Overview

The easiest way to use the freeCount R Shiny applications online is through Posit Connect Cloud, which is an online platform that simplifies the deployment of data applications and documents.

freeCount

The freeCount analysis framework provides a modular set of tools and tutorials for a structured approach to biological count data analysis. Users are guided through common data assessment, processing and analysis approaches.

The different analysis tools currently available include:

• Differential Expression (DE) Analysis – DA
• Network Analysis – NA
• Functional Analysis – FA
• Set Operations – SO

Steps

The following steps will walk you through how to run the freeCount apps online using Posit Connect Cloud.

1. Navigate to https://connect.posit.cloud/elizabethbrooks?search=freeCount
2. Select the app that you want to run
   
   Wait for the app to launch.
   
   Done! Now you are able to perform the selected analysis.

Step 1

Navigate to https://connect.posit.cloud/elizabethbrooks?search=freeCount

Step 2

Select the app that you want to run and click the name or image to open.

Wait…

Wait for the project to deploy in your Posit Cloud workspace.

Done!

Now you are able to perform the selected analysis.

Analysis Tutorials

The freeCount apps provide a set of common tools for analyzing biological data, including differential expression and network analysis. We have tutorials available to guide users through a structured analysis approach:

• Differential expression (DE) analysis is used to identify genes driving the patterns of variation associated with groups of samples.
• Functional analysis of DE results is useful for determining the functions of DE genes. Genes can have multiple functional annotations, so we need to determine which ones are important.
• Weighted gene co-expression network analysis (WGCNA) is used to investigate the function of genes at the system-level. In a network analysis genes with similar patterns of expression are grouped together into modules.
• Functional analysis of network results is useful for determining the functions of a set of interesting genes. These gene sets can be lists of genes produced from different analysis, including WGCNA.
• Set operations using Venn diagrams are generally very useful for comparing lists of things. Set operations are also a good way to identify unique or shared genes across sets of analysis results.