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I am looking for using squidpy to measure the spatial autocorrelation (like Moran's I ) for genes in spatial dataset.
I was wondering if the result would be different between using raw data vs normalized (and scaled) as input?
For my understanding, is that make more sense to use the normalized gene expression data so we could reduce the sequencing depth effects in different spatial regions?
Thanks a lot!
The text was updated successfully, but these errors were encountered:
Hi @Qirongmao97 , yes you would need to normalize the data before spatial autocorrelation. Scaling is not necessary but result might slightly vary between just normalized (however you wish to do it) and normalized + scaled, HTH
Hi Squidpy developers:
I am looking for using squidpy to measure the spatial autocorrelation (like Moran's I ) for genes in spatial dataset.
I was wondering if the result would be different between using raw data vs normalized (and scaled) as input?
For my understanding, is that make more sense to use the normalized gene expression data so we could reduce the sequencing depth effects in different spatial regions?
Thanks a lot!
The text was updated successfully, but these errors were encountered: