Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

No appropriate meta clustring with run.flowsom #154

Open
Tenrolf opened this issue Apr 5, 2023 · 3 comments
Open

No appropriate meta clustring with run.flowsom #154

Tenrolf opened this issue Apr 5, 2023 · 3 comments
Assignees

Comments

@Tenrolf
Copy link

Tenrolf commented Apr 5, 2023

Dear all,

I am running into an issue with the metaclustering from the run.flowsom function. Whether using an automatic number or setting a number, I get one big metacluster with 99% of cells, and a few other metaclusters with a few cells.

However, the function prep.cytonorm lead to an appropriate metaclustering and the same data are well separated when using umap (with the same clustering channels).

Would anyone have an idea what could go wrong?

Thanks in advance!

R version: 4.2.2
Spectre: 1.0.0
CytoNorm: 0.0.15
flowSOM: 2.2.0

@Tenrolf
Copy link
Author

Tenrolf commented Apr 6, 2023

An update to say that I could mainly solve the problem: I had an issue in the normalization distorting the data for a few cells from a few files, so these very few cells were so different that they were messing with the meta clustering.

However, I'd be curious why the meta clustering from prep.cytonorm was not impacted in the same way? (I also used it on the normalized files as a test).

@tomashhurst
Copy link
Member

Hi @Tenrolf , that is a weird finding -- thanks for pointing it out! Especially the difference between run.flowsom and prep.cytonorm. Will have a dig around and see where the difference is coming from.

Tom

@tomashhurst tomashhurst self-assigned this Apr 11, 2023
@denvercal1234GitHub
Copy link

Hi @Tenrolf -- How did you assess to detect that the issue was because of the normalisation distorting the data? Would you mind showing the bit of codes you used to examine this?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants