Plate II.7
Open-source tools
- Mapping cells through time and space with moscot2025
- An open-source framework for end-to-end analysis of electronic health record data2024
- anndata: Access and store annotated data matrices2024
- Best practices for single-cell analysis across modalities2023
- Predicting cellular responses to complex perturbations in high‐throughput screens2023
- Benchmarking atlas-level data integration in single-cell genomics2021
- Mapping single-cell data to reference atlases by transfer learning2021
- Generalizing RNA velocity to transient cell states through dynamical modeling2020
- scGen predicts single-cell perturbation responses2019
- SCANPY: large-scale single-cell gene expression data analysis2018
Pl. II.7 Placeholder summary about the software the lab builds and maintains for others. On the map of publications (Plate II) the 10 works of this theme are drawn large and ringed in the colour of their island, every other work a small ink dot; the key beside it lists them.
10 of 742 works marked · data synced 6 Nov 2026
Placeholder narrative. This paragraph stands in for the description of how the lab develops and maintains open-source tools. The theme’s leads write the real text before launch.
Placeholder second paragraph about current directions. The tools listed on this page are derived from the theme’s topics and the lab’s repositories on GitHub.
People0
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Publications10
Research area: Spatial, Omics Data Research area: Independent Component, Graphanndata: Access and store annotated data matrices
Research area: Images, Deep LearningAn open-source framework for end-to-end analysis of electronic health record data
Research area: Representations, Single CellPredicting cellular responses to complex perturbations in high‐throughput screens
Research area: Spatial, Omics DataBest practices for single-cell analysis across modalities
Research area: Cell Atlas, IntegrationBenchmarking atlas-level data integration in single-cell genomics
Research area: Cell Atlas, IntegrationMapping single-cell data to reference atlases by transfer learning
Research area: Single Cell RNA, Gene ExpressionGeneralizing RNA velocity to transient cell states through dynamical modeling
Research area: Representations, Single CellscGen predicts single-cell perturbation responses
Research area: Single Cell RNA, Gene ExpressionSCANPY: large-scale single-cell gene expression data analysis
Software11
scVelo
RNA velocity generalized through dynamical modeling.
515v0.3.49 mo agoPython
scib
Benchmarking analysis of data integration tools.
436v1.2.16 mo agoPython
scArches
Reference mapping for single-cell genomics.
414v0.5.13 y agoJupyter Notebook
Scanpy
Single-cell analysis in Python. Scales to >100M cells.
2.6K1.12.58 d agoPython
Single-cell best practices
https://www.sc-best-practices.org
1.3K0.2.08 d agoJupyter Notebook
anndata
Annotated data.
7830.13.449 d agoPython
ehrapy
Electronic Health Record Analysis with Python.
370v0.16.04 d agoPython
scGen
Single cell perturbation prediction.
361v2.1.05 y agoPython
moscot
Multi-omic single-cell optimal transport tools.
219v0.5.22 mo agoPython
CPA
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level.
157v0.5.03 y agoPython
DRVI
Unsupervised Deep Disentangled Representation of Single-Cell Omics
83v0.2.823 d agoPython