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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!
The text was updated successfully, but these errors were encountered:
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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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!
The text was updated successfully, but these errors were encountered: