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Principal component analysis (PCA) is a way to explore the overall similarity between samples, visualize possible groupings within the data set, and detect outliers.
Select PCA Scatter Plot from the QA/QC
Figure 1. Principal component analysis showing total allele intensities of normal (blue) and cancer (red) samples. Each dot represents a single sample.
Each dot on the plot corresponds to a single sample and can be thought of as a summary of all normalized marker intensities for the sample. The first categorical column is used to color the plot; here, tumor samples are shown in red and normal samples are shown in blue.
To better view the data, we can rotate the plot.
Click and drag to rotate the plot
Rotating the plot allows us to look for outliers in the data on each of the three principal components (PC1-3). The percentage of the total variation explained by each PC is listed by its axis label. The chart label shows the sum percentage of the total variation explained by the displayed PCs.
We can see that the peripheral blood samples (normal) cluster together whereas the cancer tissue samples (tumor) are more dispersed and show considerable variability. This corresponds well with the known genomic variability of cancer cells.
To view the similarity of paired normal and tumor samples from the same patient, we can connect dots by Subject ID.
Select 4. SubjectID from the Connect by drop-down menu in the upper right-hand corner of the plot tab
Paired tumor and normal samples are now connected by lines, illustrating the range of differences between normal and tumor copy number in the data set (Figure 2).
Figure 2. Lines connect paired tumor and normal samples
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Select to activate Rotate Mode
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.
Select Create Region List from the Copy Number Analysis section of the Copy Number workflow
Select Specify New Criteria
We want to include all the amplified regions across the genome shared by at least 8 samples in our first criteria (Figure 1).
Set Name to Amplified
Set Spreadsheet to 2/segmentation/summary (segment-analysis)
Set Column to 6. Total Amplifications using the drop-down menu
Deselect the box next to Include values less than or equal to
Set Include values greater than or equal to value to 8
The # pass should be 86, indicating that 86 regions meet the criteria.
Select OK
Figure 1. Configuring the Amplified criteria
Select Save to save the list
Select OK to confirm that you would like to save Amplified as a list
Select Close to exit the List Creator dialog
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.
Select the Amplified spreadsheet
Set the Column drop-down list to 8. Total Deletions
Type 0 in the Max box
Select Enter on your keyboard
This will apply a filter excluding any region with deletions (Figure 2).
Figure 2. Interactive filter excluding regions with deletions.
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.
Right-click the Amplified spreadsheet in the spreadsheet trees
Select Clone... from the pop-up menu
Set the Name of the new spreadsheet to amplified_only
Set Create new spreadsheet as a child of spreadsheet to 2/segmentation (segmentation.txt)
Select OK
The new spreadsheet is a temporary file. To keep the spreadsheet, we need to save it.
Select amplified_only in the spreadsheet tree
Set the file name as a****mplified_only
Select Save
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.
Select File from the main taskbar
Select Merge Spreadsheets...
Select the Append Rows tab
Select **2/segmentation/**deleted_only (deleted_only) from the First Spreadsheet drop-down menu
Select 2/segmentation/amplified_only (amplified_only) from the Second Spreadsheet drop-down menu
Name the merged spreadsheet amplified_or_deleted using the Specify Output File (Figure 3)
Select OK
Figure 3. Merging amplified and deleted spreadsheets
Select the new spreadsheet, amplified_or_deleted in the spreadsheet tree
This spreadsheet, amplified_or_deleted, will be used as the basis for the downstream steps in this analysis.
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Starting with copy number estimates for each marker (either taken directly from the vendor’s input file or calculated previously), the next step is to create a list of regions where adjacent markers share the same copy number.
There are two algorithms available for copy number region detection: Genomic Segmentation and Hidden Markov Model (HMM). Both algorithms look for trends across multiple adjacent markers. The genomic segmentation algorithm identifies breakpoints - changes in copy number between two neighboring regions. The HMM algorithm looks for discrete changes of whole number copy number states (e.g., 0, 1, 2 … with no upper limit) and will find regions with those numbers of copies. Therefore, the HMM model performs better in cases of homogeneous samples such as clinical syndromes with underlying copy number aberrations. Genomic segmentation is preferable for heterogeneous samples such as cancer because tumor biopsies often contain “contaminating” healthy tissue and a tumor can have cells with different genomic aberrations.
