R toolkit for single cell genomics
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Updated
Jun 1, 2024 - R
R toolkit for single cell genomics
Deep probabilistic analysis of single-cell and spatial omics data
Community-provided extensions to Seurat
Spatial Single Cell Analysis in Python
An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
Single cell perturbation prediction
Reference mapping for single-cell genomics
Single cell trajectory detection
Interfaces for HDF5-based Single Cell File Formats
CellRank: dynamics from multi-view single-cell data
Cloud-based single-cell copy-number variation analysis tool
A Shiny web app for mapping datasets using Seurat v4
Rails/Docker application for the Broad Institute's single cell RNA-seq data portal
Tutorials, workflows, and convenience scripts for Single Cell Portal
Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets
Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space
R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.
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.
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