R toolkit for single cell genomics
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Updated
Aug 2, 2024 - R
R toolkit for single cell genomics
Deep probabilistic analysis of single-cell and spatial omics data
Spatial Single Cell Analysis in Python
An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
CellRank: dynamics from multi-view single-cell data
Reference mapping for single-cell genomics
Community-provided extensions to Seurat
Single cell perturbation prediction
Single cell trajectory detection
R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.
Generate high quality, publication ready visualizations for single cell transcriptomics data.
Interfaces for HDF5-based Single Cell File Formats
Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets
A Shiny web app for mapping datasets using Seurat v4
Toolkit for highly memory efficient analysis of single-cell RNA-Seq, scATAC-Seq and CITE-Seq data. Analyze atlas scale datasets with millions of cells on laptop.
Convert Seurat objects to 10x Genomics Loupe files.
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 drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
Semi-supervised adversarial neural networks for classification of single cell transcriptomics data
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