Hello,
I applied exploratory factor analysis with network analysis to data from healthy and diseased patients. The analysis shows different clusters of parameters; some are similar in both groups, and some groups cluster differently. For instance, the parameters IleValLeu are similarly clustered in both groups whereas the parameters Pro Ala are not (see figure).
How shall I interpret the data? For instance, in the Pro/Ala case, I expected to see some differences between the groups, but they look pretty much the same to me.
Is the differences about correlation? The scatterplot of the data shows slightly different regression models, but not something compelling.
Is it the value itself? But again there is no real difference in the value distribution between the two groups.
So, what is the actual outcome of the network analysis?
Thank you
Dear Luigi,
if I understood your question correctly, I would say that to interpret results of the network analysis you need to understand the components of the network. What are nodes? What are links? What does it mean if a link connects two nodes? What does it mean if nodes are not connected?
For example, if I read your network correctly, you can easily see that Ala and Pro are correlated stronger in your decease network, compared to the control. Is it significant? Is it important? An answer is beyond network analysis, but lays in your experiment design and other information you have. You can apply some statistics to test if difference in a strength of correlation is statistically significant, but you still need to add a biology level for interpretation.
What is good about network analysis, that you can use graphs theory based properties to get more layers of differences between your conditions, if, again, it makes sense for your data.
Exploratory Factor Analysis (EFA) is a type of network analysis that uses statistical methods to identify patterns and relationships among multiple variables. It is commonly used to analyze large datasets and uncover the underlying structure of the data. EFA can help researchers to identify the relationships between variables and identify latent factors that may be influencing them. It can also be used to reduce data complexity by reducing the number of variables in a dataset, allowing for more focused analysis. Interpreting the results of an EFA can help researchers to understand the relationships between variables and identify potential areas for further research.