# Hierarchical clustering using a gene list

## Opening a gene list as a child spreadsheet

Gene lists can be visualized and their ability to distinguish samples evaluated using a hierarchical clustering heat map. Because of the batch effect in this data set, we will perform hierarchical clustering using batch-corrected intensity values. To do this, we need to open the *fourtreatments* list of differentially expressed genes as a child spreadsheet of the *batch-remove* spreadsheet

* Select **fourtreatments** from the spreadsheet tree
* Select (![](/files/MuYwhbpxS6ON8yjJivja)) to close the spreadsheet
* Select **1-removeresult (batch-remove)** from the spreadsheet tree
* Select **File** from the main tool bar
* Select **Open as child...**
* Select **fourtreatments** using the file browser

The *fourtreatments* spreadsheet will open as a child spreadsheet of *batch-remove* (Figure 1).

![](/files/ku2QXLIWO9Vz0yHf3Wzi)

Figure 1. The fourtreatments spreadsheet is open as a child spreadsheet of bath-remove. Visualizations performed using fourtreatments will pull intensity values from batch-remove.

Visualizations performed using the *fourtreatments* spreadsheet will now use intensity values from the *batch-remove* spreadsheet.

## Hierarchical clustering using a gene list

To invoke hierarchical clustering, follow the steps below.

* Select **Cluster Based on Significant Genes** from the *Visualization* section of the *Gene Expression* workflow
* Select **Hierarchical Clustering**
* Select **OK**
* Select **1-removeresult/1 (fourtreatments)** from the drop-down menu
* Select **Standardize** for *Expression normalization* (Figure 2)

![](/files/EyLmRFUNiGNCln39X4c0)

Figure 2. Configuring the Cluster the significant genes dialog

* Select **OK**

The hiearchical clustering heat map will open in a new tab (Figure 3).

![](/files/OfiexkK4e9LP9IsKAiB0)

Figure 3. Hierarchical clustering of genes with significantly different expression across the treatment groups

Genes without changes in expression are given a value of zero and are colored black. Up-regulated genes have positive values and are displayed in red. Down-regulated genes have negative values and are displayed in green. Each sample is represented in a row while genes are represented as columns. Dendrograms illustrate clustering of samples and genes. To learn more about configuring the hierarchical clustering heat map, see the [Hierarchical Clustering Analysis](/partek/partek-genomics-suite/user-manual/hierarchical-clustering-analysis.md) user guide.

For detailed information about the methods used for clustering, refer to the Partek Manual **Chapter 8: Hierarchical & Partitioning Clustering**.

## 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.


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