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Evaluate Differential Expression with Sleuth

Description:

In this final section of the tutorial, we will use the R package Sleuth to visualize our data and perform differential expression analysis. We will summarize the R analysis steps, but the tutorial itself is provided in the form of an RMarkdown notebook.

Input Data:

Input Description Example
Kallisto quantification results (abundances.h5, abundances.tsv, run_info.json for each FastQ file analyzed ) See detailed description below Kallisto outputs

Use Sleuth in RStudio App to Calculate and Visualize Results

  1. In the Discovery Environment click on the Data icon and navigate to your rna-seq-tutorial tutorial folder and create a folder to store outputs, name the folder sleuth_analysis.

  2. In the Apps view, search for and launch the RStudio Sleuth pb app. You can use this direct link: Sleuth app.

  3. In Analysis Info you can name this analysis and provide any comments (optional). Under Output folder, navigate to the sleuth_analysis folder created earlier. Your outputs will automatically be placed in this folder; click Next.

  4. In Parameters for Notebooks a default folder containing notebook specific to this tutorial will be loaded (/iplant/home/shared/cyverse_training/tutorials/pbv3/R) by default. You may change this if you have an alternative notebook.

  5. Under Datasets and Data for analysis navigate to the rna-seq-tutorial folder created earlier, go into the folder containing your Kallisto output, and select the Kallisto_quant_output folder.

  6. Under Datasets and Study design file navigate to the rna-seq-tutorial folder created earlier, go into the metadata folder and select the experimental design file (i.e., experimental_design.tsv); click Next.

  7. Click Next again to skip Advanced Settings (optional); under Review and Launch click Launch Analysis.

  8. Click on Analyses view to see the current status of the job; you can also click on the Analyses icon to navigate to this section. When the job has the status Running you will be able to access the RStudio session. There will be a link icon immediately to the left of the analysis name. Click this to open the RStudio session in a new browser tab.

Tip

Although the job has Running status, it may take a few minutes to access the RStudio session, the amount of is related to the size of the files being transferred into the RStudio environment. :::

Working in RStudio

9. In the RStudio session we must modify our RStudio home directory to make it easier to save files. Open the Terminal tab. Paste in the following commands and hit enter:

cp -r data/input/* /home/rstudio/

# make sure folder and filenames match your inputs
sudo chmod -R 777 /home/rstudio/R
sudo chmod -R 777 /home/rstudio/experimental_design.tsv
sudo chmod -R 777 /home/rstudio/kallisto_quant_output
  1. In the RStudio Files tab, go to the R folder and click sleuth_pb_tutorial.Rmd to open the notebook.
  2. Follow the notebook by clicking the green "play" button in each section (chunk) of R code. You can follow along with the notebooks explanations and then press play to run each code chunk. The final code chunk will launch the interactive visualizations in the R Shiny application.

Rstudio Outline

Without replicating the actual code presented in the notebook, here are the major steps presented:

(a) Step 1: The Sleuth library and additional libraries for plotting and retrieving data from Ensembl are loaded.

(b) Step 2: The experimental design file is loaded, and a table is created that maps this metadata with the Kallisto outputs.

© Step 3: We use the biomaRt package to load gene names from Ensembl so that we can more descriptively annotate our transcripts.

(d) Step 4: We indicate the variables we want to compare and use the Sleuth functions to create the data model.

(e) Step 5: We do an exploratory visualization of the dataset using PCA plotting.

(f) Step 6: A liner model is created, and the results of the analysis are displayed in an interactive R Shiny application. The R Shiny application will generate tables of results and figures that can be downloaded and further analyzed. Note: The test table available from the R Shiny application contains a complete list of gene names, quantifications, and other statistics. You can download this directly from the R Shiny app.

Note

Your web browser must have pop-up blocking disabled to view the Shiny application.

  1. When you have finished with your RStudio session, return to the Analysis view and select (checkbox) the RStudio analysis. Go to the Analysis view and select Complete and Save Outputs. Any files created during your RStudio analysis will be saved.

Note

VICE applications (Like this RStudio application) typically have a 48 hour run time on CyVerse. The application will automatically terminate and save outputs at this time.

Output/Results

Output Description Example
Varies In the Sleuth Shiny application you can export tables with your differential testing results and a variety of graphs. NA

Description of output and results

Sleuth Shiny application allows you to interactively examine and tailor tables of differential testing results as well as graphs of gene expression and other metrics. The application also allows you to export and save these outputs.

Last update: 2022-11-28