• Minimum System Requirements

• Computer Host ID Retrieval

• Node Locked Installation

This document describes how to setup and configure a node locked Partek Genomics Suite license and is required for users who purchase a node locked license.

• Windows Installation

• Macintosh Installation

Additional Assistance

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

Node Locked

The Partek Genomics Suite software and license resides on a single computer (laptop, desktop, or server - any type). Any user with an account on the computer can use the license. Remote access capability is not available. Windows and Macintosh platforms only.

Floating Concurrent

The Partek Genomics Suite license is installed on one computer on a network, which becomes the "license server". Partek Genomics Suite software is installed on an unlimited number of computers, which use the network to access the license. Concurrent license use is limited to the number of seats purchased. The license server should remain on and accessible over the network at all times.

Locked Floating

The Partek Genomics Suite license is installed on one computer and can be accessed locally or remotely by only one person at a time.

How do I configure Partek Genomics Suite to use an external hard drive or an alternate internal hard drive (SSD)?

In Partek Genomics Suite:

• Go to Edit > Preferences > File Locations

This guide is specific to the installation of Partek Genomics Suite software on a Macintosh operating system.

Download Partek Genomics Suite

With administrative privileges, click on the button below to download the latest version of Partek Genomics Suite.

GO ANOVA, GSEA/GeneSet ANOVA, and Pathway ANOVA

As these features require intensity (or count) data as well as experimental groups, these features cannot be performed on an imported lists.

This document was developed for Partek Genomics Suite version 6.6 software. Documentation for Partek Genomics Suite version 7.0 software is in development and will replace this document.

Additional Assistance

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

Please follow the directions below on the computer that you wish to install Partek Genomics Suite. Our Licensing team will use this information to generate your license file. This license file will be emailed back to you as an attachment with installation instructions.

Windows, Macintosh, Linux:

1. With administrative privileges, download the .

2. Once the installation is complete, start the application by double clicking on the Partek Genomics Suite icon.

3. Select Copy Information from the Computer Information section and paste the retrieved host name and ID in an email and send it to your account representative (figure 1).

This document was developed for Partek Genomics Suite version 6.6 software. Documentation for Partek Genomics Suite version 7.0 software is in development and will replace this document.

Additional Assistance

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

This document was developed for Partek Genomics Suite version 6.6 software. Documentation for Partek Genomics Suite version 7.0 software is in development and will replace this document.

Additional Assistance

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

This document was developed for Partek Genomics Suite version 6.6 software. Documentation for Partek Genomics Suite version 7.0 software is in development and will replace this document.

Additional Assistance

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

This document was developed for Partek Genomics Suite version 6.6 software. Documentation for Partek Genomics Suite version 7.0 software is in development and will replace this document.

Additional Assistance

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

This document was developed for Partek Genomics Suite version 6.6 software. Documentation for Partek Genomics Suite version 7.0 software is in development and will replace this document.

Additional Assistance

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

Partek Genomics Suite is a comprehensive suite of advanced statistics and interactive data visualization specifically designed to reliably extract biological signals from noise. Designed for high-dimensional genomic studies containing thousands of samples, Partek Genomics Suite is fast, memory efficient and will analyze large data sets on a personal computer. It supports a complete workflow including convenient data access tools, identification and annotation of important biomarkers, and construction and validation of predictive diagnostic classification systems.

• Lists

• Annotation

Additional information can be found in the manual for Partek Genomics Suite version 6.6.

Additional Assistance

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

This guide is for Partek Genomics Suite version 6.6 users with node-locked licenses.

Before following the steps shown in the videos below, please:

• Download the Partek Genomics Suite version 7.0 installation file from here.

• Download the Partek Genomics Suite version 7.0 license file you received from our licensing team and save it to your desktop. If you do not have a Partek Genomics Suite 7.0 license file, please contact [email protected].

Installation on Windows 10 is shown, but the process will be similar for older versions of Windows and on Mac.

Installing Partek Genomics Suite version 7.0

Adding a license file

After installing and adding your license file, you can delete the installation file and license file from your desktop; a copy of your license file is saved to your license file folder (C:\Program Files\Partek Genomics Suite 7.0\license by default on Windows) after it has been added using the license manager.

Additional Assistance

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

This document describes the necessary steps to setup and configure a FlexNet license server for use with locked floating and floating concurrent Partek Genomics Suite licenses.

• Linux Installation

• Installing FlexNet on Windows

Additional Assistance

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

There are two main visualizations for use with GO ANOVA outputs:

• Dot plots used to visualize differential expression of functional groups

• Profile plots used for visualizing disruption of gene expression patterns within the group

Dot Plots

Dot plots represent each sample with a single dot. The position of each dot is calculated as the average expression of all genes included in the functional group. Invoke this plot by right clicking on the row header of a functional group of interest and choosing Dot Plot (Orig. Data). The color, shape, and size of the dots can be set to represent sample information in the plot properties dialogue, invoked by pressing on the red ball in the upper left.

Figure 1 shows a dot plot for a GO category "cell growth involved in cardiac muscle cell development", which is expressed in the heart at a level of almost four times that of the brain, evidenced by the difference of just under two units on the y-axis (in the current example the values on the y-axis are shown in log2 space). Note that the replicates are grouped neatly, making this category highly significant. That is not a surprise, given that the genes belonging to that category are likely very specific for the heart.

