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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

Application

Documentation

Documentation

Back to studies

Overview

Computational Biology workflows connect data ingestion, guided pipelines, interactive exploration, and publication-ready outputs in a single study workspace. Each use case below maps a common research question to platform capabilities, data requirements, and a suggested analysis path.

Use case index

Use caseResearch questionKey capabilities
Single-cell atlasWhat cell types exist and how do they differ between conditions?QC, clustering, UMAP, differential expression, pathway enrichment, figures
Spatial transcriptomicsWhere are genes expressed in tissue and what spatial domains exist?Spatial domain clustering, spot viewer, gene overlays
Biomarker discoveryWhich genes best classify samples and how well does a classifier perform?mRMR feature selection, cross-validated classifiers, ranked gene panels
Bulk RNA-seqWhich genes are differentially expressed between groups?TMM normalization, edgeR-style DE, contrast-based analysis

Who these use cases serve

AudienceTypical goals
Wet-lab biologistsGuided analysis without writing R or Python
Computational biologistsParameter transparency, reproducibility, and provenance
Bioinformatics coresRepeatable client-facing workflows at scale
Translational and biotech teamsDE, biomarker panels, and publication-ready figures
Pharma computational biology groupsStandardized pipelines with audit trails and methods text

Common data requirements

Most use cases start with a study containing:

  • Datasets — .h5ad, 10x outputs, count matrices, or bulk expression tables
  • Metadata — sample and condition columns merged into obs before analysis
  • Contrasts (for DE and biomarker) — group comparisons defined on the Data page

Choosing a starting point

If your primary data is…Start with…
Single-cell RNA-seq (10x, .h5ad)Single-cell atlas
Visium or spatial .h5adSpatial transcriptomics
Labeled samples for classificationBiomarker discovery
Bulk count matrix with group labelsBulk RNA-seq

Study workflow sections

All use cases follow the same left-to-right flow:

Data → Explore → Analyze → Interpret → Figures → Runs

See Study workflow for section details.

What these use cases are not

These workflows support exploratory and translational research. AI interpretation summarizes computed outputs — it does not perform genomics statistics or fabricate metrics. Bulk RNA-seq has a full analysis API; the guided UI for bulk workflows is still expanding.

Next steps