> For the complete documentation index, see [llms.txt](https://help.bioinsight.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.bioinsight.illumina.com/icm/analyses/analysis-functionality/task-menu/normalization-and-scaling/normalize-to-housekeeping-genes.md).
# Normalize to housekeeping genes
This normalization is performed on observations (samples) using internal control features (genes). The internal control features, usually housekeeping genes, should not vary among samples\[1].
*Note: The input data node must contain all positive values to compute geometric mean.*
Select **Normalize to housekeeping genes** task in *Normalization and scaling* section in the pop-up menu when you select a count matrix data node, the dialog will list all the features included in the data node on the left panel
Select control genes on the left panel and move them to the right panel. You can also use search box to find the feature and click the plus button to add it to the right panel.
Click **Finish.**
The implementation details is as follows:
Let's use S represents sample, F represents feature (gene), G represents geometric mean, n represents number of samples
1\. Compute geometric mean of all the control genes (features) in each sample S individually, represented by GS1, GS2, GS3... to GSn.
2\. Compute geometric mean of the geometric means across all samples (GS1 to GSn), represented by GS
3\. Compute the scaling factor for each sample, S1=GS1/GS, S2=GS2/GS ... Sn=GSn/GS
4\. Normalize all the gene expression by divided by its sample scaling factor
## References
1. Frank Speleman. Accurate normalization of real-time quantitative RT\_PCR data by geometric averaging of multiple internal control genes. Genome Biology. 2002.
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