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.

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

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

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

Seurat meets tidyverse. The best of both worlds.

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

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.

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 single-cell analysis

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

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

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

.h5mu files interface for Seurat

Signac

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.

Label elements within user drawn gates

represent each cell in UMAP plots as a pie chart