This Perspective examines single-cell RNA-seq data challenges and the need for normalization methods designed specifically for single-cell data in order to remove technical biases.

There are several different types of data normalization. The three most common types are min-max normalization, z-score normalization, and constant factor normalization.

Comparison of expression data requires normalization. The optimum normalization method depends on sample type, with the most common being to normalize to reference genes. It is critical to select ...

There are types of experimental methods that often use normalization to fix the differences induced by factors other than what is immediately being analyzed.

Normalization is one of the corner-stones of database design. Recently some discussion emerged on the need for normalization suggesting denormalization as a more scalable solution.

On a fundamental level, the aim of data normalization is to reduce data redundancy to whatever extent possible. This forces any applications that need to use a specific type of data to access it ...

An article published in the journal PLoS ONE describes the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR were measured to establish a ...

The normalization schemes are applied to in-cylinder PLIF data obtained over a wide range of inhomogeneity levels, and the conditions over which the use of each normalization scheme is appropriate are ...