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Tutorials

Partek Genomics Suite tutorials provide step-by-step instructions using a supplied data set to teach you how to use the software’s tools. Upon completion of each tutorial, you will be able to apply your knowledge in your own studies.

Gene Expression Analysis
Gene Expression Analysis with Batch Effects
Differential Methylation Analysis
Partek Pathway
Gene Ontology Enrichment
RNA-Seq Analysis
ChIP-Seq Analysis
Survival Analysis
Model Selection Tool
Copy Number Analysis
Loss of Heterozygosity
Allele Specific Copy Number
Gene Expression - Aging Study
miRNA Expression and Integration with Gene Expression
Promoter Tiling Array
Human Exon Array
NCBI GEO Importer

Importing Affymetrix CEL files

Download the data from the Partek site to your local disk. The zip file contains both data and annotation files.

Unzip the files to C:\Partek Training Data\Down_Syndrome-GE or to a directory of your choosing. Be sure to create a directory or folder to hold the contents of the zip file
Copy or move the annotation files (HG-U133A.cdf, HG-U133A.na36.annot, HG-U133A.na36.annot.idx) to C:\Microarray Libraries.

Copying the annotation files to the default library location is done because newer annotation files that are released after the publication of this tutorial may cause the results to be different than what is shown in the published tutorial. If, however, you prefer to download the latest version, you may omit copying the HG-U133A files to C:\Microarray Libraries.

Start Partek® Genomics Suite® and select Gene Expression from the Workflows panel on the right side of the tool bar in the main window (Figure 1)

Figure 1. Selecting the gene expression workflow

Select Import Samples under the Import section of the workflow
Select Import from Affymetrix CEL Files and then select OK
Select the Browse button and select the C:\Partek Training Data\Down_Syndrome-GE folder. By default, all the files with a .CEL extension are selected (Figure 2)

Figure 2. Selecting the folder and CEL files for the experiment

Select the Add File(s) > button to move all the .CEL files to the right panel. Twenty-five CEL files will be processed
Select the Next > button to open the Import Affymetrix CEL Files dialog (Figure 3)

Figure 3. Configuring import files window

Select Customize… to open the Advanced Import Options dialog (Figure 4)

Figure 4. Configuring the Advanced Import Options dialog

Select Library Files… to open the Specify File Locations dialog (Figure 5). This dialog is used to specify the location of the library folder and the annotation files

Figure 5. Specifying Microarray Library files or change the default library directory

Partek Genomics Suite will automatically assign the annotation files according to the chip type stored in the .CEL files. If the annotation files are not available in the library directory, Partek Genomics Suite will automatically download and store them in the Default Library File Folder.

The default library location can be modified by selecting Change... in the Default Library File Folder panel. By default, the library directory is at C:\Microarray Libraries. This directory is used to store all the external libraries and annotation files needed for analysis and visualization. The library directory can also be modified from Tools > File Manager in the main Partek Genomics Suite menu
Select OK (Figure 5) to close the Specify File Locations dialog
Select the Outputs tab from the Advanced Import Options dialog (Figure 6)

Figure 6. Specifying Advanced Import Options to create chip images of and extract the scan date from the CEL files

In the Extract Time Stamp and Date from CEL File panel, make sure the Date button is selected to extract the chip scan date. This information can help you detect if there are batch effects caused by the process time
In the Quality Assess of Gene Expression panel, leave the QC report button unselected. A user guide for the microarray data quality assessment and quality control features is available in the User’s Manual
Select OK to exit the Advanced Import Options dialog
Select Import. The progress bar on the lower left of the Import Affymetrix CEL files dialog will update as .CEL files are imported. Once all files have been imported, the Import Affymetrix CEL Files dialog will close

After importing the .CEL files has finished, the result file will open in Partek Genomics Suite as a spreadsheet named 1 (Down_Syndrome-GE). The spreadsheet should contain 25 rows representing the micoarray chips (samples) and over 22,000 columns representing the probe sets (genes) (Figure 7).

Figure 7. Viewing the main or top-level spreadsheet

For additional information on importing data into Partek Genomics Suite, see Chapter 4 Importing and Exporting Data in the Partek User’s Manual. The User’s Manual is available from the Partek Genomic Suite software main menu Help > User’s Manual. The FAQ (Help > On-line Tutorials > FAQ) may also be helpful. As this tutorial only addresses some topics, you may need to consult the User’s Manual for additional information about other useful features.

It is recommended that you are familiar with Chapter 6 The Pattern Visualization System of the User’s Manual before going through the next section of the tutorial.

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Adding sample information

Twenty-five CEL files (samples) have been imported into Partek Genomics Suite. Sample information must be added to define the grouping and the goals of the experiment.

Select Add Sample Attributes in the Import section of the Gene Expression workflow panel
Choose the option Add Attributes from an Existing Column
Select OK to open the Sample Information Creation dialog

In this tutorial, the file name (e.g., Down Syndrome-Astrocyte-748-Male-1-U133A.CEL) contains the information about a sample and is separated by hyphens (-). Choosing to split the file name by delimiters will separate the categories into different columns

In the Sample Information panel, specify the column labels (Labels 1-4) as Type, Tissue, Subject, and Gender, set each as categorical, and set the other columns as skip (Figure 1). Select OK

Figure 1. Configuring the Sample Information Creation dialog

A dialog window asking if you would like to save the spreadsheet with the new sample attribute will appear. Select Yes
Make column 5. (Subject) random by right-clicking on the column header and selecting Properties from the pop-up menu (Figure 2).

Figure 2. Changing column properties

Select the Random Effect check box from the Properties dialog (Figure 3) then select OK.

Figure 3. Setting column to Random Effect

The column 5. (Subject) will now be colored red, indicating that it is a random effect.

Note: More details on Random vs. Fixed Effects can be found later in this tutorial under the section Identifying differentially expressed genes using ANOVA.

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Exploring gene expression data

At this point in analysis, you should explore the data preliminarily. Do the genes you expected to be differentially regulated appear to have larger or smaller intensity values? Do similar samples resemble each other?

The latter question can be explored using Principal Components Analysis (PCA), an excellent method for reducing and visualizing high-dimensional data.

Select PCA Scatter Plot from the QA/AC section of the Gene Expression workflow

A Scatter Plot tab containing your PCA plot will open (Figure 1).

Figure 1. PCA Scatter Plot tab

In the scatter plot, each point represents a chip (sample) and corresponds to a row on the top-level spreadsheet. The color of the dot represents the Type of the sample; red represents a normal sample and blue represents a Down syndrome sample. Points that are close together in the plot have similar intensity values across the probe sets on the whole chip, while points that are far apart in the plot are dissimilar

Left-clicking on any point in the scatter plot selects that point. A dash with an identifying row number will appear on the selected PCA plot point. The spreadsheet in the Analysis tab will also jump to the corresponding row.

As you can see from rotating the plot, there is no clear separation between Down syndrome and normal samples in this data since the red and blue samples are not separated in space. However, there are other factors that may separate the data.

Color the points by column 4. Tissue and Size the points by column 3. Type
Select OK

Figure 2. Configuring the PCA scatter plot: Color by Tissue, size by Type

Notice now that the data are clustered by different tissues (Figure 3).

Figure 3. PCA scatter plot configured with color by Tissue, size by Type

Another way to see the cluster pattern is to put an ellipse around the Tissue groups.

Open the Plot Rendering Properties dialog and select the Ellipsoids tab
Select Add Ellipse/Ellipsoid
Select Ellipse in the Add Ellipse/Ellipsoid... dialog
Double click on Tissue in the Categorical Variable(s) panel to move it to the Grouping Variable(s) panel (Figure 4)
Select OK to close the Add Ellipse/Ellipsoid... dialog and select OK again to exit the Plot Rendering Properties dialog

Figure 4. Adding Ellipses to PCA Scatter Plot

By rotating this PCA plot, you can see that the data is separated by tissues, and within some of the tissues, the Down syndrome samples and normal samples are separated. For example, in the Astrocyte and Heart tissues, the Down syndrome samples (small dots) are on the left, and the normal samples (large dots) are on the right (Figure 5).

Figure 5. PCA scatter plot with ellipses, rotated to show separation by Type

PCA is an example of exploratory data analysis and is useful for identifying outliers and major effects in the data. From the scatter plot, you can see that the tissue is the biggest source of variation. There are many genes that express differently between the tissues, but not as many genes that express differently between type (Down syndrome and normal) across the whole chip.

The next step is to draw a histogram to examine the samples.

Select Sample Histogram in the QA/QC section of the Gene Expression workflow to generate the Histogram tab (Figure 6)

Figure 6. Histogram tab

The histogram plots one line for each of the samples with the intensity of the probes graphed on the X-axis and the frequency of the probe intensity on the Y-axis. This allows you to view the distribution of the intensities to identify any outliers. In this dataset, all the samples follow the same distribution pattern indicating that there are no obvious outliers in the data. As demonstrated with the PCA plot, if you click on any of the lines in the histogram, the corresponding row will be highlighted in the spreadsheet 1 (Down_Syndrome-GE). You can also change the way the histogram displays the data by clicking on the Plot Properties button. Feel free to explore these options on your own.

The decision to discard any samples would be based on information from the PCA plot, sample histogram plot, and QC metrics. To discard a sample and renormalize the data (without the effects of the outlier), start over with importing samples and omit the outlier sample(s) during the .CEL file import.

Additional Assistance

Identifying differentially expressed genes using ANOVA

Analysis of variance (ANOVA) is a very powerful technique for identifying differentially expressed genes in a multi-factor experiment such as this one. In this data set, ANOVA will be used to generate a list of genes that are significantly different between Down syndrome and normal samples with an absolute difference bigger than 1.3 fold.

The ANOVA model should include Type because it is the primary factor of interest. From the exploratory analysis using the PCA plot, we observed that tissue is a large source of variation; therefore, Tissue should be included in the model. In the experiment, multiple samples were taken from the same subject, so Subject must be included in the model. If Subject were excluded from the model, the ANOVA assumption that samples within groups are independent will be violated. Additionally, the PCA scatter plot showed that the Downs syndrome and normal samples separated within tissue type, so the Type*Tissue interaction should be included in the model.

To invoke the ANOVA dialog, select Detect Differentially Expressed Genes in the Analysis section of the Gene Expression workflow
In the Experimental Factor(s) panel, select Type, Tissue and Subject by pressing and left clicking each factor
Use the Add Factor > button to move the selections to the ANOVA Factor(s) panel
Select both Type and Tissue by holding on your keyboard and left clicking each factor
Select the Add Interaction > button to add a Type * Tissue interaction to the ANOVA Factor(s) panel (Figure 1)

Do NOT select OK or Apply. We will be adding contrasts to this ANOVA model in an upcoming section of the tutorial.

Figure 1. Configuring ANOVA factors and interactions

Random vs. fixed effects – mixed model ANOVA

Most factors in ANOVA are fixed effects, whose levels in a data set represent all the levels of interest. In this study, Type and Tissue are fixed effects. If the levels of a factor in a data set only represent a random sample of all the levels of interest (for example, Subject), the factor is a random effect. The ten subjects in this study represent only a random sample of the global population about which inferences are being made. Random effects are colored red on the spreadsheet and in the ANOVA dialog. When the ANOVA model includes both random and fixed factors, it is a mixed-model ANOVA.

Another way to determine if a factor is random or fixed is to imagine repeating the experiment. Would the same levels of each factor be used again?

Type – Yes, the same types would be used again - a fixed effect
Tissue – Yes, the same tissues would be used again - a fixed effect
Subject - No, the samples would be taken from other subjects- a random effect

You can specify which factors are random and which are fixed when you import your data or after importing by right-clicking on the column corresponding to a categorical variable, selecting Properties, and checking Random Effect. By doing that, the ANOVA will automatically know which factors to treat as random and which factors to treat as fixed.

Nested/Nesting Relationships

The subject factor in the ANOVA model is listed as “5. Subject (3. Type)”, which means that Subject is nested in Type. Partek Genomics Suite can automatically detect this sort of hierarchical design and will adjust the ANOVA calculation accordingly.

Linear Contrasts

By default, an ANOVA only outputs a p-value for each factor/interaction. To get the fold change and ratio between Down syndrome and normal samples, a contrast must be set up.

Select Contrasts… to invoke the Configure dialog
Choose 3**.**Type from the Select Factor/Interaction drop-down list. The levels in this factor are listed on the Candidate Level(s) panel on the left side of the dialog
Left click to select Down Syndrome from the Candidate Level(s) panel and move it to the Group 1 panel (renamed Down Syndrome) by selecting Add Contrast Level > in the top half of the dialog.

Label 1 will be changed to the subgroup name automatically, but you can also manually specify the label name.

Select Normal from the Candidate Level(s) panel and move it to the Group 2 panel (renamed Normal)
The Add Contrast button can now be selected (Figure 2)

Figure 2. Adding a contrast of Down Syndrome and Normal samples

Because the data is log2 transformed, Partek Genomics Suite will automatically detect this and will automatically select Yes for Data is already log transformed? in the top right-hand corner of the dialog. Partek Genomics Suite will use the geometric mean of the samples in each group to calculate the fold change and mean ratio for the contrast between the Down syndrome and normal samples.

Select Add Contrast to add the Down Syndrome vs. Normal contrast
Select OK to apply the configuration
If successfully added, the Contrasts… button will now read Contrasts Included (Figure 3)

Figure 3. ANOVA configuration with contrasts included

By default, Specify Output File is checked and gives a name to the output file. If you are trying to determine which factors should be included in the model and you do not wish to save the output file, simply uncheck this box
Select OK in the ANOVA dialog to compute the 3-way mixed-model ANOVA

Several progress messages will display in the lower left-hand side of the ANOVA dialog while the results are being calculated.

The result will be displayed in a child spreadsheet, ANOVA-3way (ANOVAResults). In this spreadsheet, each row represents a probe set and the columns represent the computation results for that probe set (Figure 4). Although not synonymous, probe set and gene will be treated as synonyms in this tutorial for convenience. By default, the genes are sorted in ascending order by the p-value of the first categorical factor. In this tutorial,Type is the first categorical factor, which means the most highly significant differently expressed gene between Down syndrome and normal samples is at the top of the spreadsheet in row 1.

Figure 4. ANOVA spreadsheet

For additional information about ANOVA in Partek Genomics Suite, see Chapter 11 Inferential Statistics in the User’s Manual (Help > User’s Manual).

Visualizing ANOVA results

Deciding which factors to include in the ANOVA may be an iterative process while you decide which factors and interactions are relevant as not all factors have to be included in the model. For example, in this tutorial, Gender and Scan date were not included. The Sources of Variation plot is a way to quantify the relative contribution of each factor in the model towards explaining the variability of the data.

Select View Sources of Variation from the Analysis section of the Gene Expression workflow with the ANOVA result spreadsheet active

A Sources of Variation tab will appear (Figure 5) with a bar chart showing the signal to noise ratio for each factor accross the whole genome. Sources of variation can also be viewed as a pie chart showing sum or squares by selecting the Pie Chart (Sum of Squares) tab in the upper left-hand side of the Sources of Variation tab.

Figure 5. Sources of Variation tab showing a bar chart

This plot presents the mean signal-to-noise ratio of all the genes on the microarray. All the non-random factors in the ANOVA model are listed on the X-axis (including error). The Y-axis represents the mean of the ratios of mean square of all the genes to the mean square error of all the genes. Mean square is ANOVA’s measure of variance. Compare the bar for each signal to the bar for error; if a factor's bar is higher than error's bar, that factor contributed significant variation to the data across all the variables. Notice that this plot is very consistent with the results in the PCA scatter plot. In this data, on average, Tissue is the largest source of variation.

To view the source of variation for each individual gene, right click on a row header in the ANOVA-3way (ANOVAResults) spreadsheet and select Sources of Variation from the pop-up menu. This generates a Sources of Variation tab for the individual gene. View a few Sources of Variation plots from rows at the top of the ANOVA table and a few from the bottom of the table.

Another useful graph is an ANOVA Interaction Plot.

Right-click on a row header in the ANOVA spreadsheet (Figure 6)
Select ANOVA Interaction Plot to generate an Interaction Plot tab for that individual gene

Figure 6. Calling an ANOVA Interaction Plot for a gene

Generate these plots for rows 3 (DSCR3) and 8 (CSTB). If the lines in the interaction plot are not parallel, then there is a chance that there is an interaction between Tissue and Type. Error bars show standard error of the least squared mean. DSCR3 is a good example of this (Figure 7). We can look at the p-values in column 9, p-value(Type * Tissue) to check if this apparent interaction is statistically significant.

Figure 7. Interaction Plot for DSCR3

We can view the expression levels of a gene for each sample using a dot plot.

Right click on the gene row header and select Dot Plot (Orig. Data) from the pop-up menu. This generates a Dot Plot tab for the selected gene (Figure 8)

Figure 8. Dot Plot showing DSCR3 expression levels for each sample

In the plot, each dot is a sample of the original data. The Y-axis represents the log2 normalized intensity of the gene and the X-axis represents the different types of samples. The median expression of each group is different from each other in this example. The median of the Down syndrome samples is ~6.3, but the median of the normal samples is ~6.0. The line inside the Box & Whiskers represents the median of the samples in a group. Placing the mouse cursor over a Box & Whiskers plot will show its median and range.

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Creating gene lists from ANOVA results

Creating a gene list with the ANOVA Streamlined list manager

Now that you have obtained statistical results from the microarray experiment, you can create new spreadsheets containing just those genes that pass certain criteria. This will streamline data management by focusing on just those genes with the most significant differential expression or substantial fold change. The List Manager can be used to specify numerous conditions for selecting genes of interest. In this tutorial, we are going to create a gene list of gene with a fold change between -1.3 to 1.3 that has an unadjusted p-value of < 0.0005.

Invoke the List Manager dialog by selecting Create Gene List in the Analysis section of the Gene Expression workflow
Ensure that the 1/ANOVA-3way (ANOVAResults) spreadsheet is selected as this is the spreadsheet we will be using to create our new gene list as shown (Figure 1)
Select the ANOVA Streamlined tab.
Set Contrast: find genes that change between two categories panel, to Down Syndrome vs. Normal and select Have Any Change from the Setting drop-down menu

This will find genes with different expression levels in the different types of samples.

