Calculating L2 norm (Euclidean norm) to set of genes in time series I have gene expression RNA-seq data of 9 time points (0 hour, 2 hour, ..., 16 hour). I need a plot on which x axis is time point and each data point at a certain time point represents the L2 norm (Euclidean norm) of the difference between the gene expression vector at that time point and the previous time point.

This is the link of my expression data

https://drive.google.com/file/d/1crJSqVdGujy1tFyeoV6E7e-xO9SXteJZ/view?usp=sharing
If you read your matrix into R and saved it as m:

diffMatrix = m[,2:9] - m[,1:8]  # The change per-timepoint
l2n = apply(diffMatrix, 2, function(x) sqrt(sum(x^2)))  # The L2-norm


R also has a norm() function, which can directly produce the L2-norm, but it turns out that this is slower than the simple method shown here. If you click through to the other question you can find somewhat faster implementations of this, but anything will suffice for small datasets like yours.