The number of copies of each marker created in the previous step will be used to detect the genomic regions with copy number variation, i.e., to identify amplifications and deletions across the genome.
Select the IC_IntensitiesSNP6pairedcopynumber spreadsheet in the Analysis tab
Select Detect Amplifications and Deletions from the Copy Number Analysis section of the workflow (Figure 1)
Figure 1. Invoking Detect Amplifications and Deletions
The Detect Amplifications and Deletions dialog will give you the option to choose Genomic Segmentation or HMM Region Detection (Figure 2).
Figure 2. Select a method for detecting amplifications and deletions
Select Genomic Segmentation
Select OK
The Genomic Copy Number Segmentation dialog gives options for setting segmentation parameters and the configuring the region report (Figure 3).
Figure 3. Configuring the Genomic Copy Number Segmentation dialog
Set Minimum genomic markers to 50
Leave the rest of the parameters set to default values as shown (Figure 3)
Select OK
The Genomic Segmentation task is divided into two steps. In the first step, each region is compared to an adjacent region to determine whether both have the same average copy number and whether a breakpoint can be inserted. This is determined by first using a two-sided t-test to compare the average intensities of adjacent regions and then checking whether the corresponding cut-off p-value is below the specified P-value threshold. The genomic size of a region is defined by the number of genomic markers in the region, Minimum genomic markers, while the magnitude of the significant difference between two regions is controlled by Signal to noise, which can be thought of as the difference in copy numbers between the regions. If the t-test is significant, the copy number of the region differs significantly from its nearest neighbors. However, a second step is needed to detemine whether the difference is due to amplificaiton or deletion. In this second step, two one-sided t-tests are used to compare the mean copy number in the region with the expected diploid copy number. For a detailed explanation of the genomic segmenetation procedure, please consult our Genomic Segmentation white paper. For more detailed information about fine-tuning the parameters of your copy number analysis, please consult our guide, Optimizing Copy Number Segmentation.
The resulting spreadsheet, segmentation, shows one row per genomic region per sample (Figure 4). The columns provide the following information:
1-4: Genomic location of the region
5. Sample ID
6. Description of the copy number change
7. The length of the region (in base pairs)
8. The number of markers in the region
9. Markers density in the region (region length in base pairs divided by the number of markers)
10. Geometric mean of the copy number of all the markers in the region
11. Minimum p-value of the one-sided t-tests of the difference of the copy number in column 10 vs. the diploid range
Figure 4. Viewing the segmentation spreadsheet
If desired, you can use Merge Adjacent Regions under Tools in the main toolbar to combine similar regions.
Individual regions of interest can be visualized using Chromosome View.
Right-click a row header in the segmentation spreadsheet
Select Browse to location from the pop-up menu
Alternatively, you can visualize results at the whole chromosome level.
Select the segementation spreadsheet
Select Chromosome View from the QA/QC section of the workflow
The Genomic Segementation track displays the segmentation results (Figure 5). Each line in the track represents a sample. Amplified, deleted, and unchanged regions are shown in red, blue, and white, respectively. The Profile track now also includes information from the segmentation spreadsheet for the selected sample.
Figure 5. Segmentation results shown as regions of amplification and deletion in each sample
Amplified and deleted regions in each sample have been detected, we can compare the regions across multiple samples to detect copy number changes that are shared by multiple samples.
Select Analyze detected segments from the Copy Number Analysis section of the workflow
The Analyze Segments task (Figure 6) can test for associations between copy number variations and sample categories using the χ2 test. In this tutorial, all pairs share the sample phenotype, so we will not test for associations.
Figure 6. Viewing the Analyze segments dialog
Leave all boxes unchecked
Select OK to run the Analyze Segements task
The task generates a new spreadsheet, summary (segment-analysis) (Figure 7), with one region per row. The columns provide the following information:
1-4. Genomic locations of the regions
5. Total number of samples
6-7. Number of samples with amplifications and the average amplified copy number, respectively
8-9. Number of samples with deletions and the average deleted copy number, respectively
10. Total number of samples with copy number abberations
11-12. Number of samples with no change in copy number and the average copy number in those samples, respectively
13. Number of markers in the region
14. Length of the region (in base pairs)
15+. Two columns per sample - the average copy number in each sample as well as the copy number change status of the sample sample (e.g., amplified, deleted, unchanged, depending on the copy number and the threshold for unchanged defined in the Genomic Segementation dialog)
A "?" indicates that a region with the particular characterisitic does not exist or cannot be computed. For example, if a region is not amplified in any of the samples, the average amplified copy number will be shows as "?". This list may be filtered to contain only regions that meet user-specified criteria as discussed in the next section of the tutorial.
Figure 7. Viewing the results of Analyze Detected Segments
To get an overiew of the common abberations in the group of samples over the entire genome we can use View Detected Regions.
Select View Detected Regions
The View Detected Regions dialog (Figure 7) allows you to select the spreadsheet with genomic regions and choose between histogram and copy number classification plots.
Figure 8. View Detected Regions dialog
Select summary (segment-analysis) from the drop-down menu
Select View Histogram
Select OK
The plot will open in a new tab titled Karyogram View (Figure 8).
Figure 9. Viewing amplification and deletion histograms using Karyogram View
The Karyogram View shows each chromosome with red and blue histograms on either side corresponding to amplification and deletion, repsectively. The histogram height reflects the number of samples that share either amplification of deletion a that particular region. For example, the long arms of chromosomes 3 and 7 are amplified in the majority of samples and most samples share a deletion in the long arm of chromosome 4.
Mousing over the chromosome will give cytoband information, mousing over the histogram will give the number of shared regions at each position and the number of samples sharing the type of variation. Both the menu and display may be used to control which chromosomes are displayed; left-click in the menu to toggle a chromosome on/off and right click in the menu or graph to show only that chromosome.
Alternatively, we can use the Copy Number Classification plot to get a more sample-centric view.
Select View Detected Regions
Select View Copy Number Classification
Select OK
The Copy Number Classificaiton also utilizes Karyogram View to provides an overview of all the samples and the copy number of regions on each chromosome (Figure 9).
Figure 10. Viewing the Copy Number Classification plot
Each sample is drawn as a separate column next to the chromosome. Amplified regions are depicted in red, deleted regions in blue, and regions with no copy number change in white. Sample names are given accross the top of each column. For greater detail, try viewing fewer chromosomes.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
The first step in analyzing Affymetrix intensity data is to estimate the number of copies of each marker (allele).
Select Create Copy Number (from Allele Intensities Only)
This launches the Copy Number Creation dialog (Figure 1).
Figure 1. Choosing paired samples or unpaired samples
Choosing Paired samples assumes that each sample has its own reference sample with a common sample ID and generates a copy number spreadsheet. Choosing Unpaired samples uses a common reference, either a single sample or a group of samples, to create both a copy number spreadsheet and an allele ratio spreadsheet.
In this tutorial, we have paired tumor-normal samples and thus can use the Paired samples option.
Select Paired samples
Select OK
The next dialog, Create Copy Number from Pairs, asks you to choose the column shared by each pair and the column that identifies the baseline category (Figure 2).
Figure 2. Creating copy number from pairs
Select 3. Tumor for Column
Select N for Baseline category
Select 4. SubjectID for the Column to match sample pairs
Select OK
This will pair samples based on 4. SubjectID, and set the baseline sample as the sample in the pair with a value of N in the 3. Tumor column. The spreadsheet produced (Figure 3) has a row for each tumor sample. In this tutorial, columns 7+ include copy number estimates for each marker. Column 1-6 are identical to the source spreadsheet.
