There are 12 repositories under scrna-seq topic.
Deep probabilistic analysis of single-cell omics data
Analysis of single cell RNA-seq data course
🐟 🍣 🍱 Highly-accurate & wicked fast transcript-level quantification from RNA-seq reads using selective alignment
An interactive explorer for single-cell transcriptomics data
Fast, sensitive and accurate integration of single-cell data with Harmony
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
Table of software for the analysis of single-cell RNA-seq data.
Reference mapping for single-cell genomics
Single-cell Transcriptome and Regulome Analysis Pipeline
Single cell perturbation prediction
Simple simulation of single-cell RNA sequencing data
R package for the joint analysis of multiple single-cell RNA-seq datasets
Spatial alignment of single cell transcriptomic data.
STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of single-cell data
R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets
muon is a multimodal omics Python framework
Single cell trajectory detection
A tool for semi-automatic cell type annotation
Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).
Clustering scRNAseq by genotypes
A wrapper for the kallisto | bustools workflow for single-cell RNA-seq pre-processing
Useful functions to make your scRNA-seq plot more cool!
R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.
Enables cellxgene to generate violin, stacked violin, stacked bar, heatmap, volcano, embedding, dot, track, density, 2D density, sankey and dual-gene plot in high-resolution SVG/PNG format. It also performs differential gene expression analysis and provides a Command Line Interface (CLI) for advanced users to perform analysis using python and R.
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. This is an unstable experimental version. Please see http://bioconductor.org/packages/BASiCS/ for the official release version
Deep Embedding for Single-cell Clustering
Pipeline for initial analysis of droplet-based single-cell RNA-seq data
Cell-type Annotation for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural Network
Efficient and precise single-cell reference atlas mapping with Symphony
SCP provides a comprehensive set of tools for single cell data processing and downstream analysis.
a spatial deconvolution method based on deep learning frameworks, which converts bulk transcriptomes into spatially resolved single-cell expression profiles
a fully Bayesian approach to deconvolve tumor microenvironment
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.