Body: <p>I am a computational genomics student who has recently joined a research lab as an RA. We often work with oncoprints (comuts/heatmaps), and we visualize molecular signatures by reorganizing the comut. Although this is the first-line approach, what are some statistical ways to identify molecular signatures? I have heard of NNF, clustering, etc, but I also heard a colleague mention using cox analysis (which I thought was for survival analysis?).</p>
  <p>My Background:
  While introductory statistics has been hammered into my head, I have less experience with higher level computational techniques, like PCA, clustering. I am familiar with the general purpose of the statistical approaches I've mentioned, but not confident in when or how to use them. For example, I know the general purpose of PCA, but I did not know that if you have categorical binary data and want to do unsupervised clustering, you should do logisticPCA. I would appreciate any advice, general or relevant to the question at hand. I am eager to be competent, confident, and contribute to my lab! Thank you :)</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-15T21:13:49.597Z
Comments.Text: Can you tell us more about what you are trying to accomplish? Or link to examples? For example I see that comut and oncoprint are specific and distinct visualization softwares (https://github.com/vanallenlab/comut, https://bioc.ism.ac.jp/packages/3.2/bioc/vignettes/ComplexHeatmap/inst/doc/s8.oncoprint.html), Cox was a prominent statistician who worked on many techniques, not only survival analysis, but you seem to be using the words in very generic ways so I worry you mean something different. Can you be more specific?
Comments.UserDisplayName: Maximilian Press
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-19T17:17:09.627Z
Comments.Text: I agree with Max, I have hard time understanding your question. You don't need to explain your presonal context, but rather clearly explain what exactly you do (perhaps with links), what you identify as a problem (why you can not continue doing so) and how do you imagine to solve it.
Comments.UserDisplayName: Kamil S Jaron
Tags: <r><genomics><clustering><heatmap><statsmodels>
Title: General Question: Statistical Alternatives to Oncoprint
ThreadText: # General Question: Statistical Alternatives to Oncoprint
  
  (Asked by: username_0 on 2021-08-10)
  
  I am a computational genomics student who has recently joined a research lab as an RA. We often work with oncoprints (comuts/heatmaps), and we visualize molecular signatures by reorganizing the comut. Although this is the first-line approach, what are some statistical ways to identify molecular signatures? I have heard of NNF, clustering, etc, but I also heard a colleague mention using cox analysis (which I thought was for survival analysis?).
  
  My Background:
  While introductory statistics has been hammered into my head, I have less experience with higher level computational techniques, like PCA, clustering. I am familiar with the general purpose of the statistical approaches I've mentioned, but not confident in when or how to use them. For example, I know the general purpose of PCA, but I did not know that if you have categorical binary data and want to do unsupervised clustering, you should do logisticPCA. I would appreciate any advice, general or relevant to the question at hand. I am eager to be competent, confident, and contribute to my lab! Thank you :)
  
  **Tags:** r, genomics, clustering, heatmap, statsmodels
  
  > **Comments:**
  > **username_1**: Can you tell us more about what you are trying to accomplish? Or link to examples? For example I see that comut and oncoprint are specific and distinct visualization softwares (https://github.com/vanallenlab/comut, https://bioc.ism.ac.jp/packages/3.2/bioc/vignettes/ComplexHeatmap/inst/doc/s8.oncoprint.html), Cox was a prominent statistician who worked on many techniques, not only survival analysis, but you seem to be using the words in very generic ways so I worry you mean something different. Can you be more specific?  
  > **username_2**: I agree with Max, I have hard time understanding your question. You don't need to explain your presonal context, but rather clearly explain what exactly you do (perhaps with links), what you identify as a problem (why you can not continue doing so) and how do you imagine to solve it.  
  
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