Single-cell analysis in Python. Scales to >100M cells.
🧬 gget enables efficient querying of genomic reference databases
Annotated data.
A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.
pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.
Single cell perturbation prediction
Python package to perform enrichment analysis from omics data.
starfish: unified pipelines for image-based transcriptomics
A universal toolkit for upstream processing of long RNA reads
Differential expression analysis for single-cell RNA-seq data.
A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.
Identification of differential RNA modifications from nanopore direct RNA sequencing
Analyze your RNA sequencing data without writing a single line of code
Multi-agent LLM driven cell type annotation for single-cell RNA-Seq data
Cell-type Annotation for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural Network
A tool to identify, orient, trim and rescue full length cDNA reads
Unsupervised Deep Disentangled Representation of Single-Cell Omics
Cellxgene Gateway allows you to use the Cellxgene Server provided by the Chan Zuckerberg Institute (https://github.com/chanzuckerberg/cellxgene) with multiple datasets.
Multimodal foundation model predicting transcriptome-wide virtual spatial transcriptomics from histology.
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