DC ISCB Workshop 2016 - Co-expression network analysis using RNA-Seq data (Keith Hughitt)

TL;DR

This content provides an overview of co-expression analysis, including its uses, steps for building a co-expression network, and parameters for optimization.

Transcript

all right so you've seen me before probably by now but in case you came in late my name is Keith Hewitt I'm a fifth year PhD student in the lab of dr. Najib Elsayed and so my work is involved with a CO expression analysis and functional genomics of parasites and particularly of host parasite interactions and trying to understand what's going on dur... Read More

Key Insights

• 💯 Co-expression analysis is a powerful tool for studying gene regulation and inferring biological functions.
• 🏗️ Building a co-expression network involves several steps, including data preprocessing, constructing a similarity matrix, transforming it into an adjacency matrix, and detecting modules.
• 💯 Parameter optimization is important in co-expression network analysis to ensure the generation of biologically meaningful networks.

Questions & Answers

Q: What is co-expression analysis?

Co-expression analysis involves studying the correlation or similarity between expression profiles of genes in biological systems. It helps identify co-regulated genes and understand gene regulation.

Q: What are co-expression networks used for?

Co-expression networks are useful for inferring gene functions, detecting co-regulated genes, and understanding the regulatory mechanisms underlying biological processes.

Q: What are the steps involved in building a co-expression network?

The steps include data preprocessing, constructing a similarity matrix using correlation or similarity measures, transforming the matrix into an adjacency matrix, and detecting modules or clusters within the network.

Q: How can co-expression networks be optimized?

Parameters such as similarity measure, transformation function, and cut-off thresholds can be optimized to build a biologically meaningful co-expression network. The choice of these parameters can impact the quality and interpretation of the network.

Key Insights:

• Co-expression analysis is a powerful tool for studying gene regulation and inferring biological functions.
• Building a co-expression network involves several steps, including data preprocessing, constructing a similarity matrix, transforming it into an adjacency matrix, and detecting modules.
• Parameter optimization is important in co-expression network analysis to ensure the generation of biologically meaningful networks.
• Visualization and interpretation of co-expression networks can provide valuable insights into gene regulation and functional relationships among genes.

Summary & Key Takeaways

• Co-expression analysis involves studying the correlation or similarity between expression profiles of genes in biological systems.

• Co-expression networks are useful for understanding gene regulation, inferring biological functions, and detecting co-regulated genes.

• Building a co-expression network involves data preprocessing, similarity matrix construction, and module detection.