Welcome
Gradient BioSystems is a web-based workspace for transcriptomic analysis. Upload your data, run guided pipelines, explore results interactively, and review the full history behind every step.
What you can do today
| Modality | Status |
|---|---|
| Single-cell RNA-seq | Full workflow: QC → normalization → clustering → integration/annotation/trajectory → DE or pseudobulk DE → enrichment |
| Single-cell CNV | Chromosome-scale copy-number inference, malignant scoring, subclones, and oncology handoff for tumor single-cell studies |
| Spatial transcriptomics | Domain clustering, spatially variable gene ranking, neighborhood enrichment, niche candidates, spot viewer, gene overlays, and reference deconvolution (Visium-class .h5ad) |
| Biomarker discovery | WGCNA modules, coverage DEG, feature selection, and cross-validated classifiers with ranked gene panels |
| Bulk RNA-seq | Guided Analyze step for filtering, normalization, differential expression, and sample QC/PCA diagnostics |
| AI-assisted interpretation | Grounded narrative summaries for clusters, DE, enrichment, and study-level methods text — citing only computed results |
| Disease evidence | Curated gene-disease association lookup over DE, cluster, enrichment, and biomarker gene sets |
Result pages export TSV tables and per-panel PNG images; a cross-area report composer assembles figures into exportable PDF reports.
How the product is organized
Every analysis lives inside a study (called an experiment in the database). From the dashboard you create or open a study, then work through five sections:
- Data — upload datasets, metadata, design, and contrasts
- Explore — QC charts, UMAP, spatial preview, gene inspection
- Analyze — run pipeline steps (clustering, DE, bulk RNA-seq, biomarker, spatial)
- Interpret — annotations, enrichment, gene sets, methods provenance
- History — job history, snapshots, stale-output warnings
Who this is for
- Wet-lab biologists who need a guided path without writing R or Python
- Computational biologists who want parameter transparency and reproducibility
- Core facility analysts who run repeatable workflows for many labs
Next steps
- Use cases — scenario guides for single-cell, spatial, biomarker, and bulk RNA-seq workflows
- Quick start — run your first single-cell study in minutes
- Key concepts — studies, datasets, runs, and snapshots
- Study workflow — how the six sections fit together