LogNormalization

LogNormalization is a data preprocessing technique used to stabilize variance and reduce skew in datasets with wide dynamic ranges, such as gene expression counts. The method combines a normalization step that accounts for sample-specific effects (e.g., sequencing depth) with a logarithmic transformation to compress large values and render the data more amenable to downstream analyses. It is widely used in genomics and other high-throughput fields.

In practice, log normalization typically proceeds by correcting for sample-specific totals, converting counts to relative abundances, applying a scaling factor, and then applying a log transform. A common recipe is to divide each feature by the total counts in the sample, multiply by a scale factor (for example 10,000), and compute log1p of the result. The log1p transformation (log(1+x)) is preferred to handle zero counts. Some workflows apply the log transform after adding a pseudocount.

The goals are to reduce the impact of sequencing depth differences, stabilize variance across expression levels, and improve comparability across samples or cells. This facilitates downstream analyses such as principal component analysis, clustering, and differential expression testing. However, log normalization does not address all biases, and the choice of scale factor, zero handling, and gene filtering can influence results.

Alternatives include variance-stabilizing transformations, regularized log, or methods that model counts directly, such as SCTransform. In single-cell RNA-seq, software tools implement log normalization as a standard first step, with options for customization. Users should consider data characteristics and downstream goals when choosing normalization parameters.