In the Configuration for “Down Syndrome vs Normal” panel, check that Include size of the change is selected and enter 1.3 into Change > and -1.3 in OR Change <
Select Include significance of the change, choose unadjusted p-value from the dropdown menu, and < 0.001 for the cutoff

The number of genes that pass your cutoff criteria will be shown next to the # Pass field. In this example, 30 genes pass the criteria.

Set Save the list as A
Select Create to generate the new list A
Select Close to view the new gene list spreadsheet

Figure 1. Creating a gene list from ANOVA results

The spreadsheet Down_Syndrome_vs_Normal (A) will be created as a child spreadsheet under the Down_Syndrome-GE spreadsheet.

This gene list spreadsheet can now be used for further analysis such as hierarchical clustering, gene ontology, integration of copy number data, or be exported into other data analysis tools such as pathway analysis.

Creating a gene list from a volcano plot

Next, we will generate a list of genes that passed a p-value threshold of 0.05 and fold-changes greater than 1.3 using a volcano plot.

Select the 1/ANOVA-3way (ANOVAResults) spreadsheet in the Analysis tab. This is the spreadsheet our gene list will be drawn from
Select View > Volcano Plot from the Partek Genomics Suite main menu (Figure 2)

Figure 2. Generating a Volcano Plot from ANOVA results

Set X Axis (Fold-Change) to 12. Fold-Change(Down Syndrome vs. Normal), and the Y axis (p-value) to be 10. p-value(Down Syndrome vs. Normal)
Select OK to generate a Volcano Plot tab for genes in the ANOVA spreadsheet (Figure 3)

Figure 3. Volcano plot generated from ANOVA spreadsheet

In the plot, each dot represents a gene. The X-axis represents the fold change of the contrast (Down syndrome vs. Normal), and the Y-axis represents the range of p-values. The genes with increased expression in Down syndrome samples are on the right side of the N/C (no change) line; genes with reduced expression in Down syndrome samples are on the left. The genes become more statistically significant with increasing Y-axis position. The genes that have larger and more significant changes between the Down syndrome and normal groups are on the upper right and upper left corner.

In order to select the genes by fold-change and p-value, we will draw a horizontal line to represent the p-value 0.05 and two vertical lines indicating the –1.3 and 1.3-fold changes (cutoff lines).

Choose the Axes tab
Check Select all points in a section to allow Partek Genomics Suite to automatically select all the points in any given section
Select the Set Cutoff Lines button and configure the Set Cutoff Lines dialog as shown (Figure 4)

Figure 4. Setting cutoff lines for -1.3 to 1.3 fold changes and a p-value of 0.05

Select OK to draw the cutoff lines
Select OK in the Plot Rendering Properties dialog to close the dialog and view the plot

The plot will be divided into six sections. By clicking on the upper-right section, all genes in that section will be selected.

Right-click on the selected region in the plot and choose Create List to create a list including the genes from the section selected (Figure 5). Note that these p-values are uncorrected

Figure 5. Creating a gene list from a volcano plot

Note: If no column is selected in the parent (ANOVA) spreadsheet, all of the columns will be included in the gene list; if some columns are selected, only the selected columns will be included in the list.

Specify a name for the gene list (example: volcano plot list) and write a brief description about the list.

The description is shown when you right-click on the spreadsheet > Info > Comments. Here, I have named the list "volcano plot list" and described it as "Genes with >1.3 fold change and p-value <0.05" (Figure 6). The list can be saved as a text file (File > Save As Text File) for use in reports or by downstream analysis software.

Figure 6. Saving a list created from a volcano plot

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Performing hierarchical clustering

The gene list in spreadsheet Down_Syndrome_vs_Normal (A) can be used for hierarchical clustering to visualize patterns in the data.

Under the Visualization section in the Gene Expression workflow, select Cluster Based on Significant Genes

The Cluster Significant Genes dialog asks you to specify the type of clustering you want to perform.

Choose Hierarchical Clustering and select OK
Choose the Down_Syndrome_vs_Normal (A) spreadsheet under the Spreadsheet with differentially expressed genes
Choose the Standardize – shift genes to mean of zero and scale to standard deviation of one under the Expression normalization panel (Figure 1)

This option will adjust all the gene intensities such that the mean is zero and the standard deviation is 1.

Figure 1. Configuring Hierarchical Clustering

Select OK to generate a Hierarchical Clustering tab (Figure 2)

Figure 2. Hierarchical Clustering of Down_Syndrome_vs_Normal (A)

The graph (Figure 2) illustrates the standardized gene expression level of each gene in each sample. Each gene is represented in one column, and each sample is represented in one row. Genes with no difference in expression have a value of zero and are colored black. Genes with increased expression in Down syndrome samples have positive values and are colored red. Genes with reduced expression in Down syndrome samples have negative values and are colored green. Down syndrome samples are colored red and normal samples are colored orange. On the left-hand side of the graph, we can see that the Down syndrome samples cluster together.

For more information on the methods used for clustering, you can refer to Chapter 8: Hierarchical & Partitioning Clustering in Help > User’s Manual. For a tutorial on configuring the clustering plot, please refer to Hierarchical Clustering Analysis

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Adding gene annotations

During data importation, the GeneChip annotation file was linked to the imported data. This linked annotation information can be added as new columns to the ANOVA or gene list spreadsheets. For example, we can add additional annotation to the gene list we created from the ANOVA results as follows:

In the Down_Syndrome_vs_Normal (A) spreadsheet, right click on the second column header 2. ProbesetID and select Insert Annotation from the pop-up menu (Figure 3)

Figure 1. Inserting an annotation

Select Chromosomal Location under the Column Configuration panel (Figure 4). Leave everything else as default
Select OK

Figure 2. Adding Chromosomal Location annotation

Interestingly, of the 23 genes of the Down_Syndrome_vs_Normal (A) spreadsheet, 20 genes are located on chromosome 21. This suggests that the gene expression changes associated with Down syndrome observed in this study are primarily located on chromosome 21, not distributed throughout the genome, an important finding of this study.

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Gene Expression Analysis with Batch Effects

This tutorial will will illustrate:

Importing the data set
Adding an annotation link
Exploring the data set with PCA
Detect differentially expressed genes with ANOVA
Removing batch effects
Creating a gene list using the Venn Diagram
Hierarchical clustering using a gene list
GO enrichment using a gene list

Note: the workflow described below is enabled in Partek Genomics Suite version 7.0 software. Please fill out the form on Our support page 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.

Description of the Data Set

The data for this tutorial is taken from an experiment that examined the effects of four treatment conditions at two time points on estrogen receptor-positive breast cancer cell lines in vitro. Each treatment/time combination has two replicates and there are two control samples for a total of eighteen samples. Gene expression analysis was performed using the Affymetrix GeneChip_®_ Human U95A array. Values are transformed to log base 2 scale by f(x) = log2(x+1).

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Importing the data set

The original experiment is listed on the Gene Expression Omnibus as GSE848; however, this tutorial only uses a subset of the original experiment and should be downloaded from the Partek website tutorial page, Gene Expression Analysis with Batch Effects.

Download the zipped project folder, Breast_Cancer-GE.zip
Unzip the project folder to C:/Partek Training Data/ or a directory of your choosing

This location should be easily accessible. The unzipped Breast_Cancer-GE project folder and a zipped annotation file will be added to the selected directory.

Unzip the included annotation file, HG_U95Av2.na32.annot.rar
Move the annotation file, HG_U95Av2.na32.annot, to the microarray libraries folder

By default, the microarray libraries folder will be located at C:/Microarray Libraries, but the location may vary depending on your operating system and configuration.

Open Partek Genomics Suite
Select () from the main command bar
Navigate to the tutorial folder, Breast_Cancer-GE
Select Breast_Cancer.txt
Select Open (Figure 1)

Figure 1. Opening a data file. The red Partek Genomics Suite icon is shown next to the data file (FMT file format)

The spreadsheet will open as 1 (Breast_Cancer.txt) (Figure 2).

Figure 2. Breast_Cancer.txt data file

The summary at the bottom the spreadsheet shows there are 18 rows and 12,631 columns in the spreadsheet. The first column contains the Filename listing the GEO GSM number. This is also is an identifier for the microarray. Treatment, Time, and Batch are in columns 2, 3, and 4, respectively. Column 6 marks the beginning of the probesets. The data is log2 transformed.

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Adding an annotation link

While many types of data sets are automatically linked with appropriate annotation files upon import, if this does not occur, a spreadsheet can be manually linked with an annotation file.

Right-click Breast_Cancer.txt in the spreadsheet tree
Select Properties (Figure 1)

Figure 1. Selecting file properties for a spreadsheet

Configure the Configure Genomic Properties as shown (Figure 2) with the following steps:

Select Gene Expression from the Choose the type of genomic data drop-down menu
Select Feature in column label
Select Browse...
Select HG_U95Av2.na36.annot.csv from the microarray libraries folder
Select Set Column
Select Gene Symbol from the Choose column containing gene symbol/microRNA name dialog
Select Homo sapiens and hg19 from the Species and Genome Build drop-down menus

Figure 2. Configure the genomic properties dialog as shown

There is now an * after the spreadsheet name in the spreadsheet tree. This indicates an unsaved change has been made to the spreadsheet.

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Exploring the data set with PCA

Principal Components Analysis (PCA) is an excellent method to visualize similarities and differences between the samples in a data set. PCA can be invoked through a workflow, by selecting () from the main command bar, or by selecting Scatter Plot from the View section of the main toolbar. We will use a workflow.

Select Gene Expression from the Workflows drop-down menu
Select PCA Scatter Plot from the QA/QC section of the Gene Expression workflow

The PCA scatter plot will open as a new tab (Figure 1).

Figure 1. Viewing the PCA scatter plot. Each point is a sample. Samples are colored by treatment.

In this PCA scatter plot, each point represents a sample in the spreadsheet. Points that are close together in the plot are more similar, while points that are far apart in the plot are more dissimilar.

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 change the plot properties to better visualize the effects of different variables.

Set Shape to 4. Batch
Set Size to 3. Time
Set Connect to 5. Treatment Combination
Select OK (Figure 2)

Figure 2. Configuring plot properties to color by treatment, shape by batch, size by time, and connect by treatment combination

The PCA scatter plot now shows information about treament, batch, and time for each sample (Figure 3).

Figure 3. PCA scatter plot showing treatment, batch, and time information for each sample. A batch effect is clearly visible.

PCA is particularly useful for identifying outliers and batch effects in data sets. We can see a batch effect in this dataset as samples separate by batch. To make this more clear, we can add an ellipses by Batch.

Select Ellipsoids from the tab
Select Add Ellipse/Ellipsoid
Select Ellipse
Select Batch from the Categorical Vairable(s) panel and move it to the Group Variable(s) panel
Select OK
Select OK to close the dialog

The ellipses help illustrate that the data is spearated by batches (Figure 4).

Figure 4. Ellipses around batch groups show that samples separate by batch

Ways to address the batch effect in the data set will be detailed later in this tutorial.

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Detect differentially expressed genes with ANOVA

Analysis of variance (ANOVA) is a very powerful technique for identifying differentially expressed genes in a multi-factor experiment. In this data set, ANOVA will be used to generate a list of genes that are significantly differentially regulated by each treatment.

Adding factors and interactions

When setting up the ANOVA, the primary factors of interest, Treatment and Time, should be included. We will also include the interaction between Treatment and Time, Treatment * Time, because we are interested in whether different treatments behave differently over time. From our exploratory analysis using PCA, we also know that Batch is a major source of variation and needs to be included. Including Batch as a random factor will allow us to account for the batch effect.

Select Detect differentially expressed genes from the Analysis section of the Gene Expression workflow
Select Treatment, Time, and Batch in the Experimental Factor(s) panel
Select Add Factor > to move the selections to the ANOVA Factor(s) panel
Select both Treatment and Time in the Experimental Factor(s) panel by holding Ctrl on the keyboard while selecting each
Select Add Interaction > to add the Treatment * Time interaction to the ANOVA Factor(s) panel (Figure 1)
Do not select OK or Apply. We still need to add linear contrasts to the ANOVA model

Figure 1. Adding factors and interactions to the ANOVA

Adding linear contrasts

ANOVA will output a p-value and F ratio for each factor or interaction; to get the fold-change and ratio between the different levels of a factor or interaction, linear contrasts, or comparisons, must be added.

Select Contrasts... in the ANOVA dialog (Figure 1)
Select Yes for Data is already log transformed?
Select Treatment * Time from the Select Factor/Interaction drop-down menu

We will add contrasts comparing each of the three treatment groups to the control group.

Select E2 * 8 and E2 * 48 from the Candidate Level(s) panel
Select Add Contrast Level > to move them to the top panel (Group 1) on the right-hand side

The Group 1 panel will be renamed after the contents of the panel. We can specify a name for the group.

Set Label of the top panel to E2
Select Control * 0 from the Candidate Level(s) panel
Select Add Contrast Level > to move it to the bottom panel (Group 2) on the right-hand side
Set Label of the bottom panel to Control

The lower panel (Group 2) is considered the reference level. Because the data is log2 transformed, the geometric mean will be used to calculate the fold change and mean ratio to place both on a linear scale instead of a log scale.

Select **Add Contrast (**Figure 2)

Figure 2. Adding a contrast between E2 vs. Control at all time points.

To examine the time points of each treatment condition separately, we can select Add Combinations instead of Add Contrast. This adds every possible contrast for the levels in the Group 1 and Group 2 panels.

Select E2 * 8 and E2 * 48 from the Candidate Level(s) panel
Select Add Contrast Level > to move them to the top panel (Group 1) on the right-hand side
Select Control * 0 from the Candidate Level(s) panel
Select Add Contrast Level > to move it to the bottom panel (Group 2) on the right-hand side
Select Add Combinations to add contrasts for E2 * 8 vs. Control * 0 and E2 * 48 vs. Control * 0 (Figure 3)

Figure 3. Add Combinations creates contrasts for every combination of levels from the two group panels.

For this tutorial, we will not be considering the time points of each treatment condition individually. We can remove the E2 * 8 vs. Control * 0 and E2 * 48 vs. Control * 0 contrasts.

Select E2 * 8 vs. Control * 0 and E2 * 48 vs. Control * 0 from the contrasts list
Select Delete

We will now add contrasts for the other treatment conditions.

Add contrasts for E2+ICI vs. Control, E2+Ral vs. Control, and E2+TOT vs. Control following the steps outlined for E2 vs. Control

There should now be four contrasts added to the contrasts panel (Figure 4).

Figure 4. Fully configured contrasts for the tutorial

Select OK to add the contrasts to the ANVOA model

The Contrasts... button should now read Contrasts Included in the ANOVA dialog.

Select OK to perform the ANOVA

ANOVA results spreadsheet

The result of the 3-way mixed model ANOVA is displayed in a new spreadsheet, ANOVA-3way (ANOVAResults) that is a child of the Breast_Cancer.txt spreadsheet. In ANOVAResults, each row represents a probe(set)/gene with the columns containing the results of the ANOVA (Figure 5).

Figure 5. Viewing the ANOVA Results spreadsheet. Probe(sets)/genes are on rows and the ANOVA results are on columns.

By default, the rows are sorted in acending order by the p-value of the first factor, which places the most significantly differentially expressed gene between different treatments at the top of the spreadsheet.

Each factor in the ANOVA adds p-value, F value, and SS value columns. F value is a ratio of signal to noise; high values indicate that the probe(set)/gene explains variation in the data set due to the factor. SS value is the sum of squares.

Each contrast in the ANOVA adds p-value, ratio, and fold-change columns. The p-value is calculated using log space. The ratio and fold change are calculated using linear space.

Viewing the sources of variation

Sources of variation captured in the ANOVA can be viewed for the entire data set or for individual probe(sets)/genes.

Select View Sources of Variation from the Analysis section of the Gene Expression workflow

The Sources of Variation plot will open in a new tab (Figure 6).

Figure 6. Viewing the sources of variation plot. Non-random factors are included when ANOVA is run using the default REML modle.

This plot presents the signal to noise ratio accross all probe(sets)/genes for each of the non-random factors and interactions in the ANOVA model. The y-axis represents the average mean square or F ratio, the ANOVA measure of variance, for all the probesets. Each bar is a factor and random error is also included. If the factor has a greater mean F ratio than Error, the factor contrinbuted significant variation to the data set.

Note that Batch is not included as a factor. This is beacuse Batch is a random factor and accounted for by the ANOVA model.

The sources of variation for each probe(set)/gene can be viewed individually.

Right-click on a row header in the ANOVAResults spreadsheet
Select Sources of Variation from the pop-up menu

Additional Assistance

Removing batch effects

By including Batch in the ANOVA model, the variability due to the batch effect is accounted for when calculating p-values for the non-random factors. In this sense, the batch effect has already been removed. However, visualizing biological effects can be very difficult if batch effects are present in the original intensity data used to generate visualizations. We can modify the original intensity data to remove the batch effect using the Remove Batch Effect tool.

Using the Remove Batch Effect tool

The Remove Batch Effect tool functions much like ANOVA in reverse, calculating the variation attributed to the factor being removed then adjusting the original intensity values to remove the variation. Once the variation caused by the batch effect has been removed, tools like PCA or clustering can be used to visualize what the data would look like if the batch effect was not present.

Select the1 (Breast_Cancer.txt) spreadsheet
Select Stat from the main tool bar
Select Remove Batch Effect... (Figure 1)

Figure 1. Invoking the Remove Batch Effect tool

The Remove Batch Effects dialog will open. The tool functions by performing an ANOVA then modifying the original intensities values to remove the effects of the specified factor(s).

Select Treatment, Time, and Batch
Select Add Factor > to add them to the ANOVA Factor(s) panel
Select Batch in the ANOVA Factor(s) panel
Select Add Factor > to add Batch to the Remove Effect(s) of These Factor(s) panel

By default, the results will be displayed in a new spreadsheet. Options to overwrite the current spreadsheet and specify the output file appear in the bottom of the dialog (Figure 2).

Figure 2. Configuring the Remove Batch Effects tool to remove Batch and create a new spreadsheet

Select OK

The new spreadsheet, 1-removeresult (batch-remove) will open in the Analysis tab (Figure 3).

Figure 3. Viewing the new spreadsheet with batch effects removed

Batch effects in PCA

We can visualize the effects of removing the batch effects using PCA.

