, and cohort comparison (NBS); explore network visualizations; and generate AI-assisted interpretations grounded in computed metrics.

Pathology

Covers computational pathology and whole-slide imaging research. Work is organized into studies. Upload slides or region images; generate tile manifests; run tissue detection, cell segmentation, and spatial quantification; inspect results in the slide viewer; and track reproducible pipeline runs.

Oncology

Covers multi-omic, spatial, and clinical oncology research. Work is organized into studies. Organize tumor cohorts, samples, clinical endpoints, mutation and repertoire data; run tumor microenvironment, cell-cell communication, immuno-oncology profiling, mutation landscape, and survival workflows; and generate AI-assisted interpretations grounded in computed outputs.