An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
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Updated
May 21, 2024 - R
An end-to-end Single-Cell Pipeline designed to facilitate comprehensive analysis and exploration of single-cell data.
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
R package with collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using R.
Seurat meets tidyverse. The best of both worlds.
Generate high quality, publication ready visualizations for single cell transcriptomics data.
A guide to using a Seurat object in conjunction with RNA Velocity
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.
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
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
Various utility functions for Seurat single-cell analysis
NASQAR: A web-based platform for High-throughput sequencing data analysis and visualization
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.
.h5mu files interface for Seurat
Label elements within user drawn gates
represent each cell in UMAP plots as a pie chart
Visualize clonal expansion via circle-packing. 'APackOfTheClones' extends 'scRepertoire' to produce a publication-ready visualization of clonal expansion at a single cell resolution, by representing expanded clones as differently sized circles.
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