kvantilenormalisering

Kvantilenormalisierung, also known as quantile normalization, is a statistical technique used primarily in the preprocessing of high-throughput data, such as gene expression microarrays and RNA sequencing. Its main purpose is to make the distributions of data across multiple samples comparable by adjusting their statistical properties to a common reference distribution.

The method operates by ranking the data points within each sample and then replacing these original values with the corresponding quantiles from a target distribution, often a uniform or normal distribution. This process ensures that each sample has an identical empirical distribution, which helps to reduce technical variability and batch effects that can obscure true biological differences.

Kvantilenormalisierung is particularly popular in bioinformatics and systems biology because it facilitates the comparison of gene expression levels across different experiments, conditions, or laboratories. It assumes that the overall distribution of gene expression should be similar across samples, and that differences are primarily due to technical rather than biological factors.

While effective in standardizing data, kvantilenormalisierung has limitations; it can mask genuine biological variation if the assumption of similar distributions does not hold. Therefore, its application should be carefully considered in the context of specific experimental designs.

The technique was introduced in the early 2000s and has since become a standard preprocessing step for many genomic data analysis pipelines. It is implemented in several bioinformatics software packages, making it accessible for researchers engaged in large-scale data normalization efforts.

In summary, kvantilenormalisierung is a method that aligns sample data distributions to improve comparability and reduce technical bias, facilitating more accurate downstream biological analysis.