A collection of guides and examples for the Gemma open models from Google.

Explainer for black box models that predict molecule properties

Graph-linked unified embedding for single-cell multi-omics data integration

Matplotlib style for scientific publications

add statistical significance annotations on seaborn plots. Further development of statannot, with bugfixes, new features, and a different API.

Visualising data frames as funky heatmaps 📊

Simple simulation of single-cell RNA sequencing data

All (most?) of the sounds in macOS Big Sur

A game theoretic approach to explain the output of any machine learning model.

Converts profiling output to a dot graph.

DIALOGUE is a dimensionality reduction method that uses cross-cell-type associations to identify multicellular programs (MCPs) and map the cell transcriptome as a function of its environment.

Optimal transport tools implemented with the JAX framework, to solve large scale matching problems of any flavor.

Generalized Linear Models in Sklearn Style

Seven Bridges Genomics aligner/caller debugging and analysis tools

🧬 gget enables efficient querying of genomic reference databases

User-friendly extensions of the DrugBank database

Deep probabilistic analysis of single-cell and spatial omics data

Conversion between scRNA-seq objects

Single-cell analysis in Python. Scales to >100M cells.

The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of dr…

Models and datasets for perturbational single-cell omics

Benchmark datasets, data loaders, and evaluators for graph machine learning