Select 1 (Breast_Cancer.txt) from the spreadsheet tree
Set Drawing Mode to Mixed
Select the Ellipsoids tab
Select Add Centroid
Add Batch to the Grouping Variable(s) panel
Set the colors of the two centroids as shown (Figure 4) to pink and yellow

Figure 4. Adding a centroid for Batch

Select OK to close the Add Centroid...
Select OK to close the Configure Plot Properties dialog

The two centroids are distinct, showing the batch effect (Figure 5).

Figure 5. Viewing a batch effect using PCA. The batches are shown as the pink (A) and yellow (B) centroids. The clear separation of the centroids indicates a batch effect

Repeat the above steps for 1-removeresult (batch-remove)

For 1-removeresult (batch-remove), the centroids of the two batches overlap, showing that the batch effect has been removed (Figure 6).

Figure 6. Overlapping centroids for batches A and B show that the batch effect has been removed.

Batch effects in ANOVA results visualizations

Visualization of ANOVA results for single probe(sets)/genes also benefits from batch removal. To illustrate this, we first need to repeat our ANOVA using the new batch-remove intesitiy values spreadsheet.

Select the Analysis tab
Select 1-removeresult (batch-remove) in the spreadsheet tree
Select Stat from the main toolbar
Select ANOVA...
Add Treatment, Time, and Batch factors to the ANOVA Factor(s) panel
Add Treatment * Time interaction to the ANOVA Factor(s) panel
Select Contrasts...
Select Treatment from the Select Factor/Interaction drop-down menu
Select Yes for Data is already log transformed?
Set up contrasts of treatment vs. control for E2, E2+ICI, E2+Ral, and E2+TOT (Figure 7)

Figure 7. Configuring ANOVA to comparing treatment groups to control

Select OK to add contrasts
Change output file name to ANOVAResults_batch-remove
Select OK to perform the ANOVA

The ANOVAResults_batch-remove spreadsheet will open in the Analysis tab.

Select the ANOVAResults spreadsheet
Right-click on the row header for row 2, TFF1
Select Dot Plot (Orig. Data) (Figure 8)

Figure 8. Invoking a dot plot from the ANOVAResults spreadsheet

A dot plot for trefoil factor 1 (TFF1) will open (Figure 9). The dot plot shows gene intensity values (y-axis) for each sample. Samples are grouped by Treatment.

Figure 9. Viewing the dot plot for trefoil factor 1 (TIFF1) across different treatment groups

To visualize the batch effect we will make a few changes to the plot.

Select H/V to switch the horizontal and vertical axis
Set Color to Batch
Set Size to Time
Set Connect to Treatment Combination (Figure 10)

Figure 10. Configuring the dot plot (part 1 of 2)

Select the Labels tab
Select Column for In Point Labels
Select Time from the Column drop-down list (Figure 11)

Figure 11. Configuring the dot plot (part 2 of 2)

Select OK

The dot plot now clearly shows the batch effect (Figure 12). Samples within treatment groups are separated clearly between the two batches shown in blue and red.

Figure 12. Viewing a dot plot showing a batch effect. Each dot is a sample. The y-axis is treatment combinations; the x-axis is the expression value of the TFF1 gene. Dots are colored by batch, sized by time, connected by treatment combination, and labeled by time.

To view the effects of batch removal, we can view this dot plot for the ANOVAResults_batch-remove spreadsheet.

Select the Analysis tab
Select ANOVA-3way (ANOVAResults_batch-remove) from the spreadsheet tree
Repeat the steps shown above to create the dot plot for trefoil factor 1

The dot plot invoked from the ANOVAResults_batch-remove) spreadsheet shows that the batch effect has been removed as all the samples no longer clearly separate by color within treatment groups (Figure 13).

Figure 13. Viewing the dot plot that shows batch effect removal. The plot configuration matches Figure 12.

Additional Assistance

Creating a gene list using the Venn Diagram

The List Manager can be used to generate lists of genes by applying criteria such as fold change and false discovery rate (FDR) adjusted p-value thresholds.

Select the Analysis tab
Select ANOVAResults in the spreadsheet tree
Select Create Gene List from the Analysis section of the Gene Expression workflow (Figure 1)

Figure 1. Selecting Create Gene List from the Gene Expression workflow

Select E2 vs. Control from the Contrast panel of the ANOVA Streamlined tab in the List Manager dialog
Deselect the Include size of the change option
Set p-value with FDR < to 0.1 (Figure 2)

Figure 2. Configuring the List Manager using the ANOVA Streamlined filtering options

There should be ~545 probe(sets)/genes that meet this threshold.

Select Create

A new spreadsheet, E2 vs. Control, will be added as a child spreadsheet of Breast_Cancer.txt.

Repeat the steps listed above to create lists for E2+ICI vs. Control (~24 genes), E2+Ral vs. Control (~22 genes), and E2+TOT vs. Control (~177 genes) with the same threashold

Now we can use the Venn Diagram to create a list of genes that are differentially regulated in all treatment groups.

Select the Venn Diagram tab in the List Manager dialog

The Venn Diagram shows overlap between selected gene lists.

Select the four created lists (E-H) in the spreadsheet list in the List Manager dialog by selecting each while holding the Ctrl key on your keyboard

The Venn Diagram will display the number of overlapping and distinct genes from the four lists (Figure 3).

Figure 3. Viewing the Venn Diagram with intersections of four lists of significant genes

The intersection of the four ellipses shows that 14 differentially regulated genes are in common between the four threatment schemes.

Select the region intersecting all four ellipses
Right-click the intersected region
Select Create List From Highlighted Regions
Select Close to exit the List Manager dialog

The new list will appear in the spreadsheet tree with a temporary file name (ptpm).

Select the temporary list in the spreadsheet tree
Save the list as fourtreatments

Additional Assistance

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 () 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).

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)

Figure 2. Configuring the Cluster the significant genes dialog

Select OK

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

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

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

Additional Assistance

GO enrichment using a gene list

Gene Ontology (GO) enrichment analysis compares a gene list to lists of genes associated with biological processes, cellular compartments, and molecular functions to provide biological insights. Once a list of genes has been created, it is possible to see which GO terms the genes are associated with and whether any GO terms are significantly enriched in the gene list.

Select the E2 vs. Control spreadsheet from the spreadsheet tree
Select Gene Set Analysis from the Biological Interpretation section of the Gene Expression workflow
Select Next > to continue with GO Enrichment
Select Next > to continue with 1/E2_vs_Control (E2 vs. Control)
Select Next > to continue with default parameter settings
Select Next > to continue with the default mapping file

A new spreadsheet 1 (GO-Enrichment.txt) will open as a child spreadsheet of E2 vs. Control (Figure 1).

Figure 1. GO Enrichment results spreadsheet

GO terms are shown in rows and are sorted by ascending enrichment p-value.

To visualize the results, we can launch the Gene Ontology Browser.

Select View from the main tool bar
Select Gene Ontology Browser

The Gene Ontology Browser will open in a new tab (Figure 2).

Figure 2. Viewing GO enrichment results in the Gene Ontology Browser

The bar chart shows the GO terms with the highest enrichment scores for the gene list.

To learn more about GO enrichment and using the Gene Ontology Browser, please consult the Gene Ontology Enrichment tutorial.

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Differential Methylation Analysis

Illumina’s MethylationEPIC array interrogates the methylation status of over 850,000 cytosines in the human genome. Because the MethylationEPIC array is closely related to the Infinium HumanMethylation450 BeadChip, the steps presented in this document can be applied to either platform.

This tutorial illustrates how to:

Import and normalize methylation data
Annotate samples
Perform data quality analysis and quality control
Detect differentially methylated loci
Create a marker list
Filter loci with the interactive filter
Obtain methylation signatures
Visualize methylation at each locus
Perform gene set and pathway analysis
Detect differentially methylated CpG islands
Optional: Add UCSC CpG island annotations
Optional: Use MethylationEPIC for CNV analysis
Optional: Import a Partek Project from Genome Studio

Description of the Data Set

The data set accompanying this document consists of sixteen human samples processed by Illumina MethylationEPIC arrays. The data set is taken from a study of DNA methylation in human B cells and B cells infected with Epstein-Barr virus (EBV).

Infecting B cells with EBV in vitro transforms them, making them capable of indefinite growth in vitro. These immortalized cell lines are referred to as lymphoblastoid cell lines (LCLs). LCLs behave similarly to activated B cells, making them useful for expanding T cells in vitro. Because EBV is a carcinogen and immortalized cell growth is a hallmark of cancer, examining the effects of EBV transformation on B cell DNA methylation might shed light on the roles of DNA methylation in tumor development.

The data files can be downloaded from Gene Expression Omnibus using accession number GSE93373 or by selecting this link - Differential Methylation Analysis data set. To follow this tutorial, download the 32 .idat files (note that two .idat files are generated for each array) and unzip them on your local computer using 7-zip, WinRAR, or a similar program.

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Import and normalize methylation data

To follow this tutorial, download the 32 .idat files (note that two .idat files are generated for each array) and unzip them on your local computer using 7-zip, WinRAR, or a similar program. The .idat files can be downloaded in a zipped folder using this link - Differential Methylation Analysis data set.

Store the 32 .idat files at C:\Partek Training Data\Methylation or to a directory of your choosing. We recommend creating a dedicated folder for the tutorial
Go to the Workflows drop down list, select Methylation (Figure 1)

Figure 1. Selecting the methylation workflow

Select Microarray Loci Methylation from the Methylation sub-workflows panel (Figure 2)

Figure 2. Selecting the Illumina BeadArray Methylation workflow

That will open Illumina BeadArray Methylation workflow (Figure 3)

Figure 3. Illumina BeadArray Methylation workflow

Select Import Illumina Methylation Data to bring up the Load Methylation Data dialog
Select Import human methylation 450/850 .idat files (Figure 4)

Figure 4. Selecting human methylation 450/850 .idat file type for import

Select OK
Select Browse... to navigate to the folder where you stored the .idat files

All .idat files in the folder will be selected by default (Figure 5).

Figure 5. Selecting .idat files to import

Select Add File(s) > to move the files to the idat Files to Process pane of the Import Illumina iDAT Data dialog (Figure 6)

Figure 6. Confirming selection of .idat files for import

Select Next >

The following dialog (Figure 7) deals with the manifest file, i.e. probe annotation file. If a manifest file is not present locally, it will be downloaded in the Microarray libraries directory automatically. The download will take place in the background, with no particular message on the screen and it may take a few minutes, depending on the internet connection. In the future, you may want to reanalyze a data set using the same version of the manifest file used during the initial analysis, rather than downloading an up-to-date version. To facilitate this, the Manual specify option in the Manifest File section allows you to specify a specific version. For this tutorial, we will leave this on the default settings.

Figure 7. Selecting manifest file and output file

By default the output file destination is set to the file containing your .idat files and the name matches the file folder name. The name and location of the output file can be changed using the Output File panel.

Select Customize to view advanced options for data normalization

In the Algorithm tab of the Advanced Import Options dialog (Figure 8), there are two filtering options and five normalization options available. The filters allow you to exclude probes from the X and Y chromosomes or based on detection p-value. In this tutorial, we have male and female samples, so we will apply the X and Y chromosome Filter. We will also filter probes based on detection p-value to exclude low-quality probes.

Select Exclude X and Y Chromosomes

Analysis of differentially methylated loci in humans and mice often excludes probes on the X and Y chromosomes because of the difficulties caused by the inactivation of one X chromosome in female samples.

Select Exclude probes using detection p-value and leave the default settings of 0.05 and 1 out of 16 samples.

Figure 8. Advanced Import Options offers choice of normalization method and additional data outputs

Select OK to close the Advanced Import Options dialog
Select Import on the Import Illumina iDAT data dialog

The imported and normalized data will appear as a spreadsheet 1 (Methylation Tutorial) (Figure 9)

Figure 9. Viewing the imported methylation data in a spreadsheet

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Annotate samples

Each row of the spreadsheet (Figure 1) corresponds to a single sample. The first column is the names of the .idat files and the remaining columns are the array probes. The table values are β-values, which correspond to the percentage methylation at each site. A β-value is calculated as the ratio of methylated probe intensity over the overall intensity at each site (the overall intensity is the sum of methylated and unmethylated probe intensities).

Figure 1. Spreadsheet after .idat file import: samples on rows (Sample IDs are based on file names), probes on columns, cell values are functionally normalized beta values (default settings)

Before we can perform any analysis, the study samples need to be organized into their experimental groups.

Select Add Sample Attributes from the Import section of the Illumina BeadArray Methylation workflow
Select Add a Categorical Attribute from the Add Sample Attributes dialog (Figure 2)

Figure 2. Adding sample attributes. Adding Attributes from an Existing Column can be used to split file names into sections, based on delimiters (e.g. _, -, space etc.). Adding a Numeric or Categorical Attribute enables the user to manually specify sample attributes

Select OK

The Create categorical attribute dialog allows us to create groups for a categorical attribute. By default, two groups are created, but additional groups can be added.

Set Attribute name: to Cell Type
Rename the groups B cells and LCLs
Drag and drop the samples from the Unassigned list to their groups as listed in the table below

There should now be two groups with eight samples in each group (Figure 3).

Figure 3. Adding Cell Type attribute as a categorical group

Select OK
Select Yes from the Add another categorical attribute dialog
Set Attribute name: to Gender
Rename the groups Male and Female
Drag and drop the samples from the Unassigned list to their groups as listed in the table below

There should now be two groups with four samples in Male and twelve samples in Female (Figure 4).

Figure 4. Adding Gender attribute as a categorical group

Select OK
Select No from the Add another categorical attribute dialog
Select Yes to save the spreadsheet

Two new columns have been added to spreadsheet 1 (Methylation) with the cell type and gender of each sample (Figure 5).

Figure 5. Annotated beta values spreadsheet

Additional Assistance

Perform data quality analysis and quality control

Principal component analysis (PCA) can be performed to visualize clusters in the methylation data, but also serves as a quality control procedure; outliers within a group could suggest poor data quality, batch effects, mislabeled samples, or uninformative groupings.

Select PCA Scatter Plot from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Scatter Plot tab
Select 2. Cell Type for Color by
Select 3. Gender for Size by
Select () to enable Rotate Mode
Left click and drag to rotate the plot and view different angles (Figure 1)

Each dot of the plot is a single sample and represents the average methylation status across all CpG loci. Two of the LCLs samples do not cluster with the others, but we will not exclude them for this tutorial.

Figure 1. Principal components analysis (PCA) showing methylation profiles of the study samples. Each sample is represented by a dot, the axes are first three PCs, the number in parenthesis indicate the fraction of variance explained by each PC. The number at the top is the variance explained by the first three PCs. The samples are colored by levels of 2. Cell Type

Next, distribution of beta values across the samples can also be inspected by a box-and-whiskers plot.

Select Sample Box and Whiskers Chart from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Box and Whiskers tab

Each box-and-whisker is a sample and the y-axis shows beta-value ranges. Samples in this data set seem reasonably uniform (Figure 2).

Figure 2. Box and whiskers plot showing distribution of M-values (y-axis) across the study samples (x-axis). Samples are colored by a categorical attribute (Cell Type). The middle line is the median, box represents the upper and the lower quartile, while the whiskers correspond to the 90th and 10th percentile of the data

An alternative way to take a look at the distribution of beta-values is a histogram.

Select Sample Histogram from the QA/QC section of the Illumina BeadArray Methylation workflow to bring up a Histogram tab

Again, no sample in the tutorial data set stands out (Figure 3).

Figure 3. Sample histogram. Each sample is a line, beta values are on the horizontal axis and their frequencies on the vertical axis. Two peaks correspond to two probe types (I and II) present on the MethylationEPIC array. Sample colors correspond to a categorical attribute (Cell Type)

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Detect differentially methylated loci

To detect differential methylation between CpG loci in different experimental groups, we can perform an ANOVA test. For this tutorial, we will perform a simple two-way ANOVA to compare the methylation states of the two experimental groups.

Select Detect Differential Methylation from the Analysis section of the Illumina BeadArray Methylation workflow

A new child spreadsheet, mvalue, is created when Detect Differential Methylation is selected. M-values are an alternative metric for measuring methylation. β-values can be easily converted to M-values using the following equation: M-value = log2( β / (1 - β)).

An M-value close to 0 for a CpG site indicates a similar intensity between the methylated and unmethylated probes, which means the CpG site is about half-methylated. Positive M-values mean that more molecules are methylated than unmethylated, while negative M-values mean that more molecules are unmethylated than methylated. As discussed by Du and colleagues, the β-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels.

Because we are performing differential methylation analysis, Partek Genomics Suite automatically creates an M-values spreadsheet to use for statistical analysis.

Select 2. Cell Type and 3. Gender from the Experimental Factor(s) panel
Select Add Factor > to move 2. Cell Type and 3. Gender to the ANOVA Factor(s) panel (Figure 1)

Figure 1. ANOVA setup dialog. Experimental factors listed on the left can be added to the ANOVA model.

Select Contrasts...
Leave Data is already log transformed? set to No
Leave Report comparisons as set to Difference

For methylation data, fold-change comparisons are not appropriate. Instead, comparisons should be reported as the difference between groups.

Select 2. Cell Type from the Select Factor/Interaction drop-down menu
Select LCLs
Select Add Contrast Level > for the upper group
Select B cells
Select Add Contrast Level > for the lower group
Select Add Contrast (Figure 2)

Figure 2. Configuring ANOVA contrasts

Select OK to close the Configuration dialog

The Contrasts... button of the ANOVA dialog now reads Contrasts Included

Select OK to close the ANOVA dialog and run the ANOVA

If this is the first time you have analyzed a MethylationEPIC array using the Partek Genomics Suite software, the manifest file may need to be configured. If it needs configuration, the Configure Annotation dialog will appear (Figure 3).

Select Chromosome is in one column and the physical location is in another column for Choose the column configuration
Select Ilmn ID for Marker ID
Select CHR for Chromosome i
Select MAPINFO for Physical Position
Select Close

This enables Partek Genomics Suite to parse out probe annotations from the manifest file.

Figure 3. Processing the annotation file. User needs to point to the columns of the annotation file that contain the probe identifier as well as the chromosome and coordinates of the probe.

The results will appear as ANOVA-2way (ANOVAResults), a child spreadsheet of mvalue. Each row of the spreadsheet represents a single CpG locus (identified by Column ID).

