In this tutorial, the experimental goal is to identify regions with copy number changes in multiple patients. To do this, we will create a list containing deleted and amplified regions across the genome shared by 8 or more samples. 

We want to include all the amplified regions across the genome shared by at least 8 samples in our first criteria (Figure 1). 

The # pass should be 86, indicating that 86 regions meet the criteria. 

 

Amplified is now open in the Analysis tab as a child spreadsheet of segmentation. Although this list contains regions amplified in 8 or more samples, some samples may also contain deletions in the same regions. For downstream analysis, we may want to filter out these regions to create a final list with only amplified regions. Here, we will use the interactive filter. 

This will apply a filter excluding any region with deletions (Figure 2).

 

The yellow and black bar on the right-hand side of the spreadsheet indicates the porportion of rows that have been filtered. Next, we can save the filtered list.

The new spreadsheet is a temporary file. To keep the spreadsheet, we need to save it.

The amplified_only spreadsheet contains 60 rows and includes regions that were amplified in 8 or more samples and not deleted in any sample. 

To create a list of regions only deleted in 8 or more samples, repeat the above steps for deleted regions. You should create a final list, deleted_only, with 92 regions.

Next, we can merge the two lists to create a spreadsheet with both deleted and amplified regions. 

 This spreadsheet, amplified_or_deleted, will be used as the basis for the downstream steps in this analysis.