Computational Biology
The Computational Biology area is a web workspace for transcriptomic analysis. Upload single-cell, bulk, or spatial datasets — including Seurat and SingleCellExperiment objects; run guided QC (doublets, ambient RNA, dissociation stress), clustering, integration, annotation, composition testing, differential expression, pseudobulk, trajectory, RNA velocity, enrichment, and biomarker pipelines; compare sample-preparation methods; 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 — sample-quality QC (doublets, ambient RNA, dissociation stress), clustering, scalable sketch analysis, batch integration, cell type annotation, marker identification, pseudobulk differential expression, composition testing, trajectory, RNA velocity, 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
- Direct import of Seurat (v3–v5, including split layers) and SingleCellExperiment .rds / .rda files, read in Python without R, keeping metadata, reductions, and identities
- 10x Cell Ranger .h5 and MTX zip ingest, with optional per-cell metadata and embedding files carried into the dataset
- Single-cell RNA-seq QC: genes/cells detected; mitochondrial, ribosomal, and hemoglobin fractions; per-sample doublet detection (Scrublet-style score, plus an experimental scDblFinder-style classifier) with optional removal; and assay-type awareness for cells, nuclei, and fixed cells
- Dissociation-stress and cell-death QC: heat shock, apoptosis, necrosis, senescence, unfolded protein response, oxidative stress, DNA damage, and immediate-early dissociation signatures, scored per cell and summarised by sample
- Ambient RNA estimation and correction from the raw droplet matrix (SoupX-style) or the filtered matrix alone (decontX-style), with corrected counts feeding the downstream steps
- 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 (Harmony or fastMNN) with integrated clustering, before/after comparison views, batch-mixing (iLISI) and cell-type preservation (cLISI) metrics, and confounding warnings
- Cell type annotation from organism-aware marker panels (blood, adrenal, pituitary, thyroid, testis), custom markers, or a labelled reference dataset (SingleR-style), with compatibility checks, confidence gaps, and marker sources
- Cell composition testing between conditions across biological replicates (propeller-style transformed proportions with limma-style moderated t / F tests), with a per-sample proportion chart
- RNA velocity (stochastic and steady-state models) from velocyto .loom or spliced/unspliced counts, drawn on the UMAP with gene fits, coherence, and spliced-fraction QC that separates nuclei from whole cells
- Sample-preparation benchmarking (enzymatic, fixed cells, nuclei): cell recovery, quality and stress metrics with sample-level tests, composition, marker preservation, and DE between methods with technical genes flagged
- 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, visualized with an interactive genome-position-aware heatmap (real chromosome axis and zoom), 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, using the study organism's GO biological process and Reactome collections (human, mouse, rat)
- 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
- On-demand 3D protein structure card for genes in gene inspection and differential expression results: the study organism's (human, mouse, rat) reviewed UniProt protein resolved server-side and shown on its AlphaFold model with confidence colouring and curated domains, plus experimental complexes with any partner protein (e.g. CDK8–CCNC) and their interface residues
- Analyze workspace for chaining QC, ambient RNA correction, clustering, integration, annotation, trajectory, RNA velocity, DE, pseudobulk, composition, preparation-method comparison, biomarker, and spatial steps, with inline progress and errors under each step
- 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, composition, preparation-method metrics, velocity gene fits, 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