Figure 4. ANOVA spreadsheet. Each row is a result of an ANOVA at a given CpG locus (identified by the Column ID column). The remaining columns contain annotation and statistical output

For each contrast, a p-value, Difference, Difference (Description), Beta Difference, and Beta Difference (Description) are generated. The Difference column reports the difference in M-values between the two groups while the Beta Difference column reports the difference in beta values between the two groups.

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Create a marker list

To analyze differences in methylation between our experimental groups, we need to create a list of deferentially methylated loci.

Select Create Marker List from the Analysis section of the Illumina BeadArray Methylation workflow
Select LCLs vs. B cells (Figure 1)

Figure 1. Creating a list of significantly differentially methylated loci

Leave Include size of the change selected and set to Change > 2 OR Change < -2
Leave Include significance of the change selected and set to p-value with FDR < 0.05
Select Create
Select Close to exit the list manager

The new spreadsheet LCLs vs. B cells (LCLs vs. B cells) will open in the Analysis tab.

It is best practice to occasionally save the project you are working on. Let's take the opportunity to do this now.

Select File from the main command toolbar
Select Save Project...
Specify a name for the project, we chose Methylation Tutorial, using the Save File dialog
Select Save to save the project

Saving the project saves the identity and child-parent relationships of all spreadsheets displayed in the spreadsheet tree. This allows us to open all relevant spreadsheets for our analysis by selecting the project file.

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Filter loci with the interactive filter

The list, LCLs vs B cells, includes differentially methylated loci for locations across the genome; however, in many cases we may want to focus on loci located in particular regions of the genome. To filter our list to include only regions of interest, we can use the annotations provided by Illumina and the interactive filter in Partek Genomics Suite.

Select LCLs_Vs_B_cells from the spreadsheet tree
Right-click on the Gene Symbol column
Select Insert Annotation (Figure 1)

Figure 1. Adding an annotation column to the ANOVA results

Select the Add as categorical option
Select Relation_to_UCSC_CpG_Island (Figure 2)

CpG islands are regions of the genome with an atypically high frequency of CpG sites. CpG islands and their surrounding regions (termed shelf and shore) include many gene promoters and altered methylation in these regions can have a disproportionate effect on gene expression. For example, hyper-methylation of promoter CpG islands is a common mechanism for down-regulating gene expression in cancer.

Figure 2. Adding chromosome location to ANOVA results

Select OK to add Relation_to_UCSC_CpG_Island as a column in next to 3. Gene Symbol

Now, we can filter probes by their relation to CpG islands.

Select 4. Relation_to_UCSC_CpG_Island for Column

For categorical columns, the interactive filter displays each category of the selected column as a colored bar. For 4. Relation_to_UCSC_CpG_Island, each bar represents one of the categories of the UCSC annotation . To filter out a category, left-click on its bar. Right clicking on a bar will include only the selected category. A pop up balloon will show the category label as you mouse over each bar.

Right-click the Island column to filter out other columns (Figure 3)

Figure 3. Using Interactive Filter tool to filter out probes by annotation. When pointed to a categorical column, the Interactive Filter tool summarises the content of the column by a column chart. Left-click to exclude a category (two columns were excluded, so they are grayed out), right-click to include only

The yellow and black bar on the right-hand side of the spreadsheet panel shows the fraction of excluded cells in black and included cells in yellow. Right-clicking this bar brings up an option to clear the filter.

Now that we have filtered out probes that are not in CpG islands, we will create a spreadsheet containing only these probes.

Right click on the LCLs vs. B cells spreadsheet in the spreadsheet tree panel (Figure 4)

Figure 4. Cloning a filtered spreadsheet creates a new spreadsheet with only the included cells

Select Clone
Rename the new spreadsheet LCLs_vs_B_cells_CpG_Islands using the Clone Spreadsheet dialog
Select mvalues from the Create new spreadsheet as a child spreadsheet: drop-down menu (Figure 5)
Select OK

Figure 5. Renaming and configuring filtered spreadsheet

Specify a name for the spreadsheet, we chose LCLs_vs_B_cells_CpG_Islands, using the Save File dialog
Select Save to save the spreadsheet

You may want to save the project before proceeding to the next section of the tutorial.

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Obtain methylation signatures

The significant CpG loci detected in the previous step actually form a methylation signature that differentiates between LCLs and B cells. We can build and visualize this methylation signature using clustering and a heat map.

Select the LCLs_vs_Bcells_CpG_Islands spreadsheet in the spreadsheet pane on the left
Select Cluster Based on Significant Genes from the Visualization panel of the Illumina BeadArray Methylation workflow
Select Hierarchical Clustering for Specify Method (Figure 1)

Figure 1. Selecting Heirarchical Clustering for clustering method

Select OK
Verify that LCLs_vs_Bcells_CpG_Islands is selected in the drop-down menu
Verify that Standardize is selected for Expression normalization (Figure 2)

Figure 2. Selecting spreadsheet and normalization method for clustering

Select OK

The heat map will be displayed on the Hierarchical Clustering tab (Figure 3).

Figure 3. Hierarchical clustering with heat map invoked on a list of significant CpG loci

The experimental groups are rows, while the CpG loci from the LCLs vs B cells spreadsheet are columns. Methylation levels are compared between the LCLs and B cells groups. CpG loci with higher methylation are colored red, CpG loci with lower methylation are colored green. LCLs samples are colored orange and B cells samples are colored red in the dendrogram on the the left-hand side of the heat map.

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Visualize methylation at each locus

Partek Genomics Suite enables you to visualize each probe and compare the methylation between the groups at a single CpG site level.

Right click row 5_. SBNO2_ in the LCLs_vs_B_Cells_CpG_Islands spreadsheet
Select Browse to Location from the pop-up menu

Figure 1. Browsing to location from spreadsheet with differentially expressed genes

The Chromosome View tab will open, zoomed in to the selected CpG locus in SBNO2 (Figure 2).

Figure 2. Viewing location in Genome Viewer

The Chromosome View visualization is composed of a series of tracks corresponding to annotation files and data files.

RefSeq Transcripts 2017-05-02 (hg19) (+): transcripts coded by the positive strand
RefSeq Transcripts 2017-05-02 (hg19) (-): transcripts coded by the negative strand
Regions: by default, difference in methylation (M-value) between the groups
Heatmap (1/mvalue): M values for all the samples
Barchart (Methylation): methylation level in M value of the selected sample (to select a sample, click on a heat map)
Heatmap (Methylation Tutorial): Beta values for all the samples
Barchart (Methylation): methylation level in Beta value of the selected sample (to select a sample, click on a heat map)
Cytoband: cytobands of the current chromosome
Genomic Label: coordinates on the current chromosome

To modify a track, select it in the Tracks panel to bring up its configuration options panel below the Tracks panel. Let's modify a few tracks to improve our visualization of the data.

Select the Regions track, opens to Profile tab
Select Color tab
Set Color bars by to Difference (LCLs vs. B cells) (Description)
Select Apply to change

This will color regions by up or down methylated.

Select the Heatmap (1/mvalue)
Select Remove Track
Select Bar Chart (Methylation) located directly below the Regions track
Select Remove Track

We can now more clearly see the Difference in M values for the region in the Regions track, the heatmap of beta values in the Heatmap track, and the beta value for the loci of the selected sample in the Bar Chart track.

Select a sample on the heatmap to view its beta value in the Bar Chart track (Figure 3)

Figure 3. Modify the tracks of the Genome Viewer to facilitate visual analysis

The available tracks can be supplemented with a special annotation file that can be built using a UCSC annotation file as the basis. Building and viewing the UCSC annotation file is available as an optional section of the tutorial, Optional: Add UCSC CpG island annotations.

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Perform gene set and pathway analysis

To perform gene set and pathway analysis, we need to create a list of genes that overlap with differentially methylated CpG loci.

Select LCLs_vs_B_cells_CpG_Islands in the spreadsheet tree
Select Find Overlapping Genes from the Analysis section of the workflow

The Output Overlapping Features dialog will open (Figure 1). This dialog allows you to choose the annotation database that will define where gene are located. By default the promoter region will be defined as 5000 base pairs upstream and 3000 base pairs downstream from the transcription start site.

Figure 1. Selecting Finding Overlapping Genes form the main toolbar

Select Ensembl Transcripts release 75 from the Report regions from the specified database options
You can select a name for the new list, we have named it gene-list
Select OK

A new spreadsheet will be created as a child spreadsheet (Figure 2)

Figure 2. Annotating the differentially methylated CpG loci with genes

Partek Genomics Suite offers several tools to help interpret this list of genes. First, let's look at Gene Set Analysis.

Select Gene Set Analysis from the Biological Interpretation section of the Illumina BeadArray Methylation workflow
Select GO Enrichment for Select the method of analysis
Select Next >
Select 1/mvalue/lcls_vs_b_cells_cpg_islands/gene-list (gene-list.txt) for the source spreadsheet
Select Next >
Select Invoke gene ontology browser on the result and leave the rest of the options set to defaults for Configure the parameters of the test (Figure 3)

Figure 3. Configuring the parameters of the test

Select Next >
Select Default Mapping File for Select the method of mapping genes to genes sets
Select Next >

A new spreadsheet will be created with categories ranked by enrichment score and the Gene Ontology Browser will launch to graphically display the results of the spreadsheet (Figure 4). The results show which gene sets are over represented in the list of genes overlapped by differentially regulated CpG loci between the experimental and control groups.

Figure 4. GO enrichment browser showing gene groups overrepresented in the list of genes which overlap with differentially methylated CpG loci

To get a better idea whether genes associated with these GO terms have increated or decreased methylation, we can view the Forest Plot.

Select the Forest Plot tab

Go terms are listed by the number of significantly up-regulated genes, with the percent up-regulated and down-regulated shown in red to green bars. Here, we see that most GO terms show increased methylation in their associated genes (Figure 4).

Figure 5. Gene Ontology Forest Plot

Next, we can perform Pathway Analysis to see which pathways are over represented in the gene overlapped by differentially regulated CpG loci.

Select gene-list from the spreadsheet tree
Select Pathway Analysis from the Biological Interpretation section of the Illumina BeadArray Methylation workflow
Select Pathway Enrichment for Select the method of analysis
Select Next >
Select 1/mvalue/lcls_vs_b_cells_cpg_islands/gene-list (gene-list.txt) for the source spreadsheet
Select Next >
Leave the default selections for the Configure parameters of the test panel
Select Next >
Leave the default selections for the Result File and Select the parameters panels
Select Next > to run the analysis

The Pathway-Enrichment spreadsheet will be added to the spreadsheet tree in Partek Genomics Suite and the Partek® Pathway™ software will open to provide visualization of the most significantly enriched pathway as a pathway diagram (Figure 5). The color of the gene boxes reflects p-values of the associated differentially methylated CpG loci (bright orange is insignificant, blue is highly significant). The Color by option can be changed another column from the gene-list.txt spreadsheet, such as Difference.

Figure 6. : Partek Pathway illustrating one of the pathways overrepresented in the list of genes overlapping the differentially methylated CpG sites.

The Pathway-Enrichment spreadsheet can also be viewed in Partek Pathway by switching to the Pathway-Enrichment section of the menu tree on the left-hand side of the window. From the spreadsheet view, you can select a pathway name to visualize that pathway. Alternatively, you can open a pathway visualization in Partek Pathway from the Pathway-Enrichment spreadsheet in Partek Genomics Suite by right-clicking on a row and selecting Show pathway... from the pop-up menu. Please note that if you have closed Partek Pathway and have reopened it, you will need to import a gene list if you want to color the visualization by attributes form the gene list. For more information about using Partek Pathway, checkout our Partek Pathway Tutorial.

Additional Assistance

If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.

Detect differentially methylated CpG islands

The approach described in previous sections relies on ANOVA to detect differentially methylated CpG sites and takes individual sites as a starting point for interpretation. Since ANOVA compares M values at each site independently, this strategy is robust to type I/type II probe bias.

An alternative could be to first summarize all the probes belonging to a CpG island region (i.e. island, N-shore, N-shelf, S-shore, S-shelf) and then use ANOVA to compare regions across the groups. Since the summarization will include both type I and type II probes, you may want to split the analysis in two branches and analyze type I and type II probes independently. To do this, we need to annotate each probe as type I or type II.

Select the mvalue spreadsheet
Select Transform from the main toolbar
Select Create Transposed Spreadsheet... from the Transform drop-down menu (Figure 1)

Figure 1. Creating a transposed spreadsheet

Select Sample ID for Column: and numeric for Data Type:
Select OK

A new temporary spreadsheet will be created with a row for each probe and columns for each sample.

Right-click on column 1. ID to bring up the pop-up menu
Select Insert Annotation
Select Add as categorical
Select Infinium_Design_Type and UCSC_CpG_Islands_Name from the Column Configuration options (Figure 2)

Figure 2. Adding Infinium design type and CpG island annotations

Select OK to add the Inifinium design type and UCSC CpG island name as categorical columns on the spreadsheet

Now, we can use the interactive filter to create separate spreadsheets for type I and type II probes.

Select 2. Infinium_Design_Type from the drop-down menu if not selected by default
Left-click the type I column to exclude it
Right-click the temporary spreadsheet in the spreadsheet tree to bring up the pop-up dialog
Select Clone... (Figure 3)

Figure 3. Creating a probe list with only Infinium type II probes

Name the new spreadsheet female_only_typeII_probes
Select OK
Save the created spreadsheet, we chose the file name female_only_typeII_probes
Repeat process to create a spreadsheet for type I probes

The temporary spreadsheet is no longer needed so we can close it.

We can use these spreadsheets to generate lists of M values at CpG island regions

Select spreadsheet female_only_typeII_probes
Select Stat from the main toolbar
Select Column Statistics... under Descriptive (Figure 4)

Figure 4. Selecting column statistics

Add Mean to the Selected Measure(s) panel
Select Group By and set it to 3. UCSC_CpG_Islands_Name (Figure 5)

Figure 5. Configuring column statistics

Select OK

The new temporary spreadsheet has one CpG island region per row (Figure 6), samples on columns, and the values in the cells represent the mean of M values of all the CpG probes in the region.

Figure 6. New spreadsheet with average M values for probes at each CpG island; probes not at CpG islands are collected into the first row "- Mean"

Note the first row, with label “– Mean”. It corresponds to all the probes that map outside of UCSC CpG islands. As it is not needed for the downstream analysis, we will remove it.

Right-click on the row header for Mean
Select Delete to remove the row

The final step is to transpose the data back to its original orientation.

Select Transform from the main toolbar
Select Create Transposed Spreadsheet... from the Transform drop-down menu
Select 2. Level for Column: and numeric for Data Type:
Select OK

The layout of the new transposed spreadsheet is as follows: one sample per row with CpG island regions on columns; cell entries correspond to mean methylation status of the region (Figure 7). The column with a blank value for the column header is the average of all probes not associated with CpG island regions. You can delete this column if you like.

Figure 7. Spreadsheet with average M values of probes in each CpG island for each sample

Right-click the transposed spreadsheet, 2_transpose
Select Save as... from the pop-up menu
Name it mvalues_typeII_probes_CpG_islands

The mvalues_typeII_probes_CpG_islands spreadsheet can be used as a starting point for ANOVA and other analyses. You can also repeat the steps above to create an equivalent spreadsheet for type I probes.

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Optional: Add UCSC CpG island annotations

Partek Genomics Suite software can view annotation .BED files as tracks in the Genome Viewer. We can add a CpG islands track to the Genome Viewer using the UCSC Genome Browser CpG islands annotation.

Go to UCSC Genome Browser
Select Table Browser under Tools in the main command bar of the webpage (Figure 1)

Figure 1. Navigating to the Table Browser at the UCSC Genome Browser website

Configure the Table Browser page as shown (Figure 2)

Figure 2. Configuring the Table Browser to output CpG Islands BED file

Set assembly to Feb. 2009 (GRCh37/hg19)
Set group to Regulation
Set track to CpG Islands
Set table to cpgIslandExt
Set output format to BED
Set output file to cpg.bed
Select get output

The Output cpgIslandExt as BED page will open.

Select get BED to download a compressed folder containing the BED file
Unzip the file using 7-Zip, WinRAR, or a similar program of your choice to a location you will be able to find

Next, we can import the BED file into Partek Genomics Suite.

Select Genomic Database... under Import under File in the main toolbar in Partek Genomics Suite (Figure 3)\

Figure 3. Importing the CpG Islands map BED file

Select the file cpg.bed

The BED file will open as a new spreadsheet.

Change the spreadsheet name to UCSC CpG Island Annotation and save it

For this region list, you can also calculate the average beta values for the probes in each island per sample and detect differential methylated CpG islands regions. Detailed information on how to get average beta value for each CpG can be found in the Determining the average values for a region list section of Starting with a list of genomic regions.

Additional Assistance

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Optional: Use MethylationEPIC for CNV analysis

Although the 450K and MethylationEPIC arrays were initially designed to analyze DNA methylation, they are essentially a dense SNP array and can be used for copy number analysis (Feber et al. 2014). The probe intensity data is easily parsed from the idat files by using the Additional Probe Data Spreadsheet Selection dialog (Figure 1) when importing the raw data. Examining the raw intensity data can also be useful for QA/QC purposes.

Follow the steps for importing Illumina methylation data detailed in until you reach the Import Illumina iDAT Data dialog with Manifest File and Output File panels (Figure 1).

Figure 1. Customizing output during data import

Select Customize... to open the Advanced Import Options dialog
Choose No normalization in the Normalization section of the Algorithm tab
Select the Outputs tab (Figure 2)

Figure 2. Selecting additional probe data to include during data import

Detection p-values. This is the confidence score that the signal of a probe was significantly higher than the background defined by negative control probes. Selecting this checkbox produces a spreadsheet ending with '_detectionp' in addition to the spreadsheet containing beta values. Each row of the _detectionp spreadsheet will be a different sample and the sample names will end in '_detectionp'. This spreadsheet can be used to filter out probes that do not show signal above background.

Probe Intensity. This is the sum of the methlyated and unmethylated intensities per probe. Selecting this checkbox produces a spreadsheet ending with ‘_probe’ in addition to the spreadsheet containing beta values. Each row of the _probe spreadsheet will be a different sample and the file names will also end in ‘_probe.’ The probe intensity values will be log2 transformed by default (note that the beta values are not log2 transformed).

