Deep probabilistic analysis of single-cell and spatial omics data
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Updated
Jul 16, 2024 - Python
Deep probabilistic analysis of single-cell and spatial omics data
Spatial Single Cell Analysis in Python
DANCE: a deep learning library and benchmark platform for single-cell analysis
CellRank: dynamics from multi-view single-cell data
Differential expression analysis for single-cell RNA-seq data.
A wrapper for the kallisto | bustools workflow for single-cell RNA-seq pre-processing
LIANA+: an all-in-one framework for cell-cell communication
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.
A BLAST-like toolkit for large-scale scRNA-seq data querying and annotation.
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.
A Package for Cas9-Enabled Single Cell Lineage Tracing Tree Reconstruction
Deep Learning based cell composition analysis with Scaden.
User-friendly tool to infer cell-cell interactions and communication from gene expression of interacting proteins
An object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data
scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics
scDCC: Single Cell Deep Constrained Clustering
Single-cell exploratory data analysis for RNA-Seq
𝒫robabilistic modeling of RNA velocity ⬱
Wasserstein Generative Adversarial Network for analysing scRNAseq data
An unofficial demultiplexing strategy for SPLiT-seq RNA-Seq data
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