First phase of investigation complete (because we have more questions, esp. wrt to deployment strategy.)

Moving to DONE.

Summary of what we found out:

• We really like using this technology to enable fast and user-friendly visualization of results.
• We have some trouble finding "how-to" information that isn't behind a paywall.
• We need a location that we can truly control and provision with apps. We haven't figured out the deployment strategy yet.
• We like the barplot app for our own purposes of sanity-checking, i.e., it helps see if our analysis is correct.
• However, we're not even sure if anyone else that we've shared the link with is actually using it.

Goals and aspirations:

• Would like it to be used with multiple different data sets from different labs. (from: [~molly])
• Want a more reliable and easy-to-control deployment platform.(from [~aloraine] & [~RobertReid])
• Want to be able to carve up different pieces of the work for others to do. (from [~aloraine])
• Can we figure out a way to save the plots? "There should be a save button."

Note: We decided to make a new ticket to capture the remainder of the barplot coding task. That ticket is linked to this one. It is IGBF-3481. Please use that ticket to review the new branch.

Review instructions for the barplot code are there.

For reference, here is the review request comment copied over to the other ticket:

I am pushing a new branch to my fork with my barplot code.

A couple of changes:

• The new code supports both SL4 and SL5.
• I changed the y axis label to include the string "(scaled counts)"

See:

https://bitbucket.org/aloraine/flavonoid-rnaseq/branch/IGBF-3449-1

Request for [~molly]: Maybe draw / mock up some designs today. It will be 10x easier and faster to write the code if you have that to start with.

Conversation today w/ [~aloraine] & [~molly] regarding goals for the app (or apps):

• Needs to be easy to use with minimal input from person using it. They should be able to type in a gene name and not have to worry about what genome assembly the gene is from.
• [~aloraine] requests: First make it work, then make it fast. First make it easy to use and useful, then make it fast and efficient. Make the UI design then implement.

Looks like we can indeed create a shiny app that will help Muday lab and others visualize expression of individual genes.

For next steps, let's investigate the code further, to understand how it works and how we need to modify it to use our data and our code.

Reviewed the RShiny code Ivory B. created for the Krizek Lab "inducible ant" project, presented in a lecture for BINF 3121.

Code link: https://bitbucket.org/lorainelab/inducible-ant-rna-seq/src/main

To run the app:

1) Clone above onto your local computer.

2) Copy "Samples.txt" to folder named DifferentialExpression (this is needed to correct a configuration error in the repository)

3) Use RStudio to open RStudio project file DifferentialExpression/DifferentialExpression.Rproj.

4) In the RStudio interface, open the folder named "ShowGeneExpression" and open "app.R".

5) Run the by clicking "Run App"

Note you'll need to install a bunch of libraries such as edgeR due to how the Shiny app is structured.

Adding screenshots showing the different parts of the App interface.

The platform I used to deploy the above Shiny app was a disappointment. It seemed to "go dead" after very short inactivity periods. This made it inconvenient to use the app the way I wanted to.

Before doing anything else on this, I need to know more about deployment possibilities. For example, will users run it locally, in RStudio? Is it realistic to have it "live" on a Web site for many people to use at once?

Made a new shiny app and published it, including with results files.
Deployed it here:

• https://ann-loraine-gmail.shinyapps.io/72_F3H_PollenTube/