All the operations available for a gene list are available; you may also use the numeric data associated with the genes for visualization, clustering, and statistical operations. 

Descriptive Statistics 

There are numerous descriptive statistics available in Partek Genomics Suite.

• Select Stat from the main toolbar 
• Select either Descriptive or Correlate to show available options

Principal Component Analysis is located in a different menu.

• Select Tools from the main toolbar 
• Select Discover 
• Select Principal Component Analysis 

Applying Multiple Test Correction

If your imported data contains a list of p-values, you can use any of the available multiple test corrections.

• Select Stat from the main toolbar
• Select Multiple Test 
• Select Multiple Test Corrections to launch a dialog with available options 

Plotting numeric data associated with a gene list

A variety of profile plots can be used to visualize the numerical data associated with your imported gene list.

• Select View from the main toolbar
• Select any applicable option  

Genome Browser

If you have imported numerical data associated with genes (like p-values or fold-changes), you can visualize these values in the Genome Browser once an annotation file has been added. 

• Right-click on a row header in the imported gene list spreadsheet
• Select Browse to location

If the annotations have been configured properly, you should see a track for the first column of numerical data, a cytoband track, and an annotation track. You can also add another track to display a second column of numerical data. 

• Select New Track 
• Select Add a track from spreadsheet 
• Select Next >

A new track titled Regions will be added. 

• Select Regions in the track preferences panel to edit it
• Select the other numerical column in the Bar height by drop-down menu

Clustering

If the data is suitable for clustering, access the clustering function through the toolbar, not form a workflow. The workflow implementation assumes the data to be clustered are found on a parent spreadsheet and the list of genes is in a child spreadsheet. Because the data to be clustered is all on one spreadsheet, access hierarchical clustering by selecting Tools from the main toolbar then Discover then Hierarchical Clustering. Consider transposing the spreadsheet if samples are on columns and genes are on rows as Hierarchical Clustering will assume samples are rows and genes are columns. If you only have one column or one row of data, cluster only on the dimension with multiple entries by deselecting either Rows or Columns from What to Cluster or consider using an intensity plot instead. 

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