Immunology
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 run repertoire, cytokine, cell-cell communication, and ligand-to-target hypothesis analyses with reproducible run records.
Start from a research scenario
Guided workflows map common questions to data requirements, analysis steps, and documentation — pick the scenario closest to your study.
Immune atlas and annotation
Research question: Which immune populations and states are present, how confident are the reference-atlas labels, and which cells need review before downstream repertoire, communication, or disease workflows?
Use this workflow when you need to identify immune populations, assign reference-atlas labels, and score immune states before downstream analysis. It is usually the first use case for single-cell or CITE-seq immune studies because communication, repertoire, disease, and reporting workflows all depend on credible cell labels.
- Annotation
- state scoring
- composition
Read scenario guide
Repertoire diversity
Research question: Which clones are expanded, shared, phenotype-linked, or ambiguous, and how do repertoire diversity and CDR3 sequence features vary across samples, tissues, timepoints, or disease groups?
Use this workflow to study TCR/BCR clonotypes, expansion, diversity, sequence features, and clone-state relationships. It supports both bulk repertoire review and single-cell VDJ analysis where barcode-level clone assignments need to be connected to immune phenotype, tissue, timepoint, or disease context.
- TCR/BCR clonotypes
- diversity
- overlap
Read scenario guide
Cytokine signaling
Research question: Which sender/receiver pairs and cytokine pathways dominate immune signaling, and which signaling axes differ by disease, treatment, timepoint, or response group?
Use this workflow to summarize immune signaling between sender and receiver cell populations. It combines ligand-receptor scoring, pathway aggregation, permutation-aware evidence, sender/receiver role summaries, and optional ligand-to-target follow-up when signaling needs a mechanistic hypothesis.
- Communication
- cytokine networks
- pathway deltas
Read scenario guide
Ligand-to-target hypotheses
Research question: Which ligands from candidate sender populations best explain receiver target genes, and which ligand-receptor-target chains should be prioritized for follow-up?
Use this workflow when communication results need a mechanistic explanation. It ranks candidate sender ligands against receiver target-gene programs and returns ligand-target and ligand-receptor-target evidence that can be reviewed alongside cell-cell communication outputs.
- Ligand activity ranking
- target genes
- ligand-receptor-target paths
Read scenario guide
Spatial immune neighborhoods
Research question: Which immune cell neighborhoods are enriched in tissue, where do TLS-like regions appear, and how do cytokine or immune-state gradients vary by region?
Use this workflow to study immune organization in tissue coordinate data. It turns cell or spot coordinates into graph neighborhoods, neighborhood enrichment, TLS-like candidate records, region summaries, and cytokine or immune-state gradients that can be interpreted with disease context.
- Graph neighborhoods
- enrichment
- TLS criteria
- cytokine gradients
Read scenario guide
Disease cohort comparison
Research question: How do immune composition, state trajectories, spatial neighborhoods, and disease metadata vary across disease contexts, treatment groups, or response labels?
Use this workflow to compare immune profiles across disease contexts, treatments, timepoints, response labels, and spatial tissue patterns. It connects immune composition, trajectory summaries, graph-based spatial neighborhoods, TLS-like regions, and cohort metadata in one disease-oriented analysis.
- Cohort metadata
- composition
- trajectory
- spatial patterns
Read scenario guide
Multi-modal profiling
Research question: How do protein markers, paired RNA/protein signals, chromatin accessibility, cytometry clusters, and optional multimodal dependencies support or challenge immune phenotype assignments?
Use this workflow when immune phenotypes are supported by protein, chromatin accessibility, cytometry, or multiome data. It brings CITE-seq RNA/protein summaries, scATAC peak-matrix summaries, FlowSOM-style cytometry clustering, and dependency-gated multimodal outputs into one review path.
- CITE-seq
- ATAC
- multiome
- cytometry
Read scenario guide
Platform features
Everything available in the Immunology workspace today — pipelines, explorers, exports, and provenance.
- Study containers for autoimmune, infectious disease, vaccine, allergy, and immunotherapy research
- Immune composition summaries with population counts, proportions, group summaries, and cohort context
- Reference-atlas immune label transfer with probability scores, confidence categories, competing labels, marker evidence, compatibility checks, and fine-grained immune hierarchy workflows
- Immune state scoring for exhaustion, activation, effector function, cytotoxicity, and regulatory suppression
- Cytometry event analysis with arcsinh transform, self-organizing-map prototype clustering, metaclusters, minimum-spanning-tree edges, marker heatmaps, and sample abundance summaries
- CITE-seq, VDJ, spatial, ATAC, FCS, cytokine, bulk RNA-seq, and single-cell dataset metadata
- CITE-seq RNA/protein integration with ADT background correction, joint coordinates, RNA/protein discordance, and CD-marker phenotype panels
- scATAC tabular peak analysis with cell QC, TF-IDF/LSI coordinates, marker peaks, gene activity, peak-to-gene links, group coverages, and motif summaries
- Disease context and cohort metadata for timepoints, medication, vaccination, infection history, and response labels
- Canonical home for immune repertoire analysis with diversity, clone expansion, CDR3 length/property/k-mer summaries, V/J pairing, sample distances, barcode clone maps, clone occupancy, clonal bias, VDJ ambiguity flags, and public/private clones shared with oncology workflows
- Canonical home for immune cell-cell communication and cytokine network methods shared with oncology
- Complex-aware ligand-receptor scoring with permutation p-values, adjusted p-values, specificity scores, pathway aggregation, and sender/receiver role summaries, plus a differential-communication table across conditions (per-condition scores, log2 fold change, and condition-label permutation FDR), visualized as an interactive cell-type communication network (per-pathway filter, role-grouped nodes, responder vs non-responder edge coloring), a sender→pathway→receiver flow (Sankey), a pathway-activity heatmap (sender→receiver pairs × signaling pathways), and per-cell-type communication fingerprints (role, hub score, distinct out/in partners, dominant sending/receiving pathways) with graph-centrality hub analysis — betweenness (broker), eigenvector (influence), and signaling entropy (specialist vs broadcaster)
- Communication-motif detection over the directed signaling graph — reciprocal loops (feedback when both arms share a pathway, cross-regulatory when each direction uses a different pathway), feed-forward loops, and 3-cycles, each pathway-annotated and ranked by its limiting-edge strength — plus a drug-targeting overlay that annotates ligand/receptor genes with Open Targets tractability (druggable modality: small molecule, antibody, PROTAC) and safety liabilities (research evidence, not a treatment recommendation)
- Ligand activity inference workflow with sender/receiver selection, receiver target-gene sets, ligand rankings, ligand-target matrices, and ligand-receptor-target paths
- Graph-based spatial immune workflows with neighborhood enrichment, TLS-like candidate criteria, cytokine/state gradients, region summaries, and spatial edge payloads, with an interactive tissue scatter map (cells by type and neighbor density) and a cell-type neighborhood-enrichment heatmap
- Run history for immune analysis parameters, artifacts, model versions, and reproducible outputs
- Disease evidence lookup mapping immune signature gene sets against ~1.8M target-disease associations across ~24,000 diseases, with overlap enrichment (Fisher's exact, BH-FDR), integrated association scores, datasource counts, and evidence-type provenance (genetic, clinical, literature)
- Report generation with methods text, tables, figure specs, provenance, and grounded immune-analysis outputs
- Grounded analytical interpretation for immune cell states, signaling, repertoire, and disease context