Figure 3. Viewing the paired copy number spreadsheet
Alternatively, if paired samples are not available or appropriate, the Unpaired samples option can be selected in the Copy Number Creation dialog (Figure 1). Selecting this option opens the Unpaired Copy Number dialog (Figure 4).
Figure 4. Viewing the unpaired copy number dialog
There are several options for creating a reference baseline. First, you can use an existing reference file. These may be distributed by the manufacturer of your array, such as Affymetrix or Illumina, or previously created using Partek Genomics Suite from a set of normal samples. Second, you can use the reference file distributed by Partek. Third, you can choose all the samples from a separately imported spreadsheet. Fourth, you can choose a subset of the samples within the current spreadsheet to pool to create a reference.
In each case, every sample in your spreadsheet will be compared to the referece and two spreadsheets will be generated, a copy number spreadsheet and an allele ratio spreadsheet.
With a list of amplified or deleted regions in our cohort in hand, one of the more interesting questions to ask is what genes have recurrent amplifications or deletions in the data set. To address this question, we can use the Find overlapping genes function to either add a column to our region list with the genes present in each region or create a new list of genes that overlap the regions.
Here, we will create a new spreadsheet with genes that overlap the regions in the amplified_or_deleted spreadsheet.
Select the amplified_or_deleted spreadsheet in the spreadsheet tree
Select Find Overlapping Genes from the Copy Number Analysis section of the workflow
Select Create a New Spreadsheet with Genes that Overlap the Regions from the Find Overlapping Genes dialog (Figure 1)
Select OK
Figure 1. Options in Find Overlapping Genes dialog
To determine what regions in the genome correspond to genes, we need to select an annotation database (Figure 2).
Figure 2. Viewing the Output Overlapping Features dialog. Database files not present on the computer display Download required in red
Partek Genomics Suite offers a variety of possibilities including RefSeq, Ensembl, and GENCODE; however, custom annotations can also be used. If the database file has not been downloaded, Download required. Click OK to download the file, will be listed in red beneath the annotation. Selecting OK will automatically download the file and then run the task.
Select Ensembl Transcripts release 75
Select OK
A new spreadsheet, gene-list, is created as a child spreadsheet of amplified_or_deleted (Figure 3).
Figure 3. Viewing the gene-list spreadsheet, a result of overlapping genes with regions of copy number changes. Each row of the table represents one Ensembl transcript
Each row corresponds to a transcript and the columns are as follows:
1. Genomic coordinates of the transcript
4. Coding strand
5. Transcript ID
6. Gene Symbol
7. Minimum distance of the region to the transcription start site with positive values indicating downstream and negative values indicating upstream
8. Percent overlap with gene indicates how much of the transcript sequence overlaps the region
9. Percent overlap with region indicates how much of the region is overlapped by the transcript
10. + Correspond to the columns 1+ in the segment-analysis spreadsheet
This tutorial uses a spreadsheet generated after data import, but we will illustrate the steps used to import the data in this section.
Select Copy Number from the Workflows drop-down menu
Select Import Samples from the Copy Number workflow
The import dialog will open (Figure 1).
Figure 1. Viewing the Import Copy Number Samples dialog
For Affymetrix arrays, Partek Genomics Suite can import CEL files with allele intensity values and calculate copy number estimates from these intensities. For Agilent, Illumina, NimbleGen, or Affymetrix .CHP files, Partek Genomics Suite can import files containing calculated copy numbers or log ratios.
For this tutorial, we will not be importing CEL files.
Select Cancel to close the import dialog
Later sections of this tutorial will address starting with copy number or log ratios and performing GC wave correction on Affymetrix CEL files.
We can now open the tutorial data file.
Unzip the files to an accessible directory
Select File from the main menu
Select Open...
Select the file IC_Intensities_SNP6.fmt
The spreadsheet will open in the Analysis tab (Figure 2).
