Computational Biology
The Computational Biology area is a web workspace for transcriptomic analysis. Upload single-cell, bulk, or spatial datasets; run guided QC, clustering, integration, annotation, differential expression, pseudobulk, trajectory, enrichment, and biomarker pipelines; explore results interactively; and assemble publication-ready figures — with full provenance for every step.
Start from a research scenario
Guided workflows map common questions to data requirements, analysis steps, and documentation — pick the scenario closest to your study.
Single-cell atlas
Research question: What cell populations exist in this dataset, what genes define each cluster, how do cell states change across trajectory or condition, and how does expression differ between experimental groups with biological replicates?
Build a single-cell RNA-seq atlas from raw or processed data — QC, clustering, scalable sketch analysis, batch integration, cell type annotation, marker identification, pseudobulk differential expression, trajectory, and pathway enrichment with reproducible history.
Read scenario guide
Single-cell CNV and malignant cells
Research question: Which cells, clusters, or samples show broad expression-derived CNV signal consistent with malignant tumor populations, and what inferred events or subclones explain those calls?
Infer chromosome-scale copy-number evidence from tumor single-cell RNA-seq and turn it into reviewable malignant-cell, event, and subclone annotations.
Read scenario guide
Spatial transcriptomics
Research question: What spatial domains exist in this tissue section, which domains co-localize, which genes vary across space, and where are genes of interest expressed relative to tissue architecture?
Analyze Visium-class spatial datasets — cluster tissue domains, identify spatially variable genes, measure neighborhood enrichment, compare domains across conditions, nominate co-localization niche candidates, and explore gene expression in tissue coordinate space.
Read scenario guide
Biomarker discovery
Research question: Which genes best discriminate between sample classes, and how reliably does a classifier predict group membership in cross-validation?
Select predictive gene features from expression data, train cross-validated classifiers, and rank biomarker panels for translational studies.
Read scenario guide
Bulk RNA-seq
Research question: Which genes are differentially expressed between experimental groups, and what are the effect sizes and significance levels after appropriate normalization?
Run differential expression on bulk RNA-seq count matrices with guided filtering, DESeq2-style size factors, TMM normalization metadata, VST, sample QC, PCA/sample-distance diagnostics, and TSV exports.
Read scenario guide
Platform features
Everything available in the Computational Biology workspace today — pipelines, explorers, exports, and provenance.
- Study workspaces with datasets, sample metadata, design tables, and contrasts
- AnnData ingest with assay-role inspection for counts, logcounts, scaled data, protein, and spatial coordinates
- Single-cell RNA-seq QC: genes/cells detected, mitochondrial fraction, doublet scores
- Normalization, scaling, PCA, UMAP, and Leiden/Louvain clustering
- Scalable sketch analysis with backed AnnData, Zarr/Parquet artifacts, projected labels, dedicated results, and large-dataset guardrails
- Batch integration with corrected PCA representations, integrated clustering, before/after comparison views, batch-mixing metrics, and confounding warnings
- Reference-based cell type annotation with compatibility checks, per-cluster score heatmaps, confidence gaps, warnings, and supporting marker genes
- Wilcoxon differential expression with ranked gene tables and volcano views
- Pseudobulk differential expression with saved-contrast prefill, per-cell-type/group volcano views, sample aggregate review, and TSV exports
- Cell-cycle scoring and graph-based pseudotime with UMAP overlays, assumption warnings, pseudotime-associated genes, and TSV exports
- Single-cell CNV and malignant-cell evidence from expression-derived chromosome-scale signal, with reference QC, event calling, subclones, state calls, review overlays, and oncology handoff artifacts
- Reference-based deconvolution with NNLS-estimated cell fractions and fit diagnostics
- GO and pathway enrichment from differential expression results with ORA, ranked GSEA, ssGSEA/GSVA score summaries, custom run-scoped gene sets, and set-operation review
- Biomarker discovery with bounded multi-method feature comparison, inline Open Targets disease evidence, and cross-validated classifier metrics
- Co-expression network module explorer with hub genes, eigengene-trait correlations, lightweight hub-edge graph, and TSV exports
- Coverage-based DEG result review with pairwise contrast details, disease-association evidence, and TSV exports
- Spatial transcriptomics domain clustering, SVG overlays, neighborhood enrichment, condition-domain comparison, niche candidates, and TSV exports for Visium-class `.h5ad` datasets
- Interactive spot viewer with gene expression overlays
- Explore workspace: QC charts, linked gene inspection, UMAP expression panels, spatial preview, and exportable summaries
- Analyze workspace for chaining QC, clustering, integration, annotation, DE, pseudobulk, trajectory, biomarker, and spatial steps
- Interpret workspace for annotations, enrichment, gene sets, and methods provenance
- Disease context lookup mapping a gene signature against ~1.8M target-disease associations across ~24,000 diseases, with overlap enrichment (Fisher's exact, BH-FDR), integrated association scores, evidence-source badges (genetic, somatic, clinical, literature, pathway, expression), and expandable per-gene matched evidence.
- Multi-panel figure builder with PDF export
- Supplement-ready TSV exports for DE, enrichment terms, term genes, biomarker, and spatial result tables
- Run history with pipeline versions, parameters, and stale-output warnings
- Grounded analytical interpretation tied to computed genes, pathways, and metrics
- Guided bulk RNA-seq workflow with filtering, normalization controls, VST, DE, sample QC, PCA/sample-distance diagnostics, and TSV exports
- Async job polling with downloadable artifacts
- Shared organization and user model across all studies