Probe Signal. This option will become available if Probe Intensity is selected. Selecting this checkbox produces a spreadsheet ending with ‘_probe.’ The methylated and unmethylated intensities are shown on separate rows for each sample, in addition to the summed values. The sample names will end in ‘_meth’ or ‘_unmeth’ for methylated and unmethylated values, respectively. The probe intensity values will be log2 transformed by default.

Raw Probe Intensity. This is the sum of the raw red and green signal intensities per probe. Selecting this checkbox produces a spreadsheet ending with ‘_raw’ in addition to the spreadsheet containing beta values. Each row of the spreadsheet will be a different sample and the file names will also end in ‘_raw.’ The raw probe intensity values will be log2 transformed by default.

Raw Probe Signal. This option will become available if Raw Probe Intensity is selected. Selecting this checkbox produces a spreadsheet ending with ‘_raw.’ The red and green intensities will be shown on separate rows for each sample, in addition to the summed values. The sample names will end in ‘_red’ or ‘_green’ for red and green values, respectively. The raw probe intensity values will be log2 transformed by default.

Antilog Probe Intensity Values. Selecting this checkbox will show the probe intensity data without log2 transformation.

Create NCBI GEO Submission Spreadsheets. Generates matrix processed and matrix signal intensities spreadsheets for GEO submission.

How you proceed depends on your study design. Here is an example series of steps to prepare the tutorial data set for copy number analysis:

Select Probe Intensity and Antilog Probe Intensity Values (Figure 2)
Select OK to close the Advanced Import Options dialog
Select Import to import the data and perform the selected normalization method
Select the (_probe) spreadsheet from the spreadsheet tree
Delete any samples with _detectionp names
Select Transform from the main toolbar
Select Normalize to baseline

Configure the Normalize to Baseline 1 dialog as shown (Figure 3)

Select Use control set form this spreadsheet
Set Control Category to B cells
Select Ratio to baseline from the Normalization Method section
Select After ratio apply log base 2
Select New Spreadsheet from the Configure Output section

Figure 3. Configuring normalize to baseline

Select OK to generate the spreadsheet

This spreadsheet contains copy number values per probe in log2 space (i.e. diploid = 0). Prior to performing copy number analysis, you can normalize for local GC abundance.

Select Transform
Select Adjust Based on Local GC Content...
Click OK to run Local GC Adjustment (Figure 4)

Figure 4. Adjusting for local GC content

The GC adjusted spreadsheet is the starting spreadsheet for copy number analysis. You can now switch over to the Copy number workflow, skip the Create copy number step, and begin with the Detect amplifications and deletions step. Consult the user's guide for the copy number workflow for subsequent steps.

References

Feber A, Guilhamon P, Lechner M, et al. Using high-density DNA methylation arrays to profile copy number alterations. Genome Biology. 2014;15(2):R30. doi:10.1186/gb-2014-15-2-r30.

Additional Assistance

Optional: Import a Partek Project from Genome Studio

An Illumina-type project file (.bsc format) can be imported in Illumina’s GenomeStudio® (please note: to process 450K chips, you need GenomeStudio 2010 or newer) and exported using the Partek Methylation Plug-in for GenomeStudio. For more information on the plug-in, please see the . The plug-in creates six files: a Partek project file (*.ppj), an annotation file (*.annotation.txt), files containing intensity values (*.fmt and *.txt), and files containing β-values (*.fmt and *.txt) (Figure 1).

Figure 1. Output of Partek Methylation Plug-in for GenomeStudio

To load all the files automatically, open the .ppj file as follows.

Select Methylation from the Workflows drop-down menu
Select Illumina BeadArray Methylation from the Methylation sub-workflows section
Select Import Illumina Methylation Data from the Import section
Select Load a project following Illumina GenomeStudio export from the Load Methylation Data dialog

Additional Assistance

Partek Pathway

Partek Pathway provides a visualization tool for pathway enrichment spreadsheets utilizing the KEGG database. 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.

Description of the Data Set

The pathway enrichment analysis illustrated in this user guide uses the . This data set is also used in our .

Download and save the zipped project folder in an accessible location on your computer. The project folder for the tutorial will be created in the same location the zipped project folder is stored.

Importing the Data Set

Import the project using the zipped project importer in Partek Genomics Suite.

Select File from the main toolbar
Select Import
Select Zipped Project...
Choose the zipped project folder, miRNA_tutorial_data

The project will open with three spreadsheets:

1. Affy_miR_BrainHeart_intensities,

2. Affy_HuGeneST_BrainHeart_GeneIntensities,

3. ANOVAResults gene.

Additional Assistance

Performing pathway enrichment

Before performing pathway enrichment, we need to create a gene list from the ANOVA results.

Creating a list of significant genes

Select Gene Expression from the workflows drop-down menu
Select the ANOVAResults gene spreadsheet
Select Create Gene List from the Analysis section of the Gene Expression workflow
Select Brain vs. Heart from the List Manager dialog (Figure 1) leaving the other options as defaults
Select Create

Figure 1. Configuring the list manager dialog

A new list of 420 genes will be created as a child spreadsheet of 1 (ANOVAResults gene).

Select Close to exit the List Manager dialog

Performing pathway enrichment analysis

Select the new gene list, Brain vs. Heart
Select Pathway Analysis from the Biological Interpretation section of the Gene Expression workflow
Select Next > to continue with Pathway Enrichment

Select Next > to continue with the Brain vs. Heart spreadsheet
Select Next > to continue with default settings for Fisher's Exact test
Select Next > to continue with Homo sapiens and 4. Gene Symbol as parameters

Partek Pathway will now open. If this is your first time using Partek Pathway on the selected species, Partek Pathway will automatically download the KEGG information needed for the analysis. Once the pathway enrichment calculation has been performed, a new spreadsheet, Pathway-Enrichment.txt, will be added as a child spreadsheet of Brain vs. Heart and Partek Pathway will launch (Figure 2).

Figure 2. Partek Pathway displaying the most significantly enriched pathway from the gene list

The pathway currently displayed has the highest enrichment score. Both Partek Genomics Suite and Partek Pathway offer options for analyzing the results of pathway enrichment analysis. The next two sections of the user guide will show the options for analyzing the results of pathway enrichment in each program.

Additional Assistance

Analyzing pathway enrichment in Partek Genomics Suite

Pathway enrichment generates a results spreadsheet, Pathway-Enrichment.txt, visible in both Partek Genomics Suite (Figure 1) and in Partek Pathway.

Figure 1. The pathway enrichment spreadsheet is visible in both Partek Genomics Suite (shown here) and Partek Pathway

Contents of the pathway enrichment spreadsheet

The spreadsheet includes 13 columns with information for each pathway represented in the source gene list.

1. Pathway Name - the name of the KEGG pathway

2. Database - the source database for the pathway annotation

3. Enrichment score - the negative natural log of the enrichment p-value derived from the contingency table (Fisher's Exact test) or the Chi-squared test

4. Enrichment p-value - the enrichment p-value derived from the contingency table (Fisher's Exact test) or the Chi-squared test

5. % genes in pathway that are present - the percentage of genes from the pathway that are present in the source gene list

6. Tissue score, 7. Replicate score, 8. Brain vs. Heart score - for each factor, interaction, and contrast in the ANVOA results spreadsheet, a separate score is calculated. This is derived form the negative log (base 10) of the average p-value for genes within the pathway for each factor. A high score indicates that the genes that fall into the pathway have a low p-value for the given factor.

9. # genes in list, in pathway - number of genes from the list in the pathway

10. # genes not in list, in pathway - number of genes from the pathway, not in the list

11. # genes in list, not in pathway - number of genes in list, not in the pathway

12. # genes, not in list, not in pathway - number of genes not in the pathway or the list that are included in KEGG database pathways for the species

13. Pathway ID - KEGG pathway ID

Tasks available in Partek Genomics Suite

In Partek Genomics Suite, we can view several new options that are available for each pathway (row) in the Pathway-Enrichment.txt spreadsheet.

Right-click the row header of any row in the Pathway-Enrichment.txt spreadsheet (Figure 2)

Figure 2. The Pathway-Enrichment.txt spreadsheet in Partek Genomics Suite

The new options include:

Export genes in pathway, which creates a child spreadsheet of Pathway-Enrichment.txt that contains all the genes from the selected pathway(s) (Figure 3). This new spreadsheet includes gene symbols and their pathway.

Figure 3. Spreadsheet with all genes in pathway. Includes gene symbols and pathway.

Export genes in list and in pathway, which creates a child spreadsheet of Pathway-Enrichment.txt that contains the genes from your list that are present in the selected pathway(s) (Figure 4). This new spreadsheet includes gene symbols and their pathway.

Figure 4. Spreadsheet with genes only in list and pathway. Includes gene symbols and pathway.

Create Gene List, which creates a new child spreadsheet of the ANOVA results spreadsheet that contains the genes from your list that are present in the selected pathway(s) (Figure 5). This new spreadsheet includes all information for each gene from the ANOVA results spreadsheet. However, this list does not indicate the pathway of each gene.

Figure 5. Spreadsheet with genes in list and pathway. Includes all information from ANOVA results for each gene.

Show Pathway, which opens the selected pathway map in Partek Pathway.

Additional Assistance

Analyzing pathway enrichment in Partek Pathway

Partek Pathway is a separate program from Partek Genomics Suite with a distinct user interface (Figure 1).

Figure 1. Partek Pathway

The Project Elements panel (Figure 2) displays the selected pathway, the original gene list, the Pathway Enrichment spreadsheet, and the library references that were used for the pathway analysis. The Project Elements panel is used to navigate between open pathway diagrams and spreadsheets.

Figure 2. Project Elements panel

Select the Brain vs. Heart spreadsheet under Gene Lists

The Brain vs. Heart gene list we created earlier will open (Figure 3). The spreadsheet can be sorted by any column by left-clicking a column header; the first click will sort by ascending values, the second click will switch to descending values.

Figure 3. Viewing a gene list in Partek Pathway

Select the Pathway-Enrichment spreadsheet under Pathway Lists

The Pathway-Enrichment.txt spreadsheet will open (Figure 4). This spreadsheet has the same contents as the Pathway-Enrichment.txt spreadsheet in Partek Genomics Suite. Selecting any of the pathway names will open its pathway diagram. The spreadsheet can be sorted by any column.

Figure 4. Viewing the Pathway Enrichment spreadsheet in Partek Pathway

Select the GABAergic synapse pathway on the Pathway-Enrichment spreadsheet or the Project Elements panel

The GABAergic synapse pathway diagram will open. Genes in the pathway are shown as boxes. The color of the boxes is set by the Configuration panel (Figure 5).

Figure 5. The Configuration panel

Any numerical column from the source gene list can be used to color the gene boxes. While significant p-values indicate a difference between the categories, they give no information about upregulation or downregulation of the pathway. We can overlay fold-change information on the pathway diagram.

Select Brain vs. Heart: Fold-Change(Brain vs. Heart) from the drop-down menu

The pathway diagram now shows fold change for each gene in the pathway included in the gene list (Figure 6). Genes not in the gene list remain black.

Figure 6. GABAergic synapse pathway diagram showing fold-changes for genes in the gene list

The colors and range of can be changed using the Color By panel.

Select the red color square next to Max
Select yellow from the color picker interface
Select OK
Select the green color square next to Min
Select pale blue from the color picker interface
Select OK

We can see that all the colored genes in the GABAergic synapse pathway are yellow (Figure 7), indicating that they are up-regulated.

Figure 7. Changing colors in the Pathway Diagram; up-regulated genes are yellow and down-regulated genes are teal

We can select a gene to learn more about it.

Right-click GABAB (Figure 8)

Figure 8. Learn more about any gene on a pathway diagram by right-clicking

Options available include:

Look up on KEGG - opens the KEGG page for the pathway on GenomeNet (genome.jp) in your web browser

Ensembl - under External Links, opens the page for the selected Ensembl ID on ensemble.org

UniProt - under External Links, opens the page for the selected UniProt ID on uniprot.org

Jump to ___ on "___" - opens the source gene list in Partek Pathway to the row of the selected gene

The pathway database can be searched for genes of interest using the Search panel.

Type NSF in the search box

Pathways containing NSF appear on the right-hand side in the Search Results section in alphabetical order (Figure 9).

Figure 9. Using the search panel to find pathways containing a gene of interest

If multiple species or libraries have been loaded, the Filter Options section on the left-hand side can be used to choose which species and libraries to search.

Double click on Synaptic vesicle cycle in the Search Results section

The selected pathway, Synaptic vesicle cycle, will open in the Pathway Diagram panel (Figure 10).

Figure 10. Opening a pathway diagram from search results

On the right-hand side of the Partek Pathway window, we see the Pathway Detail panel (Figure 11).

Figure 11. The Pathway Detail panel

Select KEGG_Gene to open the list of genes in the pathway

Selecting a gene in the list will highlight it in the pathway diagram (Figure 12).

Figure 12. Selecting a gene in a pathway diagram using the Pathway Detail panel

Another way to select and view a pathway is browsing the Pathway Libraries.

The Pathway Libraries dialog will open (Figure 13).

Figure 13. Downloading and browsing pathway libraries

In the upper section of the dialog, you can view available KEGG libraries and download them by selecting the Download Library link. Selecting a pathway opens it in the lower section of the dialog.

In the lower section of the dialog, you can view a list of all the pathways in the selected pathway library. You can also open any pathway from the selected library in the Pathway Diagram panel.

Select Adherens Junction
Select View Pathway to open the pathway diagram

We can use the Project Elements panel to close an open pathway diagram or list.

Right-click Adherens Junction in the Project Elements panel
Select Delete from the pop-up menu to close the diagram (Figure 14)

Figure 14. Closing a pathway diagram

The Search panel and Pathway Libraries can also be used to open pathway diagrams for pathways without any open gene or pathway lists.

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Gene Ontology Enrichment

Gene ontology (GO), enrichment analysis has been incorporated into the gene expression, microRNA expression, exon, copy number, tiling, ChIP-Seq, RNA-Seq, miRNA-Seq and methylation workflows. The Gene Ontology Consortium website provides an excellent overview for new and experienced users of GO analysis. In brief, the common nomenclature of genes and gene products has been used to group genes into a functional hierarchy. This enables analyses to be compared across all types of genomic data, even data from different species. A broader understanding of experimental results is possible by grouping genes of interest into biological processes, cellular components and molecular functions of the genes. With the GO enrichment tool in Partek® Genomics Suite® you can take a list of genes (e.g. significantly differentially expressed genes) and see how they group in the functional hierarchy. This is analogous to going from looking at individual trees (genes) to see how the whole forest (gene ontology) is organized.

This tutorial illustrates how to:

Open a zipped project
Perform GO enrichment analysis

Description of the Data Set

This tutorial will provide a step-by-step guide to performing GO enrichment analysis. The data set used is based on 51 subjects run on the Illumina Human Ref-8 BeadChip platform. Twenty-six of the subjects were categorized as "Young" with an age range of 18 to 28. The other 25 subjects were categorized as "Old" with an age range of 65 to 84. Skeletal muscle, a type of striated muscle tissue, was obtained via biopsy from each subject. The total RNA was extracted from the skeletal cells, prepared and run on the BeadChips producing the data that is used for this tutorial.

The paper this data is based on can be found at PLOS.

Data and associated files for this tutorial can be downloaded by going to Help > On-line Tutorials on the main menu toolbar within the Partek Genomics Suite software. Download the zipped file and store it on your local disk drive. There is no need to manually unzip the directory.

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Open a zipped project

The zipped project file contains several prepared files used in this analysis as well as the annotation information for the BeadChip. The zipped file also contains a Partek project file (.ppj).

After downloading the file, go to File > Import > Zipped project... and browse to GO_Enrichment.zip on your local drive

Partek Genomics Suite will automatically unzip the file, read the .ppj file, open and annotate all spreadsheets (Figure 1). The parent spreadsheet (GSE8479-AVGSignal) contains the original intensity data. The first child spreadsheet (ANOVAResults) contains the results of differential gene expression analysis from a 3-way ANOVA. The second child spreadsheet (Gene_List.txt) is a list of significantly differentially expressed genes. When working with your own data, you will need to detect differentially expressed genes and create a gene list yourself.

Figure 1. Viewing the Gene List spreadsheet

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Perform GO enrichment analysis

One of the main functions of GO enrichment is to find the overrepresentation of functional categories in a gene list. With the Gene_List.txt spreadsheet selected:

From the Gene Expression workflow, choose Biological Interpretation followed by Gene Set Analysis
Select the GO Enrichment radio button in the Gene Set Analysis dialog (Figure 1) followed by Next
In the next dialog, make sure the Gene_List.txt spreadsheet is chosen from the drop-down list and click Next

Figure 1. Gene set analysis dialog. Choose GO Enrichment and select Next

You have the choice to use the Fisher's Exact or Chi-Square test. Both tests compare the proportion of a gene list in a functional group to the proportion of genes in the background for that group. Both are acceptable and you can always test both by re-running the analysis. You can also restrict the analysis to functional groups with more than or fewer than a specified number of genes. Restricting the analysis to GO groups with fewer than 150-200 genes will increase the speed of analysis and exclude large groups which may not be too informative. If analysis time is not a concern, you can just use the default settings.

Select the Use Fisher's Exact test radio button (Figure 2)
Make sure the Invoke gene ontology browser on the result check box is selected
Leave all other settings as default and click Next

Figure 2. Configure the parameters of the GO enrichment test

Select the Default mapping file radio button and click Next

Figure 3. For an explanation of the different kinds of mapping file supported, click the help icon next to each one

A new spreadsheet (Figure 4) and the gene ontology browser (Figure 5) will appear.

Figure 4. GO enrichment output spreadsheet. Right-click a row header to perform additional tasks on a chosen GO term

The new spreadsheet (GO-Enrichement.txt) is a child spreadsheet of the gene list. The first column contains the GO functional groups, each of which falls into a broader category (biological process, cellular component or molecular processes), shown in column 2. The GO functional groups are sorted by descending enrichment score, which is shown in the column 3. The enrichment score is the negative natural logarithm of the enrichment p-value, which is shown in column 4. The higher the enrichment score, the more overrepresented a functional group is in the gene list. If a functional group has an enrichment score of over 1, it is overrepresented. As a rule of thumb, an enrichment score of 3 corresponds to significant overrepresentation (p-value=0.05). For your data, you may wish to add a multiple test correction (e.g. FDR) by going to Stat > Multiple Test correction. We will not perform the multiple test correction for this tutorial.

