Emergene
Individual cell-based differential transcriptomic analysis across conditions
If your scRNA-seq data have two conditions, e.g., disease and control, and you'd like to know which cells changed the most in the disease condition, then Emergene is the tool for you. Similarly, if you are studying development, and want to understand the emergence of cell type diversity, Emergene can help you with that. Not only scRNA-seq data, but also the spatial transcriptomics data could be used as the input for Emergene.
📖 Documentation
Emergene is a component of PIASO, our Python toolkit designed for single-cell data analysis.
📦 Installation
You could simply install Emergene via pip in your conda environment:
pip install emergene
For the development version in GitHub, you could install via:
pip install git+https://github.com/genecell/Emergene.git
🌟 Key features
- Cell-level analysis: Identify which individual cells change the most between conditions
- Gene pattern detection: Discover genes with coordinated local expression patterns and condition-specific expression patterns
- Multi-condition support: Compare multiple conditions simultaneously
- Spatial compatibility: Works with both scRNA-seq and spatial transcriptomics data
- Local fold changes: Quantify cell-specific expression changes relative to other conditions
- Statistical rigor: Built-in significance testing and background correction for each individual cell
💡 When to Use Emergene
Perfect for:
- 🔬 Disease vs. Control: Identify cells and genes most affected by disease
- 🧬 Developmental Biology: Track emergence of cell type diversity over time
- 🗺️ Spatial Transcriptomics: Discover spatially coordinated expression patterns
- 🧪 Treatment Response: Find cells that respond most to interventions
- 🔄 Cell State Transitions: Detect genes driving state changes
Example Questions:
- Which cells changed the most in disease compared to healthy controls?
- What genes show emergent coordinated patterns in specific developmental stages?
- Which spatial regions show the strongest transcriptional shifts?
- What cell types are most responsive to treatment?
Citation
If Emergene is useful for your research, please consider citing Wu, S.J., Dai, M. et al. Pyramidal neurons proportionately alter cortical interneuron subtypes. Nature (2026). https://doi.org/10.1038/s41586-025-09996-8
Contact
Min Dai dai@broadinstitute.org
Metadata
Release files for emergene 1.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| emergene-1.0.2.tar.gz | 34.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| emergene-1.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 34.1 MB
Release files / emergene-1.0.2.tar.gz
| Download URL | emergene-1.0.2.tar.gz |
|---|---|
| Size | 34.1 MB |
| Tags | Source |
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No |
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twine/6.2.0 CPython/3.9.25
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Release files / emergene-1.0.2-py3-none-any.whl
| Download URL | emergene-1.0.2-py3-none-any.whl |
|---|---|
| Size | 25.6 kB |
| Tags | Python 3 |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.9.25
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