R toolkit for single cell genomics
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
Deep probabilistic analysis of single-cell and spatial omics data
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
A python library for multi omics included bulk, single cell and spatial RNA-seq analysis.
https://www.sc-best-practices.org
𧬠gget enables efficient querying of genomic reference databases
π π£ π± Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment
A list of scRNA-seq analysis tools
An interactive explorer for single-cell transcriptomics data
Tools to process and analyze deep sequencing data.
A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.
NicheNet: predict active ligand-target links between interacting cells
Fast, sensitive and accurate integration of single-cell data with Harmony
Cell type annotation for single-cell RNA-seq using multi-LLM consensus
Spatial Single Cell Analysis in Python
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
Genomic sequence storage, indexing and alignment inside PostgreSQL: https://bioseqdb.com/
Native R and Python sample-deletion robustness for omics: candidate certificates, minimum deletion bounds, Bioconductor model refits and donor-level pseudobulk
A curated list of tools, foundation models, datasets and tutorials for spatial transcriptomics and single-cell analysis, ordered the way an analysis goes.
A fast aligner for long DNA and RNA reads.
State-of-the-art bio cell models for inference and training. Minimal, readable, and produciton-ready.