Figure 2. Viewing the tutorial data set spreadsheet
This spreadsheet was generated from the import of SNP6 CEL files and shows all 20 samples on rows. Columns 1-6 describe the samples with information such as file names, Subject ID, Gender, etc. The other columns are individual markers from the microarray with the log2 normalized intensities associated with each marker (marker labels are column headers). Opening the IC_Intensities_SNP6.fmt file is equivalent to importing the 20 sample files and adding sample attributes.
This tutorial will illustrate:
Note: the workflow described below is enabled in Partek Genomics Suite version 7.0 software. Please fill out the form on to request this version or use the Help > Check for Updates command to check whether you have the latest released version. The screenshots shown within this tutorial may vary across platforms and across different versions of Partek Genomics Suite.
Copy number analysis asks whether there are regions of the genome with altered abundance. Of particular interest are any genes within those regions and how might a change in gene abundance alter phenotype. Partek Genomics Suite software allows these questions to be answered by analyzing a variety of commercially available assays for copy number analysis. SNP-genotyping arrays with closely spaced genomic markers (Affymetrix and Illumina) and comparative genomic hybridization (CGH) arrays (Agilent, NimbleGen, or custom spotted arrays) can be imported into Partek Genomics Suite and analyzed.
When performing copy number analysis, it is important to remember an inherent limitation of copy number region analysis - the inability to detect copy-neutral events caused by copy-number-neutral loss of heterozygosity (LOH) or copy-number-neutral allelic imbalance. This limitation can be addressed by supplementing copy number analysis with SNP genotyping data. Partek Genomics Suite supports both LOH and allele-specific copy number (AsCN) analysis with dedicated workflows. Tutorials on and analysis are also available.
Ramakrishna M, Williams LH, Boyle SE, Bearfoot JL et al. Identification of candidate growth promoting genes in ovarian cancer through integrated copy number and expression analysis. PLoS One 2010;5(4).
In addition to annotating regions with overlapping genes, other annotations can be to characterize the regions showing copy number variation.
For example, Overlap with known SNPs in the Copy Number workflow gives the option of annotation regions with SNPs from dbSNP or a custom SNP database (Figure 1).
Figure 1. Annotate regions with SNPs from dbSNP
This task adds two column to the region list spreadsheet - the list of SNPs described in each region and the total number of SNPs in the region. If the list of SNPs is very long, you can output a separate list by right-clicking on the row header and select Create list of dbSNP from the pop-up menu.
Another option in the workflow is Test for known abnormalities. Selecting this option compares the regions listed in the region list with a database of genomic abnormalities characteristic of particular diseases or syndromes to find possible matches. Annotation options include a Partek-distributed database of 60 syndromes or a custom database (Figure 2). Please note that the included table of known abnormalities is distributed for research use only.
Figure 2. Test for known abnormalities in your copy number data
If you like to add a custom database, organize the following information by column: the name of the abnormality, chromosome number, start location, and stop location. The input for the task should be a list of aberrations for every sample; do not include unchanged regions in the input or every syndrome will be shown as positive.
Select to open the interactive filter
Select
Select to save the spreadsheet
For more information about using unpaired samples in copy number calculations, please consult our white paper.
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
This gene-list spreadsheet is gene-centric and enables genomic integration. For example, GO and Pathway enrichment can be directly invoked on the gene-list spreadsheet to detect functional groups affected by copy number changes. While not detailed in this tutorial, please feel free to explore these options on your own. For rmore information on enrichment analysis, you can consult the tutorial.
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
Download the zipped tutorial data folder
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
The example data set consists of 20 paired samples from an ovarian cancer study in which a fresh-frozen tumor sample and peripheral blood sample were obtained from 10 female patients (Ramakrishna et al. 2010). All 20 samples were analyzed using the Affymetrix Genome Wide Human SNP Array 6.0. To download the data set, select this link - . The data set is also used for the LOH and AsCN tutorials. The spreadsheet used in this tutorial was generated by importing SNP6 CEL files and annotating them with attributes for each sample. The experimental goal is to identify copy number changes present in multiple patient tumors.