Figure 1. Dot plot of a significantly differentially expressed GO category. Each dot is a sample, box-and-whiskers summarize groups

Profile Plots

Profile plots or profiles represent each category of one of the GO ANOVA factors as a few overlapping lines. Horizontal coordinates refer to individual genes or probes in the original data. Vertical coordinates represents expression of the individual gene. Invoke this plot by right clicking on the row header of a function group of interest and choosing Profile (Orig. Data). This plot is useful as the pattern of gene expression in the group is displayed as a line. If the pattern is conserved across treatments, the lines will lie parallel, but if the gene reacts differently, the lines will follow a different pattern, maybe even cross each other.

Profile plot on Figure 2 visualizes a GO category without differential expression, but with significant disruption. Note that the gene TNNI3 is up-regulated in the heart, while STX1A is down-regulated in the heart.

Figure 2. Profile plot of a GO category with significant disruption but not differential expression. Each data point is a gene (error bars are standard error of the mean)

Additional Assistance

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

The GenomeStudio plug-in lets you export data into a project that can be opened in Partek Genome Suite open directly. It is the fastest and most consistent way to get fully annotated Illumina data into Partek Genomics Suite.

• Import gene expression data

• Import Genotype Data

Additional Assistance

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

This guide is specific to the installation of Partek Genomics Suite software on a Windows operating system.

Download Partek Genomics Suite

With administrative privileges, click the below button to download the latest version of Partek Genomics Suite.

Download Partek™ Genomics Suite™

In some cases, a Microsoft Visual C++ Package failure message may appear (Figure 1). Select "Yes" to continue with the installation.

Run the Partek Genomics Suite Application

Once the download is completed, start the application by double clicking on the Partek Genomics Suite application icon located on your desktop. The default Partek License Manager window will appear. You will be prompted to provide a license file.

1. Save the license.dat (or license.lic) file that you received from the Patek Licensing department to your desktop.

• If you do not have license, please contact your account representative or .

2. Select Add License (Figure 2).

3. Select the License file radio button.

4. Select Browse.

5. Click on the the license.dat (or license.lic) file located on your desktop and select Open (Figure 3).

6. The Partek License Manager - Add License screen will appear. Select Add (Figure 4).

• License file path: C:/Users/username/Desktop/license.dat

• License file directory: C:\Program Files\Partek Genomics Suite 7.0\license

The Partek License Manager window will now show you the status of your license (Figure 5).

7. Exit the Partek License Manager and Partek Genomics Suite will automatically start.

Once the software has been installed and the license has been added, you may delete the license.dat (or license.lic) file from your desktop (if you prefer, this is not required); a copy of your license file is saved to your license file folder (C:\Program Files\Partek Genomics Suite 7.0\license folder) after it has been added using the Partek License Manager.

Additional Assistance

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

This guide is specific to the client computer connection instructions to a floating concurrent Partek Genomics Suite license server.

Download Partek Genomics Suite

With administrative privileges, download the latest version of Partek Genomics Suite.

Run the Partek Genomics Suite Application

Once the download is completed, start the application by clicking on the Partek Genomics Suite icon. The default Partek License Manager window will appear. You will be prompted to provide a license (figure 1).

1. Select Add License.

2. Select the License server radio button.

3. Enter the Server Name and select Add.

• You will need to obtain the server name from your license server administrator.

Additional Assistance

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

Node Locked

Windows: Under the control panel, click Add/Remove Programs and select Partek Genomics Suite and click Uninstall.

Macintosh: Delete the license.dat file from the /Users/Shared folder.

Floating Concurrent

Windows, Linux, Macintosh:

1. Log on to the server as an administrator.

2. Navigate to the "C:\FLEXnet" folder and open lmtools.exe.

3. On the first tab - "Service/License File", make sure that the "Configuration using Services" radio button is selected and Partek FlexNet Service appears in the white box.

4. Go to the "Start/Stop/Reread" tab. You should see the same Partek FlexNet Service highlighted in the list of installed services. Check the "Force Server Shutdown" box, and click the "Stop Server" button.

5. Go to the "Config Services" tab, verify that the Partek FlexNet Service is selected in the "Service Name" box, and click "Remove Service". Select "Yes" when prompted.

6. If there are no other applications installed on the server licensed with FlexNet, you may safely delete the FlexNet folder and all of its contents.

Locked Floating

Windows, Linux, Macintosh:

1. Log on to the server as an administrator.

2. Navigate to the "C:\FLEXnet" folder and open lmtools.exe.

3. On the first tab - "Service/License File", make sure that the "Configuration using Services" radio button is selected and Partek FlexNet Service appears in the white box.

4. Go to the "Start/Stop/Reread" tab. You should see the same Partek FlexNet Service highlighted in the list of installed services. Check the "Force Server Shutdown" box, and click the "Stop Server" button.

5. Go to the "Config Services" tab, verify that the Partek FlexNet Service is selected in the "Service Name" box, and click "Remove Service". Select "Yes" when prompted.

6. If there are no other applications installed on the server licensed with FlexNet, you may safely delete the FlexNet folder and all of its contents.

Additional Assistance

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

This tutorial will illustrate:

• Importing Affymetrix CEL files

• Adding sample information

• Exploring gene expression data

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

Down syndrome is caused by an extra copy of all or part of chromosome 21; it is the most common non-lethal trisomy in humans. At the time of the study used in this tutorial, conflicting reports had thrown into doubt whether individuals with Down syndrome have dysregulation of gene expression throughout the genome or primarily in genes from chromosome 21. To address this question, Affymetrix GeneChip™ Human U133A arrays were used to assay 25 samples taken from 10 human subjects, with or without Down syndrome, and 4 different tissues. The data revealed a significant upregulation of chromosome 21 genes at the gene expression level in individuals with Down syndrome; this dysregulation was largely specific to chromosome 21 and not a genome-wide phenomenon.