Additional columns help describe the enrichment score for each group, including the percentage of genes in the group that are present in the gene list, the number of genes present in the group that are present in the list, and the total number of genes in the group. Because the original gene list was derived from statistical analysis, extra columns will appear for all p-values in the ANOVA model. For example, the Young/Old score and Gender score columns contain the negative natural logarithm of the geometric mean of p-values for each marker/gene present in the list and in the group. These scores represent the level of differential expression of the genes in the functional group. The larger the score, the more differentially expressed the genes are in the group. A score of 3 or greater corresponds to an average p-value of 0.05 or less. For example, the Y_oung/Old_ score explains how differentially expressed the genes present in the list and in a given group are between the "Young" and "Old" categories.

On the GO-Enrichment.txt spreadsheet, right-click on a row header of a functional group, such as hydrogen ion transmembrane transporter activity, which has an enrichment score of 29.9484, and choose Browse to GO term from the menu

Figure 5. Viewing a functional group on the gene ontology browser

The Gene Ontology Browser opens in a separate tab (Figure 5). A functional group viewed in the browser will show the hierarchical relationship to the other GO terms. The selected functional group will be highlighted on the left. In Figure 5, you can see the hydrogen ion transmembrane transporter activity is found in the tree molecular function > transporter activity > substrate-specific transmembrane transporter activity > cation-transporting ATPase activity > inorganic cation transmembrane transporter activity > monovalent inorganic cation transmembrane activity. On the right, a bar chart shows the sub-groups of the selected group and their respective enrichment scores.

On the GO-Enrichment.txt spreadsheet, right-click on a row header of a functional group and choose Term Details from the menu

A web browser will be opened and you will be re-directed to the AmiGO website, where you will find more details about the chosen GO term (internet connection required).

On the GO-Enrichment.txt spreadsheet, right-click on a row header of a functional group and choose Create Gene List from the menu

Figure 6. New gene list spreadsheet containing all the genes in the original list that belong to the chosen functional group

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RNA-Seq Analysis

RNA-Seq is a high-throughput sequencing technology used to generate information about a sample’s RNA content. Partek Genomics Suite offers convenient visualization and analysis of the high volumes of data generated by RNA-Seq experiments.

This tutorial illustrates:

Importing aligned reads
Adding sample attributes
RNA-Seq mRNA quantification
Detecting differential expression in RNA-Seq data
Creating a gene list with advanced options
Visualizing mapped reads with Chromosome View
Visualizing differential isoform expression
Gene Ontology (GO) Enrichment
Analyzing the unexplained regions spreadsheet

Description of the Data Set

In this tutorial, you will analyze an RNA-Seq experiment using the Partek Genomics Suite software RNA-Seq workflow. The data used in this tutorial was generated from mRNA extracted from four diverse human tissues (skeletal muscle, brain, heart, and liver) from different donors and sequenced on the Illumina® Genome Analyzer™. The single-end mRNA-Seq reads were mapped to the human genome (hg19), allowing up to two mismatches, using Partek Flow alignment and the default alignment options. The output files of Partek Flow are BAM files which can be imported directly into Partek Genomics Suite 7.0 software. BAM or SAM files from other alignment programs like ELAND (CASAVA), Bowtie, BWA, or TopHat are also supported. This same workflow will also work for aligned reads from any sequencing platform in the (aligned) BAM or SAM file formats.

Data and associated files for this tutorial can be downloaded by going to Help > On-line Tutorials from the Partek Genomics Suite main menu or using this link - RNA-Seq Data Analysis tutorial files. Once the zipped data directory has been downloaded to your local drive:

Unzip the downloaded files to C:\Partek Training Data\RNA-seq or to a directory of your choosing. Be sure to create a directory or folder to hold the contents of the zip file

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Importing aligned reads

We will be using the RNA-Seq workflow to analyze RNA-Seq data throughout this tutorial. The commands included in the RNA-Seq workflow are also available form the command toolbar, but may be labeled differently.

Select the RNA-Seq workflow by selecting it from the Workflow drop-down menu in the upper right-hand corner of the Partek Genomics Suite window (Figure 1)

Figure 1. Selecting the RNA-seq workflow

The Partek Genomics Suite software can import next generation sequencing data that has been aligned to a reference genome. Two standard types of alignment formats can be imported: .BAM and .SAM. It is also possible to concert ELAND .txt files to .BAM files with the converter found in the Tools menu in the main command bar. The data used in this tutorial was aligned using the Partek® Flow® software and saved as .BAM files.

To import the .BAM files, select Import and Manage Samples from the Import section of the RNA-Seq workflow. The Sequence Import dialog box will open (Figure 2)

Figure 2. Importing .BAM files

Select BAM Files (*.bam) from the Files of type drop-down menu if not set by default
Use the file browser panel on the left-hand side of the Sequence Dialog or select Browse... to navigate to the folder where you stored the tutorial .BAM files
Files with checked boxes next to the file name will be imported. For this tutorial, select brain_fa, heart_fa, liver_fa, and muscle.fa
Select OK to confirm the file selection and open the next dialog (Figure 3)

Figure 3. Viewing the Sequence Import wizard; specify Output file (and directory using Browse), Species, and Genome

Configure the dialog as shown (Figure 3)

Output file provides a name for the top-level spreadsheet. Browse can be used to change the output directory.

Select Homo sapiens from the Species drop-down menu

This will allow us to select a human genome reference assembly alignment.

Select hg19 for Genome/Transcriptome reference used to align the reads

This is the reference genome our tutorial data was aligned to using Partek Flow.

Select OK to open the BAM Sample Manager dialog (Figure 4)

Figure 4. Bam Sample Manager dialog

The Bam Sample Manager dialog allows additional samples to be added or removed after the initial sample import. To remove a sample, select a sample from the list and then select Remove selected samples. This dialog also allows us to modify samples.

Select Manage samples to open the Assign files to samples dialog

Sample ID is by default set to the file name, which may be too long or uninformative, so the Assign files to samples dialog can be used to give informative names to samples.

Change the samples names to Brain, Heart, Liver, and Muscle as shown (Figure 5)

The Assign files to samples dialog also allows multiple .BAM files to be merged into one sample. This is useful if reads from one sample are split into multiple .BAM files.

Figure 5. Changing sample names using the Assign files to samples dialog

Select OK to close the Assign files to samples dialog
Select Close to exit the Bam Sample Manager dialog and view the imported data (Figure 6)

Figure 6. Viewing the imported data in a spreadsheet

Additional files can be added to this spreadsheet using the Bam Sample Manager dialog. The Bam Sample Manager dialog can also be used to add imported samples to a separate spreadsheet by selecting a new option in the dialog, Add new experiment.

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Adding sample attributes

Now that the data has been imported, we need to make a few changes to the data annotation before analysis.

Modifying sample attributes

Notice that the Sample ID names in column 1 are gray (Figure 1). This indicates that Sample ID is a text factor. Text factors cannot be used as a variable in downstream analysis so we need to change Sample ID to a categorical factor.

Figure 1. Viewing the imported data in a spreadsheet

Right-click on the column header to invoke the pop-up menu
Select Properties (Figure 2)

Figure 2. Changing column properties

Configure the Properties of Column 1 in Spreadsheet 1 dialog as shown (Figure 3) with Type set to categorical and Attribute set to factor

Figure 3. Changing column 1 properties

Select OK

The samples names in column 1 are now black, indicating that they have been changed to a categorical variable. Next, we will add attributes for grouping the data.

Adding sample attributes

From the RNA-seq workflow panel, select Add Sample Attributes to bring up the Add Sample Attributes dialog (Figure 4)

Figure 4. Add Sample Attributes dialog

Select Add a Categorical Attribute
Select OK to bring up the Create categorical attribute dialog

Creating a categorical sample attribute allows us to group samples. This is useful for designating samples as replicates, as members of an experimental group, or as sharing a phenotype of interest. In this tutorial, we have four different samples from different tissues and different donors, but to illustrate the available statistical analysis options, we need to divide the samples into two groups: muscle (Heart and Muscle) and not muscle (Brain and Liver).

Set Attribute name: as Tissue
Rename Group 1 to muscle and Group 2 to not muscle
Select and drag the samples from the Unassigned panel to the correct group panel (Figure 5)

Figure 5. Creating a categorical attribute

Select OK
Select No from the Add another attribute? dialog
Select Yes from the Save spreadsheet 1 dialog

The attribute will now appear as a new column in the RNA-seq spreadsheet with the heading Tissue and the groups muscle and not muscle.

Choosing Sample ID column

The next available step in the Import panel of the RNA-seq workflow is Choose Sample ID Column_._ Verifying the correct column is designated the Sample ID becomes particularly important when data from multiple experiments is being combined.

Select Choose Sample ID Column from the Import panel of the RNA-Seq workflow
Select OK (Figure 6)

Figure 6. Choosing the correct column as Sample ID

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RNA-Seq mRNA quantification

We are now ready to measure gene expression in our dataset. To do this, we will use the mRNA quantification task in the Analyze Known Genes section of the RNA-Seq workflow. mRNA quantification creates spreadsheets showing expression at exon, transcript, and gene levels and reports raw and normalized reads for each sample.

Please note that the normalization method used by Partek Genomics Suite is Reads Per Kilobase per Million mapped reads (RPKM) (Mortazavi et al. 2008). In brief, this normalization method counts total reads in a sample, divides by one million to create a per million scaling factor for each sample; then divides the read counts for the feature (exon, transcript, or gene) by the per million scaling factor to normalize for sequencing depth and give a reads per million value; and finally divides reads per million values by the length of the feature (exon, transcript, or gene) in kilobases to normalize for feature size.

Select 1 (RNA-Seq) from the spreadsheet tree
Select mRNA quantification in the Analyze Known Genes section of the RNA-seq workflow

The RNA-Seq Quantification dialog will appear (Figure 1).

Select RefSeq Transcripts 2017-05-02 from the mRNA section of the Specify a database of genomic features to quantify panel of the dialog

Your choices in the Configure the test panel of the dialog depend on the design and aims of your experiment. A detailed description of each option can be viewed by selecting the () icon next to it.

For Strand-specificity: select No

Your choice here depends on the method used for sample preparation. A directional mRNA-seq sample preparation protocol only synthesizes the first strand of cDNA whereas other methods reverse transcribe the mRNA into double-stranded cDNA. If double-stranded cDNA has been synthesized, the sequencer reads sequences from both the forward and reverse strands but does not discriminate between them, eliminating strand information. When strand information is preserved, it is possible for paired-end sequences to come from a combination of the forward and reverse strands. If in doubt, select Auto-detect from the drop-down list. The data for this tutorial did not preserve strand information so we selected No.

For In the gene-level result report intronic reads as compatible with the gene?, select No

Selecting Yes would include intronic reads in the gene-level results, which might be useful for discovering unannotated transcripts for known genes, and also includes introns in the RPKM calculation for the gene-level results.

For Require strict paired-end compatibility select No

Selecting Yes would require that two alignments form the same read must map to the same transcript to be considered compatible. However, the data set used in this tutorial consists of single-end reads so this option is unnecessary.

For report results with no reads from any sample? select No

Selecting Yes would include all the genes/transcripts/exons in the transcriptome, even if there are no reads for that feature from any sample.

Make sure Report unexplained regions with more than ___ reads is selected and specify 5 as the number of reads

This option will create a spreadsheet that includes all regions with a specified number of reads that map to the genome, but not to any feature included in the selected database of genomic features.

Select Report exon-level results

If selected, spreadsheets will be created describing expression at the exon level.

Your RNA-Seq Quantification dialog should now be configured as shown (Figure 1). Descriptions of the spreadsheets that can be created by mRNA Quantification can be viewed by selecting Describe results to bring up the Quantification Result Help dialog.

Figure 1. Configuring the RNA-Seq Quantification dialog

Select OK to perform the RNA-Seq quantification

Reads will now be assigned to individual transcripts of a gene based on the Expectation/Maximization (E/M) algorithm (Xing, et al. 2006). In Partek Genomics Suite software, the E/M algorithm is modified to accept paired-end reads, junction aligned reads, and multiple aligned reads if these are present in your data. For a detailed description of the E/M algorithm, refer to the RNA-Seq white paper (Help > On-line Tutorials > White Papers). Several spreadsheets containing the analyzed results will be generated. Progress bars in the lower left-hand corner RNA-Seq Quantification window and the main window will update as the data is analyzed.

If you have not disabled it, the the Quantification Result Help dialog will appear. Select Close

The Analysis tab now shows the spreadsheets created by mRNA Quantification in the spreadsheet tree as a child spreadsheet of 1 (RNA-seq) (Figure 2).

Figure 2. Viewing the results of mRNA Quantification

The __reads and _rpkm_** spreadsheets**

Data on features - genes, transcripts, and exons - are presented before and after normalization as _reads and _rpkm spreadsheets. In this tutorial, we have created exon_reads, exon_rpkm, gene_reads, gene_rpkm, transcript_reads, and transcript_rpkm spreadsheets.In these spreadsheets, samples are listed one per row and the normalized counts of the reads mapped to features are in columns (Figure 2).

The _transcripts_** spreadsheet**

The transcripts spreadsheet lists a transcript in each row.

It is possible to derive basic information from the RNA-Seq_result.transcripts spreadsheet about differential and alternative splicing between your samples even if you don’t have replicates using a simple chi-squared or log-likelihood tests because each sample is represented only once and we can assume a null hypothesis that the transcripts are evenly distributed across all samples. However, the power of Partek Genomics Suite software resides in the implementation of a mixed-model ANOVA that can handle unbalanced and incomplete datasets, nested designs, numerical and categorical variables, any number of factors, and flexible linear contrasts when you do have biological replicates.

The _unexplained_regions_ spreadsheet

The contents of this spreadsheet are explained in more detail in a later section of the tutorial - Analyzing the unexplained regions spreadsheet.

References

Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature, 2008; 5: 621-8.

Xing Y, Yu T, Wu YN, Roy M, Kim J, Lee C: An expectation-maximization algorithm for probalisitic reconstructions of full-length isoforms from splice graphs. Nucleic Acids Res 2006, 34: 3150-3160.

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Detecting differential expression in RNA-Seq data

During import, you created a categorical attribute called Tissue and assigned the 4 samples to either the muscle or not muscle groups. This step was to create replicates within a group, albeit this grouping is somewhat artificial and is only used in this tutorial because we want to illustrate ANOVA with a small data set. Replicates are a prerequisite for differential expression analysis using ANOVA.

Select Differential Expression Analysis from the Analyze Known Genes section of the RNA-Seq workflow

The Differential Expression Analysis dialog offers the choice of analyzing at Gene-,Transcript-, or Exon-level.

Select Gene-level
Specify the 1/gene_rpkm (RNA-Seq_results.gene.rpkm) spreadsheet from the Spreadsheet drop-down menu (Figure 1)

Figure 1. Choosing the type of differential expression analysis

Select OK to open the ANOVA dialog

Available factors are listed in the Experimental Factor(s) panel on the left-hand side of the dialog.

Select Tissue, then select Add Factor > to move Tissue to the ANOVA Factor(s) panel on the right-hand side of the dialog (Figure 2)

Figure 2. The ANOVA dialog

If the ANOVA were now performed (without contrasts), a p-value for differential expression would be calculated, but it would only indicate if there are differences within the factor Tissue; it would not inform you which groups are different or give any information on the magnitude of the difference between groups (fold-change or ratio). To get this more specific information, you need to define linear contrasts.

Select Contrasts... to open the Configure dialog
For Select Factor/Interaction, Tissue will be the only factor available as it was the only factor included in the ANOVA model in the previous step; if multiple factors were included, they could be selected in the Select Factor/Interaction: drop-down menu. The levels in this factor are listed on the Candidate Level(s) panel on the left side of the dialog
For this data set, verify that No is selected for Data is already log transformed?
Left click to select muscle from the Candidate Level(s) panel and move it to the Group 1 panel (renamed muscle) by selecting Add Contrast Level > in the top half of the dialog. Label 1 will be changed to the subgroup name automatically, but you can also manually specify the label name
Select not muscle from the Candidate Level(s) panel and move it to the Group 2 panel (renamed not muscle)
The Add Contrast button can now be selected (Figure 3)
Select OK to return to the ANOVA dialog

Figure 3. Defining linear contrasts

Select OK to perform the ANOVA as configured (Figure 4)

Figure 4. Fully configured ANOVA

Once the ANOVA has been performed on each gene in the data set, an ANOVA child spreadsheet ANOVA-1way (ANOVAResults) will appear under the gene_rpkm spreadsheet (Figure 5). The format of the ANOVA spreadsheet is similar for all workflows. Mouse over each column title for a description of the column contents.

Figure 5. Viewing ANOVA results

In this tutorial, the overall p-value for the factor (column 4) is the same as the p-value for the linear contrast (column 5) as there are only two levels within Tissue. If we had more than two groups, the overall p-value and the linear contrast p-values would most likely differ. You can also see the ? symbol in the ratio/fold-change columns (6 and 7) for several genes that also have a low p-value because there are zero reads in one of the groups, thus making it impossible to calculate ratios and fold-changes between groups.

For using ANOVA with more complicated experimental designs, including multiple factors and linear contrasts, please refer to Identifying differentially expressed genes using ANOVA in the Gene Expression Analysis tutorial.

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Creating a gene list with advanced options

The basic method of creating a gene list from ANOVA results based on fold-change and p-value cut-offs is detailed in Creating gene lists from ANOVA results. Advanced options enable the creation of lists based on more complex criteria. For example, we can use the Create Gene List function to identify transcripts that are both significantly differentially expressed AND alternatively-spliced among the four tissue samples.

Select Create Gene List from the Analyze Known Genes panel of the RNA-Seq workflow to invoke the List Manager dialog
Select the Advanced tab (Figure 1)

Figure 1. Creating a gene list using advanced options

Select Specify New Criteria to invoke the Configure Criteria dialog (Figure 2)

Figure 2. Configuring criteria for transcripts with a p-value < 0.05

In the Configure Criteria dialog box (Figure 2), provide a name for the list (Diff Exp)
Select 1/transcripts (RNA-Seq_results.transcripts) from the_Spreadsheet_ drop-down menu
Select 8. p-value(DiffExp) from the Column drop-down menu
Set Include p-values to significant with FDR with a value of 0.05

A list of 38,285 transcripts that pass this criteria will be generated according to the # pass score on the right-hand side of the dialog. If the settings are changed, this number will automatically update.

