L2 normalization (also known as Euclidean normalization) scales each column (sample) in the expression matrix to have unit Euclidean length. This process makes samples with different sequencing depths more comparable and improves the performance of distance-based analyses.
$$\LARGE x'_{i,j} = \frac{x_{i,j}}{\sqrt{\sum_{i} x_{i,j}^2}} $$
Where:
(\(x_{i,j}\)) is the expression value for feature \(i\) in sample \(j\)
(\(x'_{i,j}\)) is the L2-normalized expression value
L2 normalization can be applied to raw data, but is most commonly used after other normalization methods such as TF-IDF or log normalization to standardize sample-to-sample comparisons.
None
Other normalization parameters:
norm_arcsinh,
norm_default,
norm_library,
norm_log,
norm_osmfish,
norm_pearson,
norm_quantile,
norm_tfidf