<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>

CC BY-SA 4.0
2021-08-15T21:13:49.597Z
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?
Maximilian Press
CC BY-SA 4.0
2021-08-19T17:17:09.627Z
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.
Kamil S Jaron
<r><genomics><clustering><heatmap><statsmodels>
General Question: Statistical Alternatives to Oncoprint
# 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.  

---

## 0 answers

