Transcriptomics from single cells to spatial maps
The Computational Biology area is a web workspace for transcriptomic analysis. Upload single-cell, bulk, or spatial datasets; run guided QC, clustering, differential expression, enrichment, and biomarker pipelines; explore results interactively; and assemble publication-ready figures — with full provenance for every step.
Use cases
Guided workflows map common questions to data requirements, analysis steps, and documentation — pick the scenario closest to your study.
Research question: What cell populations exist in this dataset, what genes define each cluster, and how does expression differ between experimental conditions?
Build a single-cell RNA-seq atlas from raw or processed data — QC, clustering, marker identification, differential expression, and pathway enrichment with publication-ready figures.
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Research question: What spatial domains exist in this tissue section, 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, and explore gene expression in tissue coordinate space.
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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.
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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 TMM normalization and edgeR-style statistical testing.
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Capabilities
Everything available in the Computational Biology workspace today — pipelines, explorers, exports, and provenance.