# Gene expression quantification and normalization

Normalization is an important step in the analysis of gene expression data, as it helps to control for variations in the amount of RNA that is present in a sample. This is important because the amount of RNA in a sample can be affected by factors such as the health of the organism, the tissue being studied, and the experimental conditions.

Normalization allows researchers to compare gene expression levels between different samples and conditions in a meaningful way, by adjusting for any differences in the amount of RNA present. Without normalization, it would be difficult to accurately compare gene expression levels between samples, as differences in RNA levels could mask differences in gene expression.

Read through subchapter 8.3.4 of this textbook to understand what factors contribute to RNA abundance variances and how to address them (R practice parts are optional). 

{% embed url="<https://compgenomr.github.io/book/gene-expression-analysis-using-high-throughput-sequencing-technologies.html#quantification>" %}

Now, explore the different types of normalization methods. Includes practical exercises with R. &#x20;

{% embed url="<https://hbctraining.github.io/DGE_workshop/lessons/02_DGE_count_normalization.html>" %}


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