saeyslab / CytoNorm

R library to normalize cytometry data
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Skipping the clustering #13

Open bioguy2018 opened 4 years ago

bioguy2018 commented 4 years ago

Dear all, I already know my data clustering affected by my batches and I read thatI can skip the clustering part in the training but I am not sure how this is achieved! should I just skip the FlowSOM.params in the training function and then the clustering is skipped? sorry but this was't so clear to me in the documentation. Also I have a question regarding to the training data. Can the training data be used with normalized facs files or do I need to normalize the training data too by somehow including them in the normalization process?

Thank you so much!

hrj21 commented 1 year ago

Hello, I also cannot find in the documentation how to skip the clustering step and would like to know how to achieve this.

SofieVG commented 1 year ago

Hi all,

If you don't want to include the clustering step, you can use the QuantileNorm.train and QuantileNorm.normalize functions from the CytoNorm package. The CytoNorm.train/normalize functions are actually wrappers around those to apply it on clusters.

Hope this helps! Sofie

On Tue, 27 Jun 2023, 15:40 Hefin Ioan Rhys, @.***> wrote:

Hello, I also cannot find in the documentation how to skip the clustering step and would like to know how to achieve this.

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hrj21 commented 1 year ago

Thank you @SofieVG , that makes sense. Thank you again for contributing such impactful packages to the field!