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
Although copy number analysis is a powerful tool for studying genomic aberrations, it lacks the capability to detect changes that are copy-neutral. For example, loss of heterozygosity (LOH) can involve a change in copy number or be copy-neutral. In the former case, LOH could be caused by a hemizygous deletion in which one allele is lost and the other allele remains present (Figure 1, middle panel). This type of LOH can be recognized by copy number analysis or SNP-genotyping. However, in the latter case, an allele is lost initially, but a subsequent amplification of the remaining copy creates a copy-neutral LOH (Figure 1, right panel). This copy-neutral LOH can only be detected when copy number is studied in combination with SNP genotype.
Figure 1. Possible mechanisms of LOH and their impact on copy number. Left panel: heterozygous SNP; numbers indicate the number of copies of each allele (“normal” allele = green, “mutant” = red). Middle panel: hemizygous deletion leading to the loss of normal allele. Right panel: duplication of the ”mutant” allele. The case in the middle panel changes the copy number, while the case in the right panel is copy-number neutral
Copy-neutral events can be detect by combining the copy number workflow with the LOH workflow or the Allele-Specific Copy Number (AsCN) workflow to detect allelic imbalance (AI) (advantages of AsCN over LOH are discussed below). With these approaches, the copy number data are supplemented with SNP genotyping data (currently available with Affymetrix® and Illumina® arrays) to label the genomic regions as amplification without LOH/AI, amplification with LOH/AI, deletion without LOH/AI, deletion with LOH/AI, copy-neutral LOH/AI (Figure 2). The last category, copy-neutral LOH/AI, is the added value of the workflow integration.
An important consideration when choosing between LOH and AsCN analysis is that LOH analysis in the context of cancer has been proven complex and difficult because cancer cells frequently deviate from the diploid state and tumor samples often contain many normal cells. As the proportion of tumor cells in a sample decreases and approaches 50% or less, the capacity to detect the LOH diminishes (Yamamoto et al., Am J Hum Gen 2007). Additionally, in cases where only one of two alleles is amplified, LOH genotyping algorithms fail to call a heterozygote SNP, resulting in a false-positive LOH call.
Figure 2. Integration of copy number workflow with loss of heterozygosity (LOH) or allelic imbalance (AI) under allele-specific copy number (AsCN) workflows enables the identification of copy-neutral events
AsCN analysis, on the other hand, enables reliable detection of allelic imbalance in tumor samples even in the presence of large proportions of normal cells. Unlike LOH, it does not require a large set of normal reference samples. For a heterozygous SNP, a balance is expected between the two alleles (1×A and 1×B, or 1:1 ratio). The AsCN algorithm provides an estimated number of copies of each allele and therefore enables the detection of allelic imbalance even in cases when alleles are amplified or deleted (e.g. 3×A and 1×B). Moreover, LOH can be considered a special case of AI (e.g., 1×A, B allele deleted) (Figure 3). Therefore, AsCN should be the preferred workflow for tumor samples.
Figure 3. Loss of heterozygosity (LOH) as a special case of allelic imbalance. The situation on the left represents a normal heterozygous SNP, with one copy of each allele
Diskin SJ, Li M, Hou C, Yang S, Glessner J, Hakonarson H, Bucan M, Maris JM, Wang K. Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms. Nucleic Acids Res. 2008 Nov;36(19):e126.
Ramakrishna M, Williams LH, Boyle SE, Bearfoot JL, Sridhar A, Speed TP, Gorringe KL, Campbell IG. Identification of candidate growth promoting genes in ovarian cancer through integrated copy number and expression analysis. PLoS One. 2010 Apr 8;5(4):e9983.
Yamamoto G, Nannya Y, Kato M, Sanada M, Levine RL, Kawamata N, Hangaishi A, Kurokawa M, Chiba S, Gilliland DG, Koeffler HP, Ogawa S. Highly sensitive method for genomewide detection of allelic composition in nonpaired, primary tumor specimens by use of Affymetrix single-nucleotide-polymorphism genotyping microarrays. Am J Hum Genet. 2007 Jul;81(1):114-26.
If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.
