# Normalizing counts Because different samples have different total numbers of reads, it would be misleading to calculate differential expression by comparing read count numbers for genes across samples without normalizing for the total number of reads. * Click the **Filtered counts** data node * Click **Normalization and scaling** in the task menu * Click **Normalization** (Figure 1)

The Normalization task set up page will open (Figure 2).

*Figure 2. Normalization task set up page*

Normalization can be performed by samples or by features. By samples is selected by default; this is appropriate for the tutorial data set. Available normalization methods are listed in the left-hand panel. For more information about these options, please see the [Normalize counts](https://help.partek.illumina.com/partek-flow/tutorials/bulk-rna-seq/normalizing-counts) user guide. For this tutorial, we will use the recommended default normalization settings. * Select ![Recommended button](https://1384254481-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FJVEESmJAPppJ3ijFq5aR%2Fuploads%2Fgit-blob-93279f8d7c82849b775c54b55089c7f246f82970%2Fuse%20recomemded.png?alt=media) This adds the Median ratio normalization method, which is suitable for performing differential expression analysis using DESeq2 (Figure 3).

*Figure 3. Select recommended normalization method*

* Click **Finish** to perform normalization A Normalize counts task node and a Normalized counts data node are added to the pipeline (Figure 4)

*Figure 4. Normalize counts task node and Normalized counts data node*

## Additional Assistance If you need additional assistance, please visit [our support page](http://www.partek.com/support) to submit a help ticket or find phone numbers for regional support.