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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

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Tumor microenvironment

TME analysis runs on the Computational Biology single-cell pipeline. Oncology adds the tumor biology layer — curated cancer reference signatures, cell-state scoring, and cohort comparison — without duplicating compbio clustering infrastructure.

What compbio provides

  • QC, normalization, HVG selection, clustering, and marker identification
  • UMAP exploration and differential expression
  • Spatial transcriptomics when Visium/Xenium data is linked to the study

What oncology adds

  • TME-oriented cell-type annotation using curated cancer reference signatures (tumor, immune, stromal, vascular compartments)
  • Cell-state scoring — exhaustion, activation, differentiation, regulatory suppression — using published oncology gene sets
  • Comparative TME composition across tumor types, treatment conditions, and responder/non-responder groups

Where to run TME analysis

Open the study TME page at /areas/oncology/studies/{id}/tme.

When single-cell data is linked to the study through compbio, launch clustering and annotation from the compbio study workspace, then review TME composition summaries in the oncology view.

Typical workflow

  1. Upload single-cell data via the compbio study linked to your oncology cohort
  2. Run compbio QC, normalization, and clustering
  3. Annotate cell types with oncology reference signatures
  4. Compare TME composition across treatment arms or response groups
  5. Feed annotated expression into Cell-cell communication

Spatial TME

Spatial data ingestion, tile generation, neighborhood analysis, and scRNA-seq deconvolution are handled by the compbio spatial pipeline. Oncology adds:

  • Spatially-resolved immune infiltration, exclusion, and desert phenotype classification
  • Tumor–immune boundary proximity scoring
  • Spatial co-localization of ligand-receptor pairs

Pathology WSI overlays provide tissue morphology context — see the Pathology area documentation.

Next steps