Select OK
Repeat the same steps to create a list of transcripts that are likely alternatively spliced, named Alt Splice, using the same p-value cutoff and Column set to 10. p-value (AltSplice) (Figure 3)

Figure 3. Configuring criteria for a list of alternatively spliced genes

Select OK to generate Alt Splice
Select both lists in the right-hand panel under the Criteria panel while holding the Ctrl key on your keyboard
Select Intersection from the left-hand panel of the List Manager dialog (Figure 4)

Figure 4. Creating a gene list at the intersection of two criteria

Enter a name for the criteria (Diff Exp and Alt Splice)
Select OK to close the naming dialog and OK again to close the list creation hint dialog
Select Save List from the Manage criteria section of the List Manager dialog (Figure 5)

Figure 5. Saving a created list criteria

Select Diff Exp and Alt Splice in the List Creator dialog (Figure 6)

Figure 6. Selecting list to save in List Creator dialog

Select OK to save the list
Select Close to exit the List Manager dialog and view the Diff_Exp_and_Alt_Splice spreadsheet (Figure 7)

Figure 7. A list of the differentially expressed and alternatively spliced genes is now available for downstream analysis

This list of differentially expressed and alternatively spliced transcripts will be used later in the tutorial.

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Visualizing mapped reads with Chromosome View

Once imported, it is possible to visualize the mapped reads along with gene annotation information and cytobands.

Select the parent spreadsheet 1 (RNA-seq)
Select Chromosome View in the Visualization section of the RNA-Seq workflow panel

Unless you have previously downloaded an annotation file, you will be prompted to select an annotation source.

Select RefSeq Transcripts - 2017-05-02

Partek Genomics Suite will download the relevant file and save it to your default library location. The Chromosome View tab will open with chromosome 1 displayed (Figure 1)

Figure 1. Visualizing reads on a chromosome level in Chromosome View

In Chromosome View you can choose other chromosomes from the position field drop-down menu (Figure 2) to change which chromosome is displayed. You may also type a search term (e.g. gene symbol or transcript ID) directly into the position field.

Figure 2. Choosing a chromosome to view in Chromosome View

The Tracks panel contains the following tracks:

RefSeq Transcripts (+)

The RefSeq Transcripts (+) track shows all genes encoded on the forward strand of the currently selected chromosome. This experiment uses RefSeq Transcripts, which defines genomic sequences of well-characterized genes, as the reference annotation track. Mouse-over a particular region in this track, and all genes within this region are shown in the information bar. Zoom in on this track to see individual genes, including alternative isoforms.

RefSeq Transcripts (-)

The RefSeq Transcripts (-) track shows all the genes encoded on the reverse strand the currently selected chromosome_._

Legend Base Colors

The Legend Base Colors track shows the color for each nucleotide. Colored nucleotide bases become visible in the Bam Profile tracks at higher levels of magnification. By default, the colors are set to red for adenine (A), blue for cytosine (C), yellow for guanine (G), green for thymine (T), and black for base not called (N). The color of the bases can be configured by selecting the Legend Base Colors track and selecting Configure colors in the track configuration panel beneath the Tracks panel on the left-hand side of the Genome Viewer.

Bam Pofile (Heart, Brain, Muscle, Liver)

The Bam Profile tracks show all the reads that mapped to the currently selected chromosome from the four tissue samples. The y-axis numbers on the left side of the tracks indicate the raw read counts. The aligned reads are shown in the Genome Viewer in each track with a different color for each Bam Profile track.

Genome Sequence, Cytoband, and Genomic Label

The Genome Sequence, Cytoband and Genomic Label tracks are shown at the bottom of the panel. These labels are helpful for navigating the chromosome.

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Visualizing differential isoform expression

Chromosome View in the Partek Genomics Suite software enables visualization of differential expression and alternative splicing results in RNA-Seq data.

Select New Track
Select Add a track from spreadsheet and select 1/transcripts (RNA-Seq_results.transcripts) from the drop-down menu
Select Next > (Figure 1)

Figure 1. Adding a new track to Chromosome View

The new track will be added to Chromosome View (Figure 2).

Figure 2. Viewing isoform proportion track in Chromosome View

At this point, you may find it useful to alter track properties. Each track can be individually configured. For example, isoform information will be easier to visualize if we remove a few tracks.

Select Cytoband (hg19) in the Tracks panel
Select Remove Track to remove it form the viewer
Repeat for Genomic Label, RefSeq Transcripts - 2017-05-02 (hg19) (-), Legend: Base Colors, and Genome Sequence

Next, we are going to view a single gene, SLC25A3, with differentially expressed isoforms.

Type SLC25A3 in the Plot Position bar at the top of the window and hit Enter. The browser will browse to the gene

To further improve our visualization of SLC25A3 isoforms, we can modify the remaining tracks.

Select RefSeq Transcripts - 2014-01-03 (hg19) (+) from the Tracks panel
Change Track height to 60 using the slider
Select Apply to change track height
Repeat steps to set each Bam Profile track to a height of 40 to complete our changes
Move the Isoform proportion track to below the RefSeq Transcripts track by selecting and dragging it up the list (Figure 3)

Figure 3. Changing tracks in Chromosome View to facilitate visual analysis of isoform porportions

The Muscle, Br_ain_, Heart, Liver, and genomic label tracks were described in a previous section. Here, the focus is on the Isoform proportion track, which visualizes differential expression and alternative splicing. The reads that are mapped to a certain sample and the proportion of the transcript expressed in that sample are colored to match the Bam Profile track of that sample. In this screenshot, Brain is yellow, Heart is green, Liver is red, and Muscle is orange

SLC25A3 was reported by Wang, et al., (Nature, 2008) to have “mutually exclusive exons (MXEs)”. The reads mapped to the 3 transcripts of this gene in each of the tissue samples are shown in the Genome Viewer in the isoform proportion track. The relative abundances of the individual transcripts of this gene are shown by the height of the color coded bars on each transcript in the isoform proportion track. Note transcript NM_213611 has low expression while transcripts NM_005888 and NM_002635 have higher expression. Also note that NM_005888 is expressed primarily in the heart and muscle, indicated by the primarily green and orange bars, while NM_002635 is expressed primarily in the brain and liver, indicated by the primarily yellow and red bars.

For additional tips on using the Chromosome View, refer to Visualizing mapped reads with Chromosome View.

References

Wang, E.T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S.F., Schroth, G.P., & Burge, C.B. Alternative isoform regulation in human tissue transcriptomes. Nature, 2008; 456: 470-6.

Additional Assistance

If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.

Gene Ontology (GO) Enrichment

With the GO Enrichment feature in Partek Genomics Suite, you can take a list of significantly expressed genes/transcripts and find GO terms that are significantly enriched within the list. For a detailed introduction to GO Enrichment, refer to the GO Enrichment User Guide (Help > On-line Tutorials > User Guides).

Select the Diff_Exp_and_Alt_Splice spreadsheet from the spreadsheet tree
Select Gene Set Analysis in the Biological Interpretation section of the RNA-Seq workflow (Figure 1)

Figure 1. Selecting Gene Set Analysis

Select GO Enrichment in the Gene Set Analysis dialog (Figure 2)
Select Next >

Figure 2. Selecting the method of analysis

Select the spreadsheet 1/Diff_Exp_and_Alt_Splice (Diff Exp and Alt Splice.txt) from the drop-down menu (Figure 3)
Select Next >\

Figure 3. Selecting the spreadsheet that contains the genes you want to test

Select Use Fisher's Exact test
Select Invoke gene ontology browser on the result
Set Restrict analysis to functional groups with more than _ genes to 2 (Figure 4)
Select Next >

Figure 4. GO Enrichment options

Select Default mapping file (Figure 5)
Select Next >

Figure 5. Selecting the mapping file

A GO-Enrichment spreadsheet, as well as a browser (Figure 6), will be generated with the enrichment score shown for each GO term. Browse through the results to find a functional group of interest by examining the enrichment scores. The higher the enrichment score, the more over represented this functional group is in the input gene list. Alternatively, you may use the Interactive filter on the GO-Enrichment spreadsheet to identify functional groups that have low p-values and perhaps a higher percentage of genes in the group that are present.

Figure 6. Viewing the Gene Ontology Browser

Additional Assistance

If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.

Analyzing the unexplained regions spreadsheet

During a previous section of this tutorial, a spreadsheet named unexplained_regions was generated. This spreadsheet contains locations where reads map to the genome but are not annotated by the transcript database, in this case, RefSeqGene. The unexplained_regions spreadsheet is potentially very interesting as it may contain novel findings.

Right click column 6. Average Coverage and select Sort Descending from the menu
Select Find Overlapping Genes from the Tools option in the command toolbar (Figure 1)

Figure 1. Selecting Find Overlapping Genes from Tools in the command toolbar

Select Add a new column with the gene nearest to the region in the Find Overlapping Genes dialog (Figure 2)
Select OK

Figure 2. Find Overlapping Genes

Select RefSeq****Transcripts – 2017-05-02 from the Output Overlapping Features dialog (Figure 3)

Please note that it is recommended that you annotate with the same database used when you performed mRNA quantification.

Select OK

Figure 3. Select the database to search for overlapping features

The closest overlapping feature and the distance to it is now included as columns 7. Overlapping Features and _8. Nearest Features i_n the unexplained_regions spreadsheet.

Right-clicking on a row header and selecting Browse to Location will show the reads mapped to the chromosome. For this tutorial, a couple of genes are selected to show regions that are located after a known gene or in the intron of a gene.

Right-click row 39 and select Browse to location from the pop-up menu
Select the Chromosome View tab to view a region within an intron of UNC45B. This may be a novel exon (Figure 4)

Figure 4. A region within an intron of UNC45B that might be an novel exon

Right-click row 12576 and select Browse to location to go to a region that starts 1 bp after CD82.

This peak may represent an extended exon (Figure 5).

Figure 5. A region that starts 1 bp after CD82 that might represent an extended exon

While RefSeq was used to identify overlapping features, the choice of which database to use will depend on the biological context of your experiment. For example, you may wish to utilize promoter or miRNA databases if you are interested in regulation of expression.

Additional Assistance

If you need additional assistance, please visit our support page to submit a help ticket or find phone numbers for regional support.

ChIP-Seq Analysis

Chromatin Immunoprecipitation Sequencing (ChIP-Seq) uses high-throughput DNA sequencing to map protein-DNA interactions across the entire genome. Partek Genomics Suite offers convenient visualization and analysis of ChIP-Seq data.

In this tutorial, we will use the Partek Genomics Suite ChIP-Seq workflow to analyze aligned data from a ChIP sample versus a control sample in .bam format.

This tutorial illustrates:

Description of the Data Set

The data for this tutorial comes from Johnson et al. 2007, which first described the ChIP-Seq technique.

This study mapped genomic binding sites for neuron-restrictive silencer factor (NRSF) transcription factor across the genome. There are two samples: an NRSF-enriched ChIP sample (chip.bam) and a control sample of input DNA without antibody immunoenrichment (mock.bam). The chip.bam file contains ~1.7 million mapped reads and the mock.bam file contains ~2.3 million mapped reads. These .bam files contain the aligned genomic locations and sequences of mapped reads. This data set contains reads from a single-end (SE) library; the differences in processing paired-end (PE) reads will be discussed when applicable.

Data for this tutorial can be downloaded from the Partek website using this link - . To follow this tutorial, download the 2 .bam files and unzip them on your local computer using 7-zip, WinRAR, or a similar program. Because of the large size of the .bam files, we recommend saving them to a local drive instead of trying to access them on a network drive. The first time a .bam file is read by Partek Genomics Suite, the file will be sorted to allow for faster access. Therefore, you must have write permissions for the .bam files after download and on the file folder where they are stored.

References

Johnson, D. S., Mortazavi, A., Myers, R. M., & Wold, B. (2007). Genome-Wide Mapping of in Vivo Protein-DNA Interactions (Vol. 316). New York, NY: Science.

Additional Assistance

Importing ChIP-Seq data

Data for this tutorial can be downloaded from the Partek website using this link - . To follow this tutorial, download the two .bam files and unzip them on your local computer using 7-zip, WinRAR, or a similar program.

Store the two.bam files at C:\Partek Training Data\ChIP-Seq or to a directory of your choosing. We recommend creating a dedicated folder for the tutorial on a local drive.
Select ChIP-Seq from the Workflows drop-down menu (Figure 1)

Figure 1. Selecting the ChIP-Seq workflow

Select Import and Manage Samples from the Import section of the ChIP-Seq workflow
Select Browse... or use the file tree to navigate to the folder where you stored the .bam files

All .bam files in the folder will be selected by default (Figure 2).

Figure 2. Importing .bam files using the Sequence Import dialog

Verify that chip.bam and mock.bam are selected
Select OK

The Sequence Import dialog will launch (Figure 3). This allows us to choose the output file name and destination for the parent spreadsheet, as well as the species, and genome build of the imported samples. By default, the output file destination is the folder the .bam files are located.

Figure 3. Setting the output file name, species, and genome build during .bam file import

Set Output file to ChIP-Seq
Set Species to Homo sapiens using the drop-down menu
Set Genome build to hg18 using the drop-down menu
Select OK

The Bam Samples Manager dialog will open (Figure 4). This dialog can be used to add samples to the project (Add samples), remove samples (Remove samples), to associate multiple files with particular samples (Manage samples), and to map the chromosome names from the input files to the association files (Manage sequence names).

Figure 4. The Bam Sample Manager can be used to add, remove, and manage files and samples

Select Close

The Sort bam files dialog will open. Sorting is necessary for all imported .bam files, but you can choose to hide this hint in the future by selecting Please don't show me this hint again.

Select OK

The imported spreadsheet will open while the .bam files are sorted. Progress in sorting will be displayed on the progress bar in the lower left-hand corner of the Partek Genomics Suite window. Once sorting has completed, there will be samples on rows with the sample names in column 1. Sample ID and the number of reads mapped to the reference genome for each sample in column 2. Number of allignments (Figure 5).

Figure 5. Imported .bam files with one sample in each row

Additional Assistance

Quality control for ChIP-Seq samples

We can check the quality of the samples using Partek Genomics Suite before analyzing the data.

Strand cross-correlation

In ChIP-Seq, genomic DNA is fragmented and target-protein-bound DNA fragments are purified by immunoprecipitation. These purified fragments are between 100 and 500 base pairs depending on the protocol; however, because ChIP-Seq uses short-read sequencing (25 to 35 base pair reads) to maximize sequencing depth, only the ends of each fragment will be sequenced. Consequently, with single-end sequencing, the forward and reverse strands for the each fragment will be from opposite ends of the fragment. At a protein-binding site, there will be two peaks of read enrichment, one from enrichment of forward strand reads and another from enrichment of reverse strand reads. The average distance between these peaks is termed the effective fragment length. Because the forward and reverse strand peaks are generated from a common set of fragments, the peaks should be roughly symmetrical. By phase shifting the data to the mid-point between the two peaks, a common read density plot can be created that shows single peaks at binding sites.

Strand Cross-Correlation allows us to use the symmetrical distribution of forward and reverse strand fragments calculate the effective fragment length (Kharchenko et al., 2008). The Pearson correlation coefficient between the read densities of the forward and reverse strands is calculated after phase shifts of between 0 and 500 base pairs. This is visualized with the phase shift range on the x-axis and the corresponding Pearson correlation coefficients between forward and reverse strand read densities on the y-axis (Figure 1). High-quality ChIP-Seq data will give a strong peak on the Strand Cross-Correlation plot at the effective fragment length. When calling peaks, the forward and reverse (or paired end) reads are each phase-shifted by the effective fragment length to create a combined read density profile.

For paired-end sequencing, Strand Cross-Correlation is calculated from the distribution of distances between the paired reads from the ends of each fragment.

We will perform Strand Cross-Correlation to identify the effective fragment length we can use when calling read enrichment peaks.

Select Strand Cross-Correlation from the QA/QC section of the ChIP-Seq workflow

If you have not run this step before, you will be asked if you would like to create a new QA/QC child spreadsheet.

If prompted, select Yes to create a new child spreadsheet for QA/QC

After running Strand Cross-Correlation, the Strand Separation of Samples viewer will open as a new tab (Figure 1).

Figure 1. Strand Cross-Correlation profile plot showing possible effective fragment lengths on the x-axis and resulting Pearson correlation coefficients on the y-axis.

For the chip sample (blue), we can see the peak at 111 base pairs, corresponding to an effective fragment length of 111 base pairs. This number can be determined by examining the values in the strand_correlation spreadsheet (Figure 2), by moving the cursor over the peak in the graph, or by sorting the data in the spreadsheet. The Strand Separation of Samples graph is also useful as a quality control measure. In lower quality ChIP-Seq data, we would also observe a peak at the read length. The ratio between the Pearson correlation coefficient of the effective fragment length peak and the read length peak, normalized with the minimum correlation coefficient, [cc(fragment length) - min(cc)] / [cc(read length) - min(cc)] should be greater than 0.8 to meet the minimum quality standards recommended by the ENCODE project (Landt et al., 2012).

The mock sample (red) does not have an effective fragment length peak because it does not read density peaks to phase shift. It does have a small peak at the sequencing read length of 26 base pairs.

Figure 2. The strand correlation spreadsheet shows the Pearson correlation coefficients for each relative strand shift value (effective fragment length)

Checking the distribution of reads

BAM files can contain both aligned and unaligned reads. The spreadsheet created during import shows the number of reads that were aligned to the reference genome. A large number of unaligned reads may be the result of poor quality sequencing data or alignment problems. It may also be useful to know how many reads map to more than one location in the genome if the options used during alignment supported multiple-mapped reads.

Select Alignments per read form the QA/QC section of the ChIP-Seq workflow

A new spreadsheet named Alignment_Counts will be generated (Figure 3).

Figure 3. Unaligned reads have been removed from these BAM files and the alignment options did not permit mapping to more than one location

The titles of columns 2. 0 Single End Alignments Per Read and 3. 1 Single End Alignment Per Read indicate that this is single end data. Column 2 shows the number of unaligned reads, while column 3 shows the number of reads that aligned exactly once. If the BAM files used in this tutorial included reads that mapped to more than one location in the genome, there would be additional columns.