The raw data is available as experiment number GSE1397 in the .

Data and associated files for this tutorial can be downloaded using this link - (right-click the link and choose "Save Link As" to download the tutorial data).

Additional Assistance

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

This user guide illustrates:

This user guide describes how to export gene expression data using Partek's Report Plug-in for Illumina GenomeStudio Gene Expression Module for use in Partek Genome Suite. The GenomeStudio plug-in lets you export data into a project that can be directly opened in Partek Genomics Suite. It is the fastest and most consistent way to get fully annotated Illumina gene expression data into Partek Genomics Suite.

Partek Gene Expression plug-in installation

Download the plug-in zip file

unzip the file, there is a folder called PartekReport which contains two .dll files --Partek.Common.dll

When looking for simple differential expression, sorting by ascending on the factor p-values is ideal. This will find groups that are the most significantly apart across all the contained genes. In the interest of finding groups that are less likely to be called by chance, it may be wise to filter to groups with a minimum of 4 or 5 genes (Figure 1). Simple filters can be done using the interactive filter () available from the button on the toolbar at the top of the screen.

If there is more than one factor in the model, more complex criteria combining the factors can be specified using Tools>List Manager menu Advanced tab. For example, to find categories that are significant and changed by at least two fold, make two criteria: one for a low p-value and the other for a minimum of two fold change, and take the intersection of the two criteria.

Figure 1. Top ten functional groups sorted by the Tissue p-value after filtering to a minimum five gene in the GO category. Note that most of the groups can be directly related to the heart muscle

If the disruption (factor*gene interaction) is tested, the filters can become more complicated. The most pressing need for complex filters is that when analyzing larger functional groups it is not expected that the entire functional group will behave the same. Looking back at Figure 1, notice how the low values in column 7 are present because not every gene is equally differentially expressed even in the most differentially expressed of groups. That is, when there is significant differential expression, it is likely that there will also be disruption as at least a single gene is likely participating in a role beyond that of the functional group and will not follow the pattern of the rest of the group. This situation is expected and leads to a new type of filter.

This document was developed for Partek Genomics Suite version 6.6 software. Documentation for Partek Genomics Suite version 7.0 software is in development and will replace this document.

Additional Assistance

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

With gene ontology (GO) ANOVA, Partek Genomics Suite includes the ability to use rigorous statistical analysis to find differentially expressed functional groupings of genes. Leveraging the Gene Ontology database, Partek Genomics Suite can organize genes into functional groups. Not only can GO ANOVA detect up and down regulated functional groups, but also functional groups, which are disrupted in a few genes as a result of treatment. Moreover, the common diction of the GO effort enables this analysis to be compared across all types of gene expression data, including those from other species. Traditional tests, such as GO enrichment, require defining filtered lists of differentially expressed genes followed by an analysis of functional groups related to those genes. On the other hand, GO ANOVA is performed directly after data import and normalization. This minimizes the risk that a highly stringent filter will cause important functional groups to be overlooked.

Other tests, such as gene set enrichment analysis (GSEA), tolerate minimal or no pre-filtering. However, these tests are very limited in their ability to integrate complicated experimental designs. GSEA, for example, can only handle two groups at a time. GO ANOVA, on the other hand, can leverage the wealth of sample information collected and use powerful multi-factor ANOVA statistics to analyze very complex interactions and regulatory events. The analysis output includes detailed statistical results specifying the effect and importance of phenotypic information on differential expression and subsequent disruption of Gene Ontology functional categories. Furthermore, GSEA calculates enrichment scores using a running-sum statistic on a ranked gene list. GO ANOVA takes into account more information by utilizing each sample’s expression values to calculate the enrichment score.

Note that the same principles apply to Pathway ANOVA, the only difference being the mapping file; GO ANOVA organizes genes into GO categories, while Pathway ANOVA looks at pathways.

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, .

• 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

Frequently Asked Questions related to Partek Genomics Suite License Server

What is required to access the log file to find out who is using the software?

The log file is written on the computer that runs the license server. The user specifies the location of this log file when running the lmgrd command.

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.

Preparing a data set for analysis requires importing the data, normalizing the data as appropriate for standard gene expression analysis, and inserting columns containing the experimental variables. Checkout for more details about preparing data. It is not necessary to perform a differential analysis of gene expression before GO ANOVA.

For the sake of example, the following walkthrough will consider an experiment that has been imported which includes two different tissues, brain tissue and heart tissue, extracted from a small set of patients.

The GO ANOVA function is available in the Gene Expression, microRNA Expression, RNA-Seq, and miRNA-Seq workflows.

• Select the Gene Expression

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)

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

Scientists often develop lists of genes, probes, transcripts, SNPs, and genomic regions of interest from analysis tools, research papers, and databases. Using Partek Genomics Suite, these lists can be integrated with genomics data sets, analyzed with powerful statistics, and visualized for new insights.

This user guide will illustrate:

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

This document describes the steps necessary to set up a FlexNet license server for Partek Genomics Suite on a Linux server. You are not required to install the full version of Partek on your license server; only the license server executables installed by the Partek License Server installer or contained in FLEXnet11.12.zip are required to serve Partek FlexNet licenses on your network.

You must log on to the license server with an account which has administrative or sudo root privileges to install a system service (sysv or systemd), create directories on system folders, and/or modify the system configuration. Partek recommends completing all installation instructions with an account with administrative or sudo root privileges. If you install into a non-system directory, you may run the license server manually (see the Run the License Server Manually section).

Installation

Follow either the Automated Installation or the Manual Installation instructions below.

(make sure lsb-core is installed)

Automated Installation

1 . Download the .

2. When prompted, select "/opt/FlexNet" as the directory and click "Continue".

3. In the pop-up menu, select all components from the list and click "Continue".

4. Click "Install" and proceed to the Configuration section below.

Manual Installation

1 . Unzip the file into the folder of your choice. The recommended location is "/opt". This will create a folder called "FlexNet" (it's full pathname will be "/opt/FlexNet") containing the executable and providing a location to store your license.lic file.

2. Move all of the files from the linux64 subfolder to the /opt/FlexNet folder.

3. Proceed to the Configuration section below.

Configuration

Save your license.dat or license.lic file into the FlexNet folder as license.lic. It's full pathname will be "/opt/FlexNet/license.lic", if you use the recommended installation directory.

Follow either the Systemd Installation or SYSV Installation instructions below.

Systemd Installation

To install FlexNet to run as a system service (use admin privileges or right click and "Run as administrator"):

1 . Create user "parteklm" in group "parteklm"

• sudo useradd -r -s /sbin/nologin parteklm

• sudo chown parteklm:parteklm /opt/FlexNet

2. If not using default values, edit the file parteklm.service in the installation directory (/opt/FlexNet/parteklm.service) and change the non-default path, User, and/or Group

3. Make sure that /usr/tmp is created and has correct permissions by running the commands:

• sudo mkdir -p /usr/tmp

• sudo chmod 1777 /usr/tmp

4. Copy the parteklm.service file into place (/usr/lib/systemd/system may also be used):

• sudo cp /opt/FlexNet/parteklm.service /etc/systemd/system/.

5. Enable the service:

• sudo systemctl enable parteklm

6. Start the service:

• sudo systemctl start parteklm

This will add a FlexNet server for Partek licenses into your set of system services that will automatically start on system restart. You can use the standard systemctl command to manage the service.

SYSV Installation

To install FlexNet to run as an sysv system service (still using admin privileges or sudo root):

1. Create user "parteklm" in group "parteklm"

• sudo useradd -r -s /sbin/nologin parteklm

• sudo chown parteklm:parteklm /opt/FlexNet

2. If not using default values, edit the file parteklm.init in the installation directory (/opt/FlexNet/parteklm.init) and change the non-default path (FLEXNETDIR), USER, and/or GROUP

3. Make sure that /usr/tmp is created and has the correct permissions by running the following commands:

• sudo mkdir -p /usr/tmp

• sudo chmod 1777 /usr/tmp

4. Copy the parteklm.init file into place (/usr/lib/systemd/system may also be used):

• sudo cp /opt/FlexNet/parteklm.init /etc/init.d/parteklm

• sudo chmod +x /etc/init.d/parteklm

5. Enable the service (using chkconfig):

• sudo chkconfig --add parteklm

• sudo chkconfig parteklm on

6. Start the service:

• sudo service parteklm start

This will add a FlexNet server for Partek licenses into your set of system services that will automatically start on system restart. You can use the standard service command to manage the service.

Run the License Server Manually

To run the license server manually, you may use the command line (where <path_to_FlexNet> is /opt/FlexNet or the folder where you installed FLEXNet):

cd <path_to_FlexNet>

./lmgrd -c <path_to_FlexNet> -L <path_to_FlexNet>/log.txt

Putting a "+" (plus) character in front of the path to the log file (log.txt) causes the license manager server to append logging entries.

For more details, see the lmgrd - License Server Manager/Starting the License Server Manager on UNIX Platforms/Manual Start section of the .

Firewall Configuration

Refer to the and/or the ReadMe_FlexNetFirewallPinholes.txt document lcoated in your FlexNet folder for information about firewall configuration.

Advanced Configuration

Refer to the for advanced configuration options or questions.

GO ANOVA output is very similar to standard ANOVA output except each row in the resulting sheet contains statistical results from a single GO functional group rather than a single gene. Columns can be broken down into four sections:

• Annotations contain detail about the category being considered

• ANOVA results contain the significance of the effect of the factors in the model

• Contrast results contain significance and fold change of the difference between groups compared via contrast

• F-ratios display the significance of the factors in the ANOVA model

Annotations

Annotations will take up the first four columns of the results sheet (Figure 1). The first column (# of genes) is the number of genes in the GO category. Specifically, this is not necessarily the number of unique genes in the category; depending on the technology, it can be the number of probes or probe sets on the microarray whose targets fall into the GO category. Genes targeted more than once will be counted more than once. The second column (GO ID) is the unique numeric identifier of the GO category; it is sometimes useful for searching with when the GO category has a very long name. The third column is the type of the GO category, while the fourth column (GO Description) is the name of the GO category.

Figure 1. GO ANOVA annotation columns (example)

When right click on any row header to choose Create Gene List , a new spreadsheet will be generated, it contains a list of genes (probes/probesets) within the selected GO category.

ANOVA Results

ANOVA results will include a column for each factor in the setup (Figure 2). A column with the name of the factor or interaction followed by p-value will contain how significant the effect of the variable is on the data. A lower p-value corresponds with a more significant effect. For example, a p-value of 0.1 for tissue means that given the difference between the tissue and the inherent variability of the measurements of the genes in the functional group, there is a 10% likelihood that the tissues are equivalent. A p-value of 0 occurs when the value is too small to be displayed. This can be caused by a very low estimate of inherent variability due to either a very small number of replicates or severely unbalanced data.

Figure 2. Viewing the GO ANOVA result

In the example experiment, a low p-value for tissue would imply the functional group is differentially expressed across tissues.

A low p-value for an interaction implies that the effect of one factor on the other is significant. In the example dataset, no interactions between two main variables were included as factors. To illustrate what the interaction p-value would mean, consider the case that a drug compound and a control injection were dosed over several time points and an interaction between injection compound and time point was included in the GO ANOVA. A low p-value for the drug-time point interaction corresponds to the effect of drug on the functional group being altered with time.

A column will also be present for each factor placed in the Disruption Factor(s) box. This column will have the header Disruption(Factor name). A low p-value in this column corresponds to the different states presenting with different gene patterns within the functional group. For functional groups containing only a single gene, no value will be present as the pattern cannot change. In the example experiment, a low p-value for the Disruption(Tissue) represents function categories which have different genes operating in the heart and in the brain.

Contrast Results

Contrast results include four columns for each of the comparisons declared during GO ANOVA setup. The first column contains the p-value representing the significance of the difference between the two categories. The second column contains the ratio between the two groups where increases are represented as greater than one and decreases are represented as values between zero and one. The third column is the fold change of the functional group between the two categories where increases are greater than one and decreases are less than negative one. The fourth column contains a plain text description of the direction of the fold change. Fold changes and ratios represent the average change in the functional category. In the example, a contrast was run comparing expression in the cerebral tissue to the heart tissue (Figure 3). As these were the only tissues, the p-values are identical to those in column 5. While the p-value column shows which groups are differentially expressed between the tissues, the fold change columns allow us to see by how much they are differentially expressed. Using the sign of the fold change, or the description column, you can see which categories are increased in brain and which are increased in heart.

Figure 3. Viewing the GO ANOVA contrast columns

F-Ratios

F-ratios (Figure 4) are used in the computation of p-values. The values in the columns can safely be ignored by most users; there are exceptional cases when the F-ratios may be informative. To see the general significance of the factors included in the model, a Sources of Variation plot can be computed from these values from the View menu (or the Workflow). The higher the average F-ratio, the more important the factor is to the model on average.

Figure 4. Viewing the GO ANOVA F-ratios

Additional Assistance

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

The method used to detect changes in functional groups is ANOVA. For detailed information about ANOVA, see Chapter 11 of the Partek User Manual. There is one result per functional group based on the expression of all the genes contained in the group. Besides all the factors specified in the ANOVA model, the following extra terms will be added to the model by Partek Genomics Suite automatically:

• Gene ID - Since not all genes in a functional group express at the same level, gene ID is added to the model to account for gene-to-gene differences

• Factor * Gene ID (optional) - Interaction of gene ID with the factor can be added to detect changes within the expression of a GO category with respect to different levels of the factor, referred to in this document as the disruption of the categories expression pattern or simply disruption

Suppose there is an experiment to find genes differentially expressed in two tissues: Two different tissues are taken from each patient and a paired sample t-test, or 2-way ANOVA can be used to analyze the data. The GO ANOVA dialog allows you to specify the ANOVA model, which includes the two factors: tissue and participant ID. The analysis is performed at the gene level, but the result is displayed at the level of the functional group by averaging of the member genes’ results. The equation of the model that can be specified is:

y = µ + T + P + ε

• y: expression of a functional group

• µ: average expression of the functional group

• T: tissue-to-tissue effect

When the tissue is interacted with the gene ID then the ANOVA model becomes more complicated as demonstrated in the model below. The functional group result is not explicitly derived by averaging the member genes as the new model includes terms for both gene and group level results:

y = µ + T + P + G + T *G + ε

• y: expression of a functional group

• µ: average expression of the functional group

• T: tissue-to-tissue effect

In the case that there is more than one data column mapping to the same gene symbol, Partek Genomics Suite will assume that the markers target different isoforms and will not treat the two markers as replicated of the same gene. Instead, each column is treated as a gene unto itself.

If there are only two samples in the spreadsheet then, Partek Genomics Suite cannot calculate a type by gene ID interaction. In this case, the result spreadsheet will contain a column labeled Disruption score. First, for each gene in the functional group Partek Genomics Suite will calculate the difference between the two samples. A z-test is used to compare the difference between each gene and the rest of the genes in the functional group. The disruption score is the minimum p-value from the z-tests comparing each gene to the rest in the functional group. A low disruption score therefore indicates that at least one gene behaves differently from the rest. This implies a change in the pattern of gene expression within the functional group and potential disruption of the normal operation of the group. The category as a whole may or may not exhibit differential expression in addition to the disruption.

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The volcano plot displays p-values and fold-changes of numerous genomic features (e.g., genes or probe sets) at the same time. This allows differentially expressed genes to be quickly identified and saved as a gene list.

Note: the same list can be generated without a visual aid using the List Manager (ANOVA Streamlined tab).

We will invoke a volcano plot from an ANOVA results child spreadsheet with genes on rows.

• Select View from the main toolbar

• Select Volcano Plot (Figure 1)

Figure 1. Invoking a volcano plot on an ANOVA results spreadsheet

The Volcano Plot Configure dialog will open (Figure 2).

Figure 2. Select the columns to display in the volcano plot

• Select the fold-change and p-value columns you would like to visualize from the ANOVA results spreadsheet; here we have chosen 12. Fold-Change(Down Syndrome vs. Normal) for the X Axis and 10. p-value(Down Syndrome vs. Normal) for Y Axis

• Select OK

The volcano plot will open in a new tab (Figure 3). Control and color options for the volcano plot are largely similar to those described for a . On volcano plots with many probe(sets)/genes, the shapes and sizes of individual probe(sets)/genes will not be visible until they are selected.

Figure 3. The volcano plot shows each probe(set)/gene as a point. The X Axis shows fold change with no change (N/C) as the mid-point. The Y Axis shows p-values in descending value from a maximum of 1 at the X Axis intersection.

To facilitate analysis, we can add cutoff lines for both fold-change and p-value.

• Select ()

• Select the Axes tab

• Select Set Cutoff Lines (Figure 4)

Figure 4. Adding cutoff lines to the volcano plot

• Set Vertical Line(s) to 1.3 and -1.3

• Set Horizontal Line(s) to 0.05

• Select Select all points in a section

Figure 5. Setting cutoff lines. The vertical lines are fold-change cutoffs. The horizontal line is a p-value cutoff.

• Select OK to close the Plot Rendering Properties dialog

The volcano plot now has cutoff lines for fold-change and p-value (Figure 6).

Figure 6. Cutoff lines facilitate visual analysis of ANOVA results

Because we selected Select all points in a section when adding the cutoff lines, selecting any of the quandrants will select all probe(sets)/genes in that quadrant. If this option is not selected, individual probe(sets)/genes or groups can be selected using selection mode. Gene lists can be generated from selected probe(sets)/genes.

If columns are selected in the ANOVA results source spreadsheet for the volcano plot, only those columns will be included in the created list.

• Select the upper right-hand quadrant of the volcano plot

• Right click the selected quadrant

• Select Create List (Figure 7)

Figure 7. Creating a gene list from a volcano plot

• Give the new list a name and description as appropriate

• Select OK

The list will be saved as a text file and open as a child spreadsheet in the Analysis tab.

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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

• Select () from the quick action bar to save the ANOVA-2way (ANOVA Results) spreadsheet with the added annotation

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

• Select () from the quick action bar to invoke the interactive filter

• 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)

Figure 5. Renaming and configuring filtered spreadsheet

• Select () from the quick action bar to save the 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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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

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

Pathway Enrichment is the only option available for a gene list. To learn more about the other option, Pathway ANOVA, see the tutorial, which follows the same procedure as Pathway ANOVA.

• 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.

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This user guide describes how to export copy number and genotype data using Partek's Report Plug-in for Illumina GenomeStudio Genotype Module for use in Partek Genome Suite. The GenomeStudio plug-in lets you export data into a project that can be opened in Partek Genome Suite open directly. It is the fastest and most consistent way to get fully annotated Illumina gene expression data into Partek.

Partek Genotype plug-in installation

Download the plug-in zip file

unzip the file, there is a folder called PartekReport which contains two .dll files --Partek.Common.dll and Partek.GeneExpression.GenomeStudio.dll, move the PartekReport folder to

C:\Program Files \Illumina\GenomeStudio 2.0\Modules\BSGT\ReportPlugins, if there is no ReportPlugins folder in BSGT folder, create one, the path and folder names have to be exactly match one described above (Figure 1).

Export report from GenomeStudio

In GenomeStudio genotype project:

• Choose Analysis > Reports>Report Wizard from the main menu

• Select Custom Report and choose Partek Report Plug-in from the drop-down list

• Specify AnnotationName, do NOT include <> in the name, you can the same name as the .bgx file you imported the ddata with, or a unique name to your dataset

Figure 1. Configuring the GenomeStudio copy number report dialog

• Leave all the others as default value (Figure 2) click Next

• Specify the report file name, we recoommend to put the exported files in their own folder, which allows you to move thefolder instead of all the files individually.

• Click Finish (Figure 2)

Figure 2. Specify output folder and file name

The output generate 9 files in the folder including a project file (.ppj), annotation file, summary file and 3 sets of Partek spreadshet file-- each spreadsheet consists of 2 files.

Open project in Partek Genomics Suite

To open the report, launch Partek Genomics Suite, choose File > Open Project, browse to the .ppj file to open. There will be three spreadsheets opened (Figure 3)

Figure 3. Open project in Partek Genomics Suite

Spreadsheet 1 contains genotype calls, spreadsheet 2 contains log R ratio which is copy number in log scale, spreadsheet 3 contains B allele frequency.

To do copy number analysis, select spreadsheet 2 log R ratio, choose Copy number workflow, start from QA/QC section. Genotype spreadsheet will be used for Association and LOH workflow.

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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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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

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

• Select the temporary list in the spreadsheet tree

• Select () from the command bar

• Save the list as fourtreatments

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A list of SNPs using dbSNP IDs can be imported as a text file and associated with an annotation file as described for a list of genes. The annotation file you use to annotate the SNPs should minimally contain the chromosome number and physical position of each locus.

Novel SNPs or SNPs that are not found in your annotation source must be imported as a region list. For this, follow the procedure outlined in Starting with a list of genomic regions, but use the SNP name in place of a region name.

Annotating SNPs with genes

Starting with a list of SNPs that have been associated with genomic loci using an annotation file and assigned a species with genome build, you can use Find Overlapping Genes to annotate these SNPs with the closest genes.

• Select Tools from the main toolbar

• Select Find Overlapping Genes (Figure 1)

Figure 1. Adding overlapping genes to a SNP list

• Select Add a New Column with the Gene Nearest to the Region from the method dialog

The Report Regions from the specified database dialog will open.

• Select your preferred database. Be sure to match the species and genome build of your SNP list

• Select OK

This will add 3 columns to the list of SNPs spreadsheet including Nearest Feature, which will indicate the nearest gene and strand (Figure 2).

Figure 2. Find Overlapping Genes adds three columns to a SNP list: overlapping features, nearest feature, and distance to nearest feature (bps)

To allow gene list operations such as GO Enrichment or Pathway Enrichment to be performed on the SNP list, we can set the Nearest Feature column as the gene symbol column for the spreadsheet.

• Right click the spreadsheet in the spreadsheet tree

• Select Properties from the pop-up menu

• Select Gene symbol instead of Marker ID

Figure 3. Setting Nearest Feature as the gene symbol allows gene list functions to be performed on a SNP list

Annotating a Partek Genomics Suite-generated SNP list with SNVs

If you have a SNP spreadsheet that was generated using Partek Genomics Suite (not imported as a .txt file), you can annotate the SNP list with gene, transcript, exon, and information about the predicted effect of the SNPs.

• Select Tools from the main command toolbar

• Select Annotate SNVs

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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

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 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 or by selecting this link - . 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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The profile plot displays probe(set)/gene intensity values across samples and genes.

We will invoke a profile plot from a gene list child spreadsheet with genes on rows.

• Select the rows to be visualized

• Right-click on a row header of one of the selected rows

• Select Profile Plot (Orig. Data) from the pop-up menu (Figure 1)

Figure 1. Selecting Profile Plot for selected rows

The profile plot will be displayed in a new tab (Figure 2). Lines are probe(sets)/genes and columns are samples from the parent spreadsheet.

Figure 2. Basic profile plot. Each line represents a different prob(set)/gene; each column represents a sample from the parent spreadsheet

A basic profile plot will likely need customization. The plot configuration, properties, and control options are the same as shown for a . We will illustrate a few modifications here.

We can change the row labels to show each sample ID.

• Select ()

• Select the Axes tab

• Set Grid to 1

We can add symbols to show which group each sample belongs to.

• From the Shape by drop-down menu, select 3.Type

• Select OK

Symbols have now been added to each profile line plot (Figure 3).

Figure 3. The profile plot can be modified to facilitate analysis or presentation

Note that samples present on the parent spreadsheet cannot be excluded from the profile plot. To plot only a subset of the samples you must filter the parent spreadsheet.

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Partek Genomics Suite (Next Generation Sequencing Studies)

• Windows: 7 SP1 or newer

• Mac: OS 10.11 - 10.13.4

• Linux: Ubuntu® 12.04, Red Hat® 6, CentOS® 6, or newer

• 64-bit 2GHz quad-core processor

• 16GB of onboard memory

• 500GB of storage available for data and installation

• A graphics card with OpenGL-capable drivers

Partek Genomics Suite (Microarray Studies)

• Windows: 7 SP1 or newer

• Mac: OS 10.11 - 10.13.4

• Linux: Ubuntu® 12.04, Red Hat® 6, CentOS®, or newer

Partek Pathway

• Windows: 7 SP1 or newer

• Mac: OS 10.11 - 10.13.4

• Linux: Ubuntu® 12.04, Red Hat® CentOS®, or newer

Additional Assistance

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This document is specific to the installation of a floating concurrent Partek Genomics Suite license on a Windows server. It is not required to install the full version of Partek on your license server; only the license server executables installed by the Partek License Server installer are required to serve Partek FlexNet licenses on your network.

Installation

1. Download the Windows Installer.

2. When prompted, select "C:FlexNet” as the installation folder and click “Next”.

3. Select all components from the list and click “Next”.

4. Select “Partek License Server” as the Start Menu and click “Next”.

5. Click "Install" and "Finish".

6. Save the license file (license.lic or license.dat) that Partek Licensing Support team sent you as license.lic and proceed to the Configuration section below.

• The full pathname will be "C:\FlexNet\license.lic"

Configuration

To install FlexNet to run as a system service (use admin privileges or right click and "Run as administrator"):

1. Run lmtools.

2. Navigate to the to the “Service/License File” tab and select the “Configuration using Services” radio button (figure 1).

3. Navigate to the “Config Services” tab and fill in the following (figure 2):

a. Service Name: Partek FlexNet Server

b. Path to the lmgrd.exe file: C:\FlexNet\lmgrd.exe

c. Path to the license: C:\FlexNet\license.lic

d. Path to the debug log file: C:\FlexNet\log.txt

e. Check “Use Services” and “start Server at Power Up”

f. Click “Save Service”

4. Navigate to the "Start/Stop/Reread" tab and start the service by clicking the "Start Server" tab (figure 3).

This will add a FlexNet server for Partek licenses into your set of system services. You can then control this service with the standard services control panel or lmtools.exe.

For a step-by-step video to help you set up your license server on a Windows platform, please visit: .

Firewall Configuration

For information about firewall configuration, refer to the and/or the ReadMe_FlexNetFirewallPinholes.txt document located in your FLEXnet folder.

Advanced Configuration

For advanced configuration options or questions, refer to the .

Additional Assistance

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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

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

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 - . 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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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

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 .

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Partek Pathway provides a visualization tool for pathway enrichment spreadsheets utilizing the KEGG database. This tutorial will illustrate:

• Performing pathway enrichment

• Analyzing pathway enrichment in Partek Genomics Suite

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...

