# Is data leakage relevant when scaling across samples?

I have a question about data leakage when pre-processing data for a neural network and whether data leakage actually applies in my instance.

I have variance stabilising transformed genomic data. Because it is genomic data we know apriori that lower numbers translate to lower levels of a gene being made and vice versa. Before input into the neural network, the data are squashed to between 0 and 1 using sklearn:

preprocessing.minmax_scale(data, feature_range=(0,1), axis=1)


The min_max scaling needs to be done across sample (axis=1) as opposed to features because of this apriori assumption of gene levels - low genes need to remain low and vice-versa...

Because of this, my question is: do training samples still need to be scaled separately from test samples as it doesn't seem there is a risk of data leakage here? Is this the correct assumption to make?