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Hello! Thanks for developing the tool! I went through your example scripts, and I felt that this wasn't really clarified in the script.
So, I am doing a case-control analysis whereby I run mvNMF on separate inputs for my cases and controls. Using the original input and the exposures from a de-novo run, I identified the clusters stratifying my data. At the same time, I do not find any outliers in my data. How should I account for cohort stratification while interpreting the signatures? Should I run the mvNMF separately for the stratified cohorts?
I also observed that in the latter case, if I re-run a de-novo discovery on my stratified data (let's say all the samples from cluster 0), and re-run the pre-processing script again (using these new exposures and stratified input), the function further stratified the data into more clusters. Is this expected? Could you please share some insights?
Feel free to correct me if I got something wrong.
Thanks a lot!
Vanshika
The text was updated successfully, but these errors were encountered:
Hello! Thanks for developing the tool! I went through your example scripts, and I felt that this wasn't really clarified in the script.
So, I am doing a case-control analysis whereby I run mvNMF on separate inputs for my cases and controls. Using the original input and the exposures from a de-novo run, I identified the clusters stratifying my data. At the same time, I do not find any outliers in my data. How should I account for cohort stratification while interpreting the signatures? Should I run the mvNMF separately for the stratified cohorts?
I also observed that in the latter case, if I re-run a de-novo discovery on my stratified data (let's say all the samples from cluster 0), and re-run the pre-processing script again (using these new exposures and stratified input), the function further stratified the data into more clusters. Is this expected? Could you please share some insights?
Feel free to correct me if I got something wrong.
Thanks a lot!
Vanshika
The text was updated successfully, but these errors were encountered: