Updates
Pathway Analysis vs Gene Set Analysis
Pathway Analysis vs Gene Set Analysis What is the Difference and When Should I Use Each? To some, pathway analysis and gene set analysis are synonyms. However, there are important distinctions between these two groups of methods, and they provide different results. This is part of a series of articles on pathway analysis methods. Pathway analysis provides superior results to gene set analysis for many purposes. Of the gene set analysis methods, gene set enrichment analysis (GSEA) is the most unbiased. Learn about when to use each method. Once upon a time, the dream [...]
Pathway Analysis Method Validation
How do you validate a pathway analysis method? How do you compare such methods? My previous post, Pathway Analysis vs Gene Set Analysis, ended with an interesting question: has anybody really compared side-by-side gene set analysis methods and pathway analysis methods on the same data sets? Well, the answer is: not yet! The main reason for this is that it is extremely difficult to objectively validate the results of a pathway analysis method. It is even more difficult to compare the results of different pathway analysis methods. In this blog, I explain why such [...]
Enrichment vs Impact Pathway Analysis
Not getting much from your pathway analysis? Use Impact Analysis instead of Enrichment alone. How are you currently analyzing your RNASeq and proteomics data? Are you currently using one of the many free tools available for pathway analysis, such as DAVID? Or, are you perhaps using Qiagen’s Ingenuity Pathway Analysis (IPA)? If yes, you are using a pathway enrichment analysis approach. Even though it is one of the oldest approaches (or maybe because of that), the pathway enrichment analysis will severely limit your ability to identify the pathways that are truly [...]
Using Pathway Analysis to Understand Biological Phenomena
Using Pathway Analysis to Understand Biological Phenomena What is a “good” number of DE genes to use in order to try to understand the underlying biological phenomena using a pathway analysis? It is clear that in a whole transcriptome experiment that produces 20-30,000 measurements, selecting a list of DE genes including only 5 or 10 genes would be completely insufficient to understand the underlying biological phenomena using any kind of pathway analysis, systems biology, or functional analysis. At the same time, selecting 10,000 DE genes is also unlikely to produce good results. [...]