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Introduction

In the last two decades, transcriptome-wide profiling has been playing a major role in revolutionizing biology and medicine. Recent single-cell RNA-seq technique has taken the field of transcriptomics to the next level by allowing the quantification of gene expression in individual cells. Albeit, this enormous increase in sequencing of tens of thousands of cells in a single run has brought new challenges in data analysis. In this tutorial, we will use CyVerse Discovery Environment (DE) public apps for analyzing a sample dataset of Peripheral Blood Mononuclear Cells (PBMC). The dataset has 1,222 single cells and is freely available from 10X Genomics website. Access the slides for this tutorial here.

Some experience with R programming and command-line is required to follow this tutorial.

Learning Objectives

  • What is single-cell RNA-Seq and why it is useful
  • Basic steps for single-cell analysis
  • Using single-cell analysis tools in the CyVerse Discovery Environment

Tutorial Maintainer(s)

Who to contact if this guide needs fixing. You can also email learning@CyVerse.org

Maintainer Institution Contact
Reetu Tuteja CyVerse / UArizona reetututeja@cyverse.org
Ruchika Bhat UArizona ruchikabhat@arizona.edu

Prerequisites

Downloads, access, and services

In order to complete this tutorial, you will need access to the following services/software

Prerequisite Preparation/Notes Link/Download
CyVerse account You will need a CyVerse account to complete this exercise CyVerse User Portal
Cyberduck Standalone software for upload/download to Data Store Download Cyberduck

Platform(s)

We will use the following CyVerse platform(s):

Platform Interface Link Platform Tour
Data Store GUI/Command line Data Store Data Store Guide
Discovery Environment Web/Point-and-click Discovery Environment Discovery Environment Guide

Application(s) used

Discovery Environment App(s):

App name Version Description App link Notes/other links
Rstudio-Seurat 4.0.1 Single-cell Analysis App link Original App Documentation

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