🏆 #1 Multi-LLM consensus framework | 550+ stars | 95% accuracy | 10+ LLM providers | Leading cell annotation tool

An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.

R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.

scplotter is an R package that is built upon plotthis. It provides a set of functions to visualize single-cell sequencing data in an easy and efficient way.

Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

Generate high quality, publication ready visualizations for single cell transcriptomics data.

Inside Out Positional Tracking (6DoF) for GearVR/Cardboard/Daydream using ARCore v1.6.0

Seurat meets tidyverse. The best of both worlds.

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.

Cell type annotation with local Large Language Models (LLMs) - Ensuring privacy and speed with extensive customized reports

A guide to using a Seurat object in conjunction with RNA Velocity

This repository contains R code, with which you can create 3D UMAP and tSNE plots of Seurat analyzed scRNAseq data

Bring your single-cell data to life

Various utility functions for Seurat v5 single-cell analysis

R wrappers to connect Python dimensional reduction tools and single cell data objects (Seurat, SingleCellExperiment, etc...)

.h5mu files interface for Seurat

NASQAR: A web-based platform for High-throughput sequencing data analysis and visualization

A web-based interactive (wizard style) application to perform a guided single-cell RNA-seq data analysis and clustering based on Seurat v3

R package developed for single-cell RNA-seq analysis. It was designed using the Seurat framework, and offers existing and novel single-cell analytic work flows.

R package expanding integrative analysis capabilities of Seurat by providing seamless access to popular integration methods and to an integration benchmarking toolkit.