BioInfoSummer18 - Advanced Kallisto RNA-Seq (Bulk and Single Cell)¶
Goal¶
This draft tutorial will introduce some of the features of the Kallisto and Sleuth tools for RNA-Sequencing of bulk and single-cell samples.
Warning
This tutorial is still in its draft form. These datasets are not yet available on the CyVerse Data Store, but are available on an Amazon AMI and docker container. These will later be migrated to VICE. Additionally, these materials are part of a workshop collection and not yet designed for independent use. These should not be relied on for analysis of your own data - they are merely useful examples that can inform your own analysis.
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 |
Docker | You must have access to Docker to run this tutorial | Dockerhub |
Amazon AWS Account | You can run this tutorial from this AMI: | ami-042909f3c5b9bf6ed |
Platform(s)¶
We will use the following CyVerse platform(s):
Platform | Interface | Link | Platform Documentation | Quick Start |
---|---|---|---|---|
Data Store | GUI/Command line | Data Store | Data Store Manual | Data Store Guide |
Discovery Environment | Web/Point-and-click | Discovery Environment | DE Manual | Discovery Environment Guide |
VICE | A flexible environment for using Jupyterlab, RStudio, and RShiny | Vice Documentation |
Important links for this workshop¶
Workshop logistics¶
Resource/Description | Link |
---|---|
Gitter channel - we will have live chat and share through this channel | Gitter channel for chat |
Opening poll | Google opening poll |
Software resources¶
Resource/Description | Link |
---|---|
Bioconductor - resource for R-based bioinformatics tools | Bioconductor |
Bioconda - Reproducible software installation | Bioconda |
CyVerse Learning Center - CyVerse learning materials | CyVerse Learning Center |
Papers¶
Resource/Description | Link |
---|---|
A survey of best practices for RNA-seq data analysis | Conesa |
Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts | Ntranos |
Differential analysis of RNA-seq incorporating quantification uncertainty | Pimentel |
Tackling the widespread and critical impact of batch effects in high-throughput data | Leek |
AmpUMI: design and analysis of unique molecular identifiers for deep amplicon sequencing | Clement |
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies | lafzi |
Other learning resources¶
Resource/Description | Link |
---|---|
BioInfoSummer Workshop slides | Slides |
Hemberg single cell RNA-Seq wiki (very comprehensive) | Hemberg wiki |
How to Use t-SNE Effectively | tsne |
Dana Pe’er: “Having fun with single-cell RNA-seq: imputation and manifolds” | Pe'er (YouTube) |
Lior Pachter: Differential analysis of count data in genomics | Pachter (YouTube) |
Principal Component Analysis (PCA) clearly explained (2015) | Statquest (YouTube) |
Single-cell RNA-Seq tools | SC Tools |
Seurat pipeline for SC RNA-Seq | Seurat |
Fix or improve this documentation
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- Report an issue or submit a change: Github Repo Link
- Send feedback: Tutorials@CyVerse.org
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