<p>When we have a rescaled data with median (divided by median value)(e.g.,metabolome) and perform natural log transformation, do we need to use autoscaling again to perform PCA or to compute euclidian distance?</p>

CC BY-SA 4.0
2020-05-30T20:10:46.180Z
I understand that PCA and computing Euclidian distance need scaled data. Do I need to autoscale again to these median-rescaled data (simply divided by median), Some companies give us rescaled proteome data.
user224050
CC BY-SA 4.0
2020-05-31T00:13:05.340Z
I would like to know when we have median scaled data (divided by median) in metabolomics , some use natural log transformation and to compute euclidian distance and others use natural log transformation with subsequent autoscaling to compute euclidian distance. So I would like to know which methods we should use.
user224050
CC BY-SA 4.0
2020-05-31T02:36:09.163Z
Whether to scale your data again in the PCA function even if you have already had some reasonable ways to scale it beforehand has no clear answer. Theoretically, there is no clear reason to do it or not. I guess you could try both and look at the resulting figure and decide.
Phoenix Mu
<r><pca>
about rescaled data (median)
# about rescaled data (median)

(Asked by: username_0 on 2020-05-30)

When we have a rescaled data with median (divided by median value)(e.g.,metabolome) and perform natural log transformation, do we need to use autoscaling again to perform PCA or to compute euclidian distance?

**Tags:** r, pca
> **Comments:**
> **username_0**: I understand that PCA and computing Euclidian distance need scaled data. Do I need to autoscale again to these median-rescaled data (simply divided by median), Some companies give us rescaled proteome data.  
> **username_0**: I would like to know when we have median scaled data (divided by median) in metabolomics , some use natural log transformation and to compute euclidian distance and others use natural log transformation with subsequent autoscaling to compute euclidian distance. So I would like to know which methods we should use.  
> **username_1**: Whether to scale your data again in the PCA function even if you have already had some reasonable ways to scale it beforehand has no clear answer. Theoretically, there is no clear reason to do it or not. I guess you could try both and look at the resulting figure and decide.  

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