Answers.Body: <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>
  
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Answers.Text: Thanks for your help, I am perhaps slightly further along the road/ am looking at something slightly different. I have looked at correlations between all variables be they biomarkers or end points etc. What (for me) is important to look at are co-dependencies of biomarkers across the whole system - all be it only 10 markers. I am here in the process of literature research :) https://arxiv.org/pdf/1501.04731.pdf
Answers.UserDisplayName: JP1
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Answers.CreationDate: 2017-07-14T11:27:44.770Z
Answers.Text: If you read carefully my answer. correlations are the start of the process to find the relationship between markers and profiles
Answers.UserDisplayName: llrs
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Answers.CreationDate: 2017-07-14T13:01:19.787Z
Answers.Text: Ok i see what you mean. I will have to look at WGCNA to see if it is extendable outside of genetic data. I am happy to implement this myself. I primarily wanted to make sure what I was proposing wasnt stupid (have poele done thins before - yes ) and what others experience was. Thanks for the advice!
Answers.UserDisplayName: JP1
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Answers.CreationDate: 2017-07-14T13:01:59.120Z
Answers.Text: Yes, it is extendable outside genetic data
Answers.UserDisplayName: llrs
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Answers.CreationDate: 2017-07-14T14:10:21.393Z
Answers.Text: @JB1 Perhaps if you wait a bit before selecting my answer as the best one you will get more answers (I doubt it, but is in general recommended). BTW if you find other methods let me know I am also interested in this area and will be doing a similar project soon
Answers.UserDisplayName: llrs
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Answers.Text: thanks for the advice! Ill let you know how i get on, libraries i find/ build
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Answers.UserDisplayName: llrs
Body: <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>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-13T19:28:49.713Z
Comments.Text: First of all it is important to know very well what kind of data you have and the limitations and advantages it has. It is also equally important to know your goal: which is your goal? Find the best biomarker? I was starting to answer, but then I thought that asking this won't damage my answer. Also which is the number of samples for each data type?
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-14T07:38:26.630Z
Comments.Text: Yes agreed! 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". Its not necessarily "best" biomarker as different diseases will have different marker profiles :) there are around 1000 marker samples and 10s of biomarkers mixed between clinical and biochemical. There are also numerous time points to be incorporated.
Comments.UserDisplayName: JP1
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-14T07:39:00.010Z
Comments.Text: https://www.reddit.com/r/bioinformatics/comments/6n0x2s/building_network_for_biomarker_interaction/   Someone alse answered here :)
Comments.UserDisplayName: JP1
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-07-14T10:32:59.850Z
Comments.Text: It would be fine if you [edit] the post to include the information from the previous comment and from reddit in your post. It will improve the question and make it self contained
Comments.UserDisplayName: llrs
Tags: <proteins><networks><pathway><interactions>
Title: Building network for biomarker interaction analysis
ThreadText: # Building network for biomarker interaction analysis
  
  (Asked by: username_0 on 2017-07-13)
  
  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!
  
  **Tags:** proteins, networks, pathway, interactions
  
  > **Comments:**
  > **username_1**: First of all it is important to know very well what kind of data you have and the limitations and advantages it has. It is also equally important to know your goal: which is your goal? Find the best biomarker? I was starting to answer, but then I thought that asking this won't damage my answer. Also which is the number of samples for each data type?  
  > **username_0**: Yes agreed! 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". Its not necessarily "best" biomarker as different diseases will have different marker profiles :) there are around 1000 marker samples and 10s of biomarkers mixed between clinical and biochemical. There are also numerous time points to be incorporated.  
  > **username_0**: https://www.reddit.com/r/bioinformatics/comments/6n0x2s/building_network_for_biomarker_interaction/   Someone alse answered here :)  
  > **username_1**: It would be fine if you [edit] the post to include the information from the previous comment and from reddit in your post. It will improve the question and make it self contained  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2017-07-14. Score: 2
  
  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.
  
  > **Comments:**
  > **username_0**: Thanks for your help, I am perhaps slightly further along the road/ am looking at something slightly different. I have looked at correlations between all variables be they biomarkers or end points etc. What (for me) is important to look at are co-dependencies of biomarkers across the whole system - all be it only 10 markers. I am here in the process of literature research :) https://arxiv.org/pdf/1501.04731.pdf  
  > **username_1**: If you read carefully my answer. correlations are the start of the process to find the relationship between markers and profiles  
  > **username_0**: Ok i see what you mean. I will have to look at WGCNA to see if it is extendable outside of genetic data. I am happy to implement this myself. I primarily wanted to make sure what I was proposing wasnt stupid (have poele done thins before - yes ) and what others experience was. Thanks for the advice!  
  > **username_1**: Yes, it is extendable outside genetic data  
  > **username_1**: @JB1 Perhaps if you wait a bit before selecting my answer as the best one you will get more answers (I doubt it, but is in general recommended). BTW if you find other methods let me know I am also interested in this area and will be doing a similar project soon  
  > **username_0**: thanks for the advice! Ill let you know how i get on, libraries i find/ build  
  
  ---
  
  