Additional Assistance

Detecting peaks and enriched regions in ChIP-Seq data

Binding sites for the DNA-binding protein of interest are indicated by peaks of enriched sequencing read density. How are peaks calculated from reads in Partek Genomics Suite?

Using the effective fragment length calculated by Cross Strand-Correlation, each read is extended in the 3' direction by the effective fragment length and overlapping extended reads are merged into single peaks. For paired-end reads, the distance between paired reads is used as the fragment length and overlapping fragments are merged into peaks. For peak detection, the genome is divided into windows of a user-defined size and the number of fragments whose mid-points fall within each window is counted. A model for expected read density (a zero-truncated negative binomial) is used to determine which peaks are significantly enriched over a user-defined false discovery rate (FDR). See the for more information on the peak-finding algorithm and tips for setting the Fragment extension and window sizes.

Select spreadsheet 1 (ChIP-Seq) from the spreadsheet tree
Select Detect peaks from the Peak Analysis section of the ChIP-Seq workflow

The Peak Detection dialog will open. Configure the dialog as shown (Figure 1).

Figure 1. Configuring the peak detection dialog. The appropriate settings for will depend on your experimental design and data.

Select Maximum average fragment size for Fragment Extensions
Set Maximum average fragment size to 111

Your choice for Maximum average fragment size is based on your experimental design. If you have used an antibody that binds DNA as the control antibody such as an IgG control, you could use different fragment lengths for each sample based on its effective fragment length by selecting the Individual maximum fragment sizes option. Here, we have chosen the effective fragment length of 111 base pairs calculated using Cross Strand-Correlation.

Select Reference sample from Reference sample
Select mock from the Reference sample drop-down menu
Set Set the window size to (base pairs) to 111

The peak detection algorithm divides the genome into windows to find windows with enriched for reads based on an FDR cut-off value. Here, we have chosen to match the window and individual maximum fragment sizes.

Select Overlapping for How should windows be merged?
Set The fraction of false positive peaks allowed to 0.001

The Peak Cut-off FDR determines the cut-off for calling peaks. Setting a lower value demands greater differences between mock and chip samples for a peak to be called; a false discovery rate of 0.001 anticipates 1 false positive per 1000 peaks called.

Select Entire region, spanning all merged windows for Which regions should be reported?

Optimal peak detection settings are dependent on your experimental design and data so fine tuning may be required. Because transcription factor binding sites tend to have localized and sharp clusters of reads, the window size used during the analysis of a transcription factor study can be left relatively small, approximately the same as the average fragment length, and the option to allow for gaps between enriched windows does not need to used. Region in the window with most reads could also be selected to report a more narrow region for each peak call. Conversely, histone modification peaks tend to be subtle and diffuse. To analyze histone modification ChIP-seq data, larger window sizes, combining neighboring windows into larger windows using Within a gap distance of, and reporting entire regions using Entire region, spanning all merged windows might be appropriate.

A convenient way to visualize the relationship between window size and gap size is to select the More info link at the top of the Peak Detection dialog box. A simulated read count histogram will open below the Description of Peak Detection section (Figure 2). The blue bars underneath the histogram will reflect how regions are detected and reported using your current Peak Detection settings. Try changing the How should windows be merged or Which regions should be reported? options to visualize their effects on peak detection.

Figure 2. The visual guide helps show the impact of window size and result reporting settings on peak calling.

Select OK to run the peak detection algorithm with your chosen settings

Peak Detection generates a new child spreadsheet, regions (peaks) (Figure 3).

Figure 3. Peaks spreadsheet lists regions with significant peak enrichment with one row per region.

This spreadsheet is sorted by chromosome number and genomic location. Each row represents one genomic region of peak enrichment. The columns are:

Column 1. Chromosome gives the chromosome location of region

Column 2. Start gives the start of region (inclusive)

Column 3. Stop gives the end of region (exclusive)

Column 4. Sample ID gives the sample containing the enriched region

Column 5. Interval length gives the length of the region, Start - Stop, in base pairs

Column 6. Maximum Extended Reads in Window gives the greatest number of extended reads in any of the windows of a region

Column 7. Reads per Million (RPM) divides the total number of aligned reads in the sample (in millions). This helps you compare peaks across samples, especially when there is a large difference in the number of aligned reads between samples.

Column 8. Mann-Whitney p-value identifies the separation between forward and reverse peaks for single-end reads using the Mann-Whitney U-test. Lower p-values indicate better separation. This p-value can be used if there was no control sample or to eliminate regions called due to PCR bias.

Columns 9-10. Total reads in region gives the total number of non-extended reads for each sample in the given genomic region. One column for each sample.

Column 11. p-value(Sample ID. vs. mock) compares the sample specified in column 4 to the reference sample for this genomic region using a one-tailed binomial test. A low p-value means there are significantly more reads in the sample specified in column 4 than in the mock sample. This column is only included if a reference sample is specified.

Column 12. scaled fold change (Sample ID vs. mock) compares intensity of signal between the sample specified in column 4 to the reference sample in the given genomic region. The fold-change is scaled by a ratio of the number of reads for each sample (IP vs. control) on a per-chromosome basis. Scaled fold changes >1 indicate more enrichment in the IP-sample than in the control sample. This column is only included if a reference sample is specified.

Columns 13 -14. overlap percent gives the fraction of the given genomic region that overlaps a called peak region from the indicated sample. For example, the values of 100% in column 13 and 0% in column 14 indicate regions detected in the chip sample, but not in the mock sample. Similarly, regions with the value of 100% in column 14 were detected in the mock sample.

Additional Assistance

Creating a list of enriched regions

In this section, we will create a list of peaks significantly enriched in the ChIP sample versus the control sample.

Select Create a list of enriched regions from the Peak Analysis section of the ChIP-Seq workflow
Select Specify New Criteria (Figure 1)

Figure 1. List creator for ChIP-Seq data allows you to create lists using preset or custom criteria

Configure the new criteria as shown (Figure 2).

Name the criteria p-value filtered
Select 1/regions (peaks) from the Spreadsheet drop-down menu
Select 11. p-value(Sample ID vs. mock) from the Column drop-down menu
Select significant with FDR of from the include p-values drop-down menu with a value of 0.05

Figure 2. Creating a criteria that includes regions significantly enriched in ChIP vs. mock

Select OK to add the criteria to the criteria list (Figure 3)

Figure 3. New criteria are added to the criteria list

Select Save
Select p-value filtered from the list of criteria (Figure 4)

Figure 4. Choosing criteria to save as lists

Select OK

The new spreadsheet will open (Figure 5).

Figure 5. Spreadsheet with regions that are significantly enriched in the ChIP sample vs. control

Other List Creator operations like the Venn Diagram, Union (Or), and Intersection (And) of the lists could be used to create different lists of enriched peaks. For example, you could filter on the intersection between Strand Separation FDR of 0.05 and Peaks not in mock or filter by scaled fold change or apply a minimum number of reads per million. The choice of what peaks you want to consider for downstream analysis depends on the goals and details of your experimental design.

Additional Assistance

Identifying novel and known motifs

With a list of enriched regions, you can now identify recurring patterns or motifs in these regions. Transcription factors bind sites throughout the genome, but each has a characteristic sequence it binds - a consensus sequence that appears in most of its binding sites. By searching for binding site motifs, you can determine the consensus sequence for a transcription factor and predict potential binding locations throughout the genome that may not have been found in your experiment.

Partek Genomics Suite detects de novo motifs using the Gibbs motif sampler (Neuwald et al., 1995) and can search for known transcription factor binding sites using a database such as .

Discover de novo motifs

Select Motif Discovery from the Peak Analysis section of the ChIP-Seq workflow
Select Discover de novo motifs
Select OK

The Detect Motifs dialog will open to allow you to configure the search (Figure 1).

Figure 1. Configuring search parameters for de novo motfis

Select 1/p-value_filtered from the Spreadsheet with genomic regions drop-down menu
Set Number of Motifs to 1
Set Discover motifs of length to 6 bp to 16 bp
Set Result file to Motifs
Select OK

If you have not previously downloaded the reference genome on your computer, you may be asked if you would like to download the .2bit reference genome. If prompted, select Automatically download a .2bit file then select OK. If Partek Genomics Suite cannot connect to the internet, this option may not be available. If not, you will need to download the .2bit file from the UCSC Genome Browser and import it by selecting Manually specify a .2bit file and choosing the downloaded .2bit file. The reference genome map is required to determine which genes overlap the enriched peak regions and to display the aligned sequences in the Genome Viewer.

A motif visualization tab, Sequence Logo, will open and two spreadsheets will be generated. One spreadsheet, motifs (Motifs), contains information about the motif. The other, instances (Motifs_instances.txt), lists the genomic locations of the motif.

Description of Motif Detection Output

Sequence Logo Window

The Sequence Logo tab (Figure 2) opens after motif detection and displays the most significant motif found in the regions listed in the source spreadsheet_._

Figure 2. Viewing the binding site for NRSF. Use the blue arrows to cycle through views of all motif found (if there are more than one). Select Reverse to view the reverse complement sequence.

In this case, the motif finder discovered a motif in the NRSF-enriched regions that is 16 base pairs in length. The height of each position is the relative entropy (in bits) and indicates the importance of a base at a particular location in the binding site.

To view the reverse complement of the motif, select Reverse.

Motifs spreadsheet

The motif information spreadsheet (Figure 3), Motifs, lists the information about all motifs discovered during de novo Motif Detection. This includes five columns describing each motif.

Figure 3. Viewing the Motifs spreadsheet

1. Counts gives the summed counts for each base call across all occurrences of the motif in the region list as {A, C, G, T}

2. Consensus Sequence gives the consensus sequence of the motif in IUPAC nucleotide codes

3. Motif ID gives a unique ID to each discovered motif using its row in the Motifs spreadsheet

4. Log Likelihood Ratio scores the relative likelihood that the pattern did not occur by chance, with larger numbers indicating that it is less likely to have occurred by chance

5. Background frequency (A,C,G,T) gives the frequency of each of the bases in all the sequences of that motif

You can bring up the Sequence Logo visualization of a listed motif by right-clicking on the row header and selecting Logo View from the pop-up menu.

Motif_instances spreadsheet

The _instances (_Motif_instances) spreadsheet (Figure 4) is a child spreadsheet of the Motifs spreadsheet. It details all the locations of the motif(s) detected in the enriched regions. Each row lists a putative binding site for a motif. The columns give detailed information about the putative binding sites.

Figure 4. Viewing the instances spreadsheet

1-4. chromosome, start, stop, strand give the position

5. Motif ID gives the identity of the motif

6. instance gives the sequence of this instance of the motif

7. score gives the log ratio of the probability that this sequence was generated by the motif versus the background distribution. A higher number indicates a better chance that the sequence is an instance of the motif.

Search JASPAR for known motifs

Select Motif discovery from the Peak Analysis section of the ChIP-Seq workflow
Select Search for known motifs
Select OK

Search for known motifs will search the JASPAR database for motifs that are over-represented in the list of sequences in the significant regions list. The JASPAR database will download automatically if needed during the Search for known motifs step. Downloading the JASPAR database will create a spreadsheet in your experiment named JASPAR.txt that contains all of the species-specific motifs in the database. To visualize the motifs, right-click on a row in the JASPAR.txt spreadsheet and select Logo View.

Before Search for known motifs runs, we need to configure the search (Figure 5).

Figure 5. Configuring a search for known motifs in the JASPAR database

Select 1/p-value_filtered (p-value filtered.txt) from the Choose Region Spreadsheet drop-down menu
Select Search using motifs specified in: for Choose Motifs to Search
Set Search using motifs specified in: to 2 (JASPAR.txt) using the drop-down menu
Set Search for to All Motifs using the drop-down menu
Set Sequence Quality >= to 0.7
Name the result file MotifSearch
Select OK

Because we are searching for around 1200 motifs, the process will take some time to complete. Progress is displayed in the progress bar in the lower left-hand side of the Search for Motif(s) in Sequences dialog (Figure 6).

Figure 6. Progress in the motif search will display in the progress bar

Two spreadsheets are created, similar to the spreadsheets in the de novo motif discovery, the motif_summary (MotifSearch) spreadsheet (Figure 7) and the motif_instances (MotifSearch.instance) spreadsheet.

Figure 7. Viewing the results of motif search

In the MotifSearch spreadsheet, each motif used in the motif search is shown. The columns detail the results of the search for each motif that was found in the reads.

1. Motif this is the name or ID of the motif

2. Probability of Occurrence gives the probability of detecting a false positive for this motif in a random DNA sequence

3. Expected Number of Outcomes gives the Probability of Occurrence multiple by the summed length of the reads

4. Actual Number of Occurrences gives a count of sequences that match the known motif in the reads

5. p-value is the uncorrected p-value (binomial test)

As you can see, REST, which is another name for NRSF, is near the top of the list as one of the most significantly over-represented motifs (Figure 7). This motif agrees with the motif found in the de novo motif detection step. Interestingly, other motifs appear a significant number of times in the ChIP-Seq peaks and may represent possible co-factors or regulators.

The motif_instances spreadsheet contains all instances of the motifs from the motif_summary spreadsheet in a format identical to the instances spreadsheet from de novo motif detection.

Generating a list of regions containing a motif

While the motif_instances spreadsheet contains every instance of every motif, it may be useful to create a spreadsheet with just instances of one motif or a select group of motifs. Let's do this for both REST motifs.

Select the motif_instances spreadsheet in the spreadsheet tree
Right-click the 5. Motif Name column
Select Find / Replace / Select... from the pop-up menu (Figure 8)

Figure 8. Finding all REST peaks (step 1)

Set Find What: to REST
Select By Columns for Search:
Select Only in column with 5. Motif Name selected form the drop-down menu
Select Select All (Figure 9)

Figure 9. Selecting all REST instances in motif_instances spreadsheet (step 2)

This finds and selects every instance of REST in column 5. Motif Name.

Select Close

In the motif_instances spreadsheet the selected columns are highlighted.

Right-click on the first highlighted row visible; in this example, we see row 13196
Select Filter Include from the pop-up menu (Figure 10)

Figure 10. Filtering for selected rows

The spreadsheet will now include 2098 rows and a black and yellow bar will appear on the right-hand side of the spreadsheet (Figure 11). The black and yellow bar is a filter indicator showing the fraction of the spreadsheet currently visible as yellow and the filtered fraction as black.

Figure 11. Filtered motif_instances spreadsheet containing 2098 instances of the REST motifs

To create a spreadsheet that contains only the REST instances, we can clone the motif_instances spreadsheet while the filter is applied.

Right-click on motif_instances in the spreadsheet navigator
Select Clone... from the pop-up menu
Set the Name of resulting copy as REST
Select 1/p-value_filtered/motif_summary (MotifSearch) from the Create as a child of spreadsheet drop-down menu
Select OK

This creates a temporary spreadsheet rest from the filtered motif_instances spreadsheet. We can now save the new spreadsheet.

Select rest from the spreadsheet tree
Name the file REST
Select Save

We can now remove the filter from the source motif_instances spreadsheet.

Select motif_instances from the spreadsheet tree
Right-click the filter bar
Select Clear Filter

References

Neuwald, A. F., Liu, J.S., & Lawrence, C.E. (1995). Gibbs motif sampling: detection of outer membrane repeats (Vol. 4). Protein Science.

Additional Assistance

Finding nearest genomic features

In this section, you will learn how to find genomic features (genes) that are near the IP-enriched regions of the data. You will also learn how to classify the peak locations by gene section (5’ UTR, 3’ UTR, Promoter, exon, intron).

Finding the nearest genomic features

Select p-value_filtered from the spreadsheet tree
Select Find Nearest Genomic Feature from the Peak Analysis section of the ChIP-Seq workflow

The Output Overlapping Features dialog will open (Figure 1).

Figure 1. Selecting a database for overlapping features

With this dialog, you can specify the reference database.

Select RefSeq Transcripts 81 - 2017-08-02 or your preferred annotation database

The promoter region can also be defined. The default settings are appropriate in this case.

Select OK

The resulting spreadsheet, gene-list, is a child of the p-value_filtered spreadsheet (Figure 2). Each row represents a transcript.

Figure 2. Viewing genes overlapped by regions

Column 1. transcript chromosome gives the chromosome location of transcript

Column 2. transcript start gives the start of transcript (inclusive)

Column 3. transcript stop gives the end of transcript (exclusive)

Column 4. strand gives the strand of the transcript

Column 5. Transcript ID gives the identify of the transcript

Column 6. Gene Symbol gives the identity of the gene

Column 7. Distance to TSS gives the distance of each enriched region to the transcription start site in base pairs with positive indicating downstream and negative indicating upstream

Column 8. Percent overlap with gene gives the percent of the gene that overlaps with the region

Column 9. Percent overlap with region gives the percent of the region that overlaps with the gene

Percent overlap with gene is more likely to close to 1 in cases where one region covers several genes, in histone studies, for example. Percent overlap with region is likely to be close to 1 in cases where a region is relatively small and is found completely within a gene, in transcription factor binding studies, for example. If both columns are close to 1, then the gene and the region have nearly the same start and stop sites. If both columns are close to 0, then the region does not overlap with the gene directly and likely covers only the promoter region.

Classifying regions by gene section

Another way to interpret the genomic location of peaks is to use Classify regions by gene selection.

Select p-value_filtered from the spreadsheet tree
Select Classify regions by gene selection from the Peak Analysis section of the ChIP-Seq workflow

The Output Overlapping Features dialog will open.

Select RefSeq Transcripts 81 - 2017-08-02 or your preferred annotation database

The promoter region can also be defined. The default settings are appropriate in this case. The results can be further configured to give one result per detected region or one result per genomic feature. The default setting, one result per detected region, is appropriate in this case.

Select OK

A new spreadsheet, gene-classification will be generated (Figure 3).

Figure 3. Classifying regions by gene section

Columns 1-6 have the same contents we saw in gene-list.

Column 7. Gene Section gives the section of the gene that overlaps with the region

Column 8. Distance to TSS gives the distance of each enriched region to the transcription start site in base pairs with positive indicates downstream and negative indicating upstream

Column 9. Distance to nearest gene gives the distance of each enriched region to the nearest gene in base pairs with positive indicating downstream and negative indicating upstream

Column 10. Sample ID gives the sample in which the region is enriched