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There are numerous descriptive statistics available in Partek Genomics Suite. Selecting Stat in

• Select Stat from the main

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• toolbar 
• Select either Descriptive or Correlate 

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• to show available options

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Principal Component Analysis is available by selecting Tools then Discover located in a different menu.

• Select Tools from the main

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• toolbar 
• Select Discover 
• Select Principal Component Analysis 

Applying Multiple Test Correction

If your imported data contains a list of p-values, you may can use any of the available multiple test corrections by by selecting Stat then Multiple Test then Multiple Test Corrections from the main toolbar. . .

• 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

To see a profile plot of A variety of profile plots can be used to visualize the numerical data associated with your imported gene list, select View then Profiles or any of the other available View options form the main toolbar. .

• Select View from the main toolbar
• Selectany 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. 

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

A new track will titled Regions will be added. 

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

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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, select 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. 

Starting with a list of SNPs

A list of SNPs using dbSNP IDs can be imported as a text file and associated with an annotation file as described for a list of genes. The annotation file (genomic database) you use to annotate the SNPs should minimally contain the genomic coordinates (chromosome number and physical position) of the locus. Please be aware that using hg19_dbSNP_v2.pannot as the annotation source is very memory intensive due to the file’s large size. Including the genomic coordinates of the SNPs if you have them (see Starting with a list of Genomic Regions) saves having to look-up genomic coordinates from the dbSNP name.

Novel SNPs, or SNPs that are not found in your annotation source, must be imported as a region list. The only difference would be to use the SNP name in place of a region name.

Annotating SNPs with genes

Starting with a list of SNPs that have been associated with genomic coordinates, you may use Find Overlapping Genes to annotate these SNPs. 

• Select Tools from the main toolbar
• Select Find Overlapping Genes
• Select Add a New Column with the Gene Nearest to the Region from the method dialog

If you have not specified the species or added an annotation file, the Edit Genome dialog will open

• Select the species 
• Select OK
  

The Report Regions from the specified database dialog will open.

• Select your preferred database

This will add 9 columns to the list of SNPs spreadsheet.