Cytogence Atlas Omics
A professional-grade platform for spatial omics and multi-omics analysis. Load your data, run publication-ready analyses, and generate client-ready reports — all from a single desktop application, no coding required.
GeoMx-Native
Plug-and-play DCC/PKC import. No preprocessing scripts.
Full Provenance
Every output traceable to its data, parameters, and code version.
Publication-Ready
Static and interactive figures. Export as SVG, PDF, or PNG.
No Coding Required
Full GUI. Download, install, analyze. No R or Python setup.
End-to-end spatial and multi-omics analysis
From raw data import through publication-ready figures and client deliverables — one platform for the entire workflow.
Smart data ingestion
Atlas automatically detects data layout, transposes matrices when needed, merges metadata, and identifies column types. Just drop your files in.
GeoMx DSP Native Import
Direct DCC + PKC file import. Parses probe kits, builds expression matrices, extracts QC metrics — no preprocessing scripts needed.
Universal Expression Matrices
CSV, TSV, and TXT with smart detection. Auto-identifies genes-as-rows vs. samples-as-rows and transposes when needed.
File Integrity Tracking
SHA256 hashing, file size, row/column counts, and format detection for every imported dataset.
Research-grade analysis modules
A growing library of analysis modules powered by Python's scientific stack — NumPy, SciPy, scikit-learn, scanpy, and gseapy — accessible through a clean graphical interface.
Differential Expression
Parametric (t-test) and non-parametric (Mann-Whitney U) testing with Benjamini-Hochberg FDR correction. Automatic volcano plots.
Cross-Compartment Analysis
Correlate features across spatial compartments — tumor vs. stroma, epithelial vs. immune — with Manhattan-style visualization.
Pathway Enrichment
EnrichR ORA and pre-ranked GSEA across KEGG, Reactome, GO, MSigDB Hallmark, and WikiPathways — with dot plot visualization.
Correlation & Stratification
Pearson and Spearman correlation with full matrix, target-specific, and group-stratified modes. Clustered heatmaps included.
Dual-Render Figures
Every analysis generates both static (matplotlib, publication-ready) and interactive (Plotly, zoomable/hoverable) figures simultaneously.
Report Builder
Drag-and-drop blocks — text, figures, tables, headings — into curated reports. Export as self-contained HTML, print to PDF.
Data Inventory Browser
Tree view of every dataset, analysis run, and output. Click any result to trace it back to its source data, parameters, and code version.
Composable Pipelines
Use outputs from one analysis as inputs to the next. Build analysis chains — differential expression to pathway enrichment to report — with full traceability.
Desktop + Server
Run as a native desktop app (Windows, macOS, Linux) or deploy as a headless server for shared lab access via any browser.
Bundled Environment
Python scientific stack included. No R, Python, or conda installation required. Download, install, analyze.
Built for spatial omics workflows
Atlas was designed around the real-world workflows of spatial omics research — from GeoMx DSP data import through cross-compartment analysis and client-ready deliverables. Future releases will expand support to 10x Visium, CosMx SMI, Xenium, and single-cell platforms.
How Atlas fits in your workflow
Atlas fills the gap between raw spatial data and publishable results — the step where most teams spend weeks writing custom scripts.
Custom R/Python scripts. Manual file tracking. Copy-paste into PowerPoint. Results not reproducible by collaborators.
Import data, run analyses, generate figures, build reports. Every output traceable. Share projects with collaborators.
Publication-ready figures. Client-ready reports. Full provenance. Reproducible analyses. Weeks saved per project.
Interested in Atlas Omics?
Atlas is currently in active development. Request early access to be among the first to try it, or get in touch to discuss how it can fit your spatial omics workflow.