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What type of data should be used? #32
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In general, I would recommend to work on clean data, but it is not an
absolute requirement. The main things of importance are:
- if you use a clustering step, check that the clustering is at a high
enough level that it does not get impacted by batch effects (e.g. with the
testCV function)
- the data for which quantiles get aligned (i.e. every cluster, or the
whole dataset if no clustering is applied) should be reasonably similar in
percentage positive for all markers you want to use (so that's why we
typically use a control sample)
In my experience clustering gives better results on cleaned data, and
doublets and dead cells might also differ a bit more between the control
samples, resulting in different distributions.
On the other hand, sometimes you want to normalize first to ensure you can
have a more robust preprocessing pipeline for cleaning the data. So as long
as these main requirements are quite ok, you can also apply it on raw data.
It is important data is transformed for correct clustering and quantile
alignment. Either you need to pass a transformList, or you can also
transform upfront, save these preprocessed fcs files and set the
transformList to NULL.
All the best,
Sofie
…On Thu, 19 May 2022, 10:45 mattvtcea, ***@***.***> wrote:
Hi,
I would like to apply CytoNorm to my mass-cytometry dataset but I would
like to know more about the data used.
Do you use raw FCS files (after beads normalization and debarcoding) or do
you "clean" your data first (singlets, live cells etc...)?
Best
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Hi,
I would like to apply CytoNorm to my mass-cytometry dataset but I would like to know more about the data used.
Do you use raw FCS files (after beads normalization and debarcoding) or do you "clean" your data first (singlets, live cells etc...)?
Best
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