The project will open with three spreadsheets:

1. Affy_miR_BrainHeart_intensities,

2. Affy_HuGeneST_BrainHeart_GeneIntensities,

3. ANOVAResults gene.

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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.

• To save changes to the spreadsheet, select the Save Active Spreadsheet icon (). Spreadsheets with unsaved changes have an asterisk next to their name in the spreadsheet tree.

Note: More details on Random vs. Fixed Effects can be found later in this tutorial under the section .

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The Manhattan plot is a common way to visualize p-values or log-odds ratios for GWAS studies across genomic coordinates.

The starting point for a Manhattan plot is a spreadsheet with SNPs on rows and p-values or log-odds ratios in a column. If beginning with p-values, you will need to convert the p-values to -log10(p-value).

• Select the column with p-values

• Select Transform form the main toolbar

Sort Rows by Prototype is a function that can identify genes with similar expression patterns. For example, if a gene with an interesting expression pattern has been detected, using Sort Rows by Prototype makes it possible to find other genes that have a similar pattern of intensity values. Although this is most commonly used for changes in gene expression over a time course, it can be applied to other experimental designs as well.

To invoke Sort Rows by Prototype_,_ probe(sets)/genes must be on rows. If you want to use this tool to analyze the main intensity values spreadsheet, the spreadsheet must be transposed prior to analysis. A common way to view and analyze gene expression in a time-series experiment is to include means or LS means in the ANOVA spreadsheet.

• Configure the ANOVA dialog to include the factor or interaction of interest

The primary use of the dot plot is visualizing intensity values across samples.

We will invoke a dot plot from a gene list child spreadsheet with genes on rows.

• Right-click on the row header of the gene you want to visualize

• Select Dot Plot from the pop-up menu (Figure 1)

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

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

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

• Associating a spreadsheet with an annotation file

• Adding annotations to a spreadsheet

There are many useful visualizations, annotations, and biological interpretation tools that can operate on a gene list. In order for these features work with an imported list, an annotation file must be associated with the gene list. Additionally, many operations that work with a list of significant genes (like GO- or Pathway-Enrichment) require comparison against a background of “non-significant” genes. The quickest way to accomplish both is to use the background of “all genes” for that organism provided by an annotation source like RefSeq, Ensembl, etc. in .pannot (Partek annotation), .gff, .gtf, .bed, tab- or comma-delimited format. If the file is not already in a tab-separated or comma delimited format, you may import, modify, and save the file in the proper file format.

Associating a spreadsheet with an annotation file

• Select File from the main toolbar

• Select Genomic Database under Import (Figure 1)

• Select the annotation file; in this example, we select a .pannot file downloaded from Partek distributed library file repository – hg19_refseq_14_01_03_v2.pannot

• Delete or rearrange the columns as necessary; we have placed the column with identifiers (should be unique ID) that correspond to our gene list first

• Select File then Save As Text File... to save the annotation file; we have named it Annotation File (Figure 2)

• Select () to close the annotation file

Now we can add the annotation file to our imported gene list.

• Right click 1 (gene_list.txt) in the spreadsheet tree

• Select Properties from the pop-up menu

This brings up the Configure Genomic Properties dialog (Figure 3).

• Select Browse under Annotation File

• Choose the annotation file; we have chosen Annotation File.txt

If this is the first time you have used an annotation, the Configure Annotation dialog will launch. This is used to choose the columns with the chromosome number and position information for each feature. Our example annotation file has chromosome, start, and stop in separate columns.

• Select the proper column configuration options (Figure 4)

• Select Close to return to the Configure Genomic Properties dialog

• Select Set Column: to open the Choose column with gene symbols or microRNA names dialog (Figure 5)

• Select the appropriate column; here the default choice of 1. Symbol is appropriate

• Select OK to return to the Configure Genomic Properties dialog

• Select the appropriate species and genome build options; we have selected Homo sapiens and hg19 (Figure 6)

• Select OK

• Select () to save the spreadsheet

The annotation file has been associated with the spreadsheet and additional tasks can now be performed on the data, e.g. since the annotation has genomic location, you can draw chromosome view on this data.

Adding annotations to a spreadsheet

Inserting annotations from an annotation file

If an annotation file has been associated with a spreadsheet, annotations from the file can be added as columns in the spreadsheet when each identifier is on a row.

• Right click on a column header

• Select Insert Annotation

• Select columns to add from Column Configuration; we have selected Chromosome, Start, and Stop (Figure 7)

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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.

You can practice creating new gene list criteria of your own to become familiar with the List Manager tool. For more information, you can always click on the () buttons.

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).

• Select Rendering Properties ()

• 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

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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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 , 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

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

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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Scatter Plot

A scatter plot is a simple way to visualize differentially expressed genes. We can plot a scatter plot with gene expression values for two samples at one time. While most probe(sets)/genes fall on a 45° line, up- or down-regulated genes are positioned above or below the line.

To draw a scatter plot, you first need to transpose the original intensities spreadsheet so that the samples are on columns and probe(sets)/genes are on rows.

• Select the main spreadsheet

• Select Transform from the main toolbar

• Select Create Transposed Spreadsheet...

• Select the column with sample IDs from the drop-down menu

• Select OK

A new temporary spreadsheet will be created with probe(sets)/genes on rows and samples on columns.

• Select the two sample columns you would like to compare

• Select View from the main toolbar

• Select Scatter Plot (Figure 1)