text: user367: I am looking for advice on how to best approach a problem I am faced with. 
  I have a dataset of numerous degradative biomarkers, clinical information and various other measures (from clinical trials). I would like to see how different biomarkers relate to each other, and effect their levels (high/low etc).
  My goal is to gain an understanding of how these biomarker levels fluctuate in different disease profiles - and understand if there is a relationship between each of them (co/independent). The limitation is that I do not have access to genetic or environmental data so this will be a "random effect". There are around 1000 marker samples and 10's of biomarkers mixed between clinical and biochemical. There are also numerous time points to be incorporated.
  I have thought about implementing something similar to a protein-protein interaction network, although this will of course not involve physical interactions, but more pathway interactions for further hypothesis driven research. 
  I wanted to firstly: validate if this project actually makes sense and secondly: ask for some advice around how to best implement this. 
  I have experience implementing neural/deep nets, directed and undirected networks (Bayes) in python, but not sure exactly how to go about this.
  Thanks!
  
  user48: You can start by looking at the correlation (if data is non-parametric) of each variable with each variable of each data point you have. One thing that might change is the correlation between variables along the disease.
  Next you can determine which relationships are constant along the disease progression/clinical trial. Identify also the ones that don't show a constant relationship. To do so you can use WGCNA (although with just 10 variables is too low to expect a scale free network you can use to correlate the variables and group them by correlation profile)
  Then the question is which variables are more important, those who kept the same relationship or those which change? 
  Use the eigengenes of those variables (separately by those that have a constant relationship and those which don't) to estimate the dependency with the parametric variables. Or you simply can do an ANOVA of all the variables with each non-parametric variable. 
  
original_text: 367: <p>I am looking for advice on how to best approach a problem I am faced with. </p>
  
  <p>I have a dataset of numerous degradative biomarkers, clinical information and various other measures (from clinical trials). I would like to see how different biomarkers relate to each other, and effect their levels (high/low etc).</p>
  
  <p>My goal is to gain an understanding of how these biomarker levels fluctuate in different disease profiles - and understand if there is a relationship between each of them (co/independent). The limitation is that I do not have access to genetic or environmental data so this will be a "random effect". There are around 1000 marker samples and 10's of biomarkers mixed between clinical and biochemical. There are also numerous time points to be incorporated.</p>
  
  <p>I have thought about implementing something similar to a protein-protein interaction network, although this will of course not involve physical interactions, but more pathway interactions for further hypothesis driven research. </p>
  
  <p>I wanted to firstly: validate if this project actually makes sense and secondly: ask for some advice around how to best implement this. </p>
  
  <p>I have experience implementing neural/deep nets, directed and undirected networks (Bayes) in python, but not sure exactly how to go about this.</p>
  
  <p>Thanks!</p>
   48: <p>You can start by looking at the correlation (if data is non-parametric) of each variable with each variable of each data point you have. One thing that might change is the correlation between variables along the disease.</p>
  
  <p>Next you can determine which relationships are constant along the disease progression/clinical trial. Identify also the ones that don't show a constant relationship. To do so you can use WGCNA (although with just 10 variables is too low to expect a scale free network you can use to correlate the variables and group them by correlation profile)</p>
  
  <p>Then the question is which variables are more important, those who kept the same relationship or those which change? 
  Use the eigengenes of those variables (separately by those that have a constant relationship and those which don't) to estimate the dependency with the parametric variables. Or you simply can do an ANOVA of all the variables with each non-parametric variable. </p>
  