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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

Documentation

Documentation

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File formats

Primary format: AnnData (.h5ad)

All analysis runs use AnnData as the internal model. Users upload .h5ad directly or convert from other formats on the Data page.

Expected structure:

ComponentPurpose
X or layers['counts']Expression matrix
obsCell or sample metadata (barcodes as index)
varGene metadata (gene symbols in index or gene_symbols column)
obsm['spatial']Required for spatial workflows (x/y coordinates)
obsm['X_umap']Written by clustering; used by UMAP explorer

Upload formats

FormatExtensionConversion
AnnData.h5adDirect ingest
Count matrix.csv, .tsvConvert → processed .h5ad
10x Genomics HDF5.h5Convert
10x MTX bundle.zipConvert (matrix + barcodes + genes)

CSV/10x conversion stores raw counts in layers['counts'] and attempts to restore sample / condition from sidecar metadata when present.

Gene identifiers

Enrichment and pathway tools expect gene symbols (for example HGNC for human). Ensembl IDs or custom prefixes may yield empty enrichment results. Demo fixtures use real HGNC symbols from bundled GO reference sets.

Spatial bundles

Full Visium output folders (H&E images, scale factors, JSON) are not yet ingested natively. Prepare a spatial .h5ad with coordinates in obsm['spatial'], or use the included Visium test fixture.

Export formats

OutputFormat
Figure canvasPDF (client-side export)
DE / enrichment tablesIn-app tables; CSV export planned
Processed checkpoints.h5ad on server disk (not user-downloadable in prototype)

Size guidance

The prototype runs locally with datasets up to a few thousand cells comfortably. Large single-cell and spatial datasets will require cloud storage, lazy loading, and tiled imagery in production — see internal architecture docs.