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User Manual

Partek Flow software is designed specifically for the analysis needs of large genomic data. It has a simple-to-use graphical interface, context sensitive menus, powerful statistics, and interactive visualizations to easily find biological meaning in genomic data.

The information found in this manual will help you to get the most of your Partek Flow software license.

Additional Assistance

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

Interface

Home Page
Home Page Icons
System Options
Progress Indicator and Queue
Searching the Home Page and Project Details

Home Page

The Partek Flow Home page is the first page displayed upon login. It provides a quick overview of recent activities and provides access to several system options (Figure 1).

To access a project, click the blue project name. The projects can be sorted by the column title and searched by criteria using the search options on the right and the search bar above the project table. The Repository summary shows the total number of projects, samples, and cells in your Partek Flow server (Figure 1).

Figure 1. Partek Flow Home page

Shown at the top of the Home page, the New project button provides a quick link to create a new project in your Partek Flow server.

The Transfer files button is used to transfer data to the server.

The Optional columns button can be used to add additional column information to the project table.

The Search button will search for project names and descriptions that have been typed into the search bar.

Additional project details can also be opened and closed individually using the arrow to the left of the project name. To open additional project details for all projects at once, use the arrow to the left of the Project name column header.

The table listing all the projects can be sorted by clicking the sort icon to the right of the table headers. By default, the table is sorted by the date when the project was last modified.

Under the Actions column, three vertical dots will open the project actions options: Open in new tab , Export project , and Delete project (Figure 2).

System Options

The drop-down menu in the upper right corner of the Home page (Figure 3) displays options that are not related to a project or a task, but to the Partek Flow application as a whole. This links to the Settings and Profile and gives you the option to log out of your server.

Progress Indicator and Queue

The next icon is the progress indicator, summarizing the current status of the Partek Flow server. If no tasks are being processed, the icon is grey and static and the idle message is shown upon mouse over. Clicking on this icon will direct you to the System resources under Settings (Figure 4).

If the server is running, the progress indicator will depict green bars animated on the server icon (Figure 5).

Selecting the Queue dropdown will list the number of running tasks launched by the user. Additional information about the queue including the estimated completion time as well the total number of queued tasks launched can be obtained by selecting View queued tasks (Figure 6).

To view all previous tasks, select the View recent activity link. Clicking this link loads the Activity log page (Figure 7). It displays all the tasks within the projects accessible to the user (either as the owner or as a collaborator), including the tasks launched by other users of the Partek Flow instance.

The Display radio buttons enable filtering of the log. All activity will show all the tasks (irrespective of the task owner, i.e. the user starting the task), while My activity lists only the tasks started by the current user. In contrast to the latter, Collaborator’s activity displays the tasks that are not owned by the current user (but to which the user has access as a collaborator).

The Activity log page also contains a search function that can help find a particular task (Figure 8). Search can be performed through the entire log (All columns), or narrowed down to one of the columns (using the drop-down list).

Searching the Home Page and Project Details

The Home page lists the most recent projects that have been performed on the server. By default, the table contains all the projects owned by the current user or ones where the user is a collaborator. The list entries are links that automatically load the selected project.

The Search box can be used to find specific projects based on project titles and descriptions. You can also Search by individual or multiple criteria based on projects Owners, Members, Cell count, Sample count, Cell type, Organ, and Organism (Figure 9). Cell type, Organ, Organism, cell count, and sample count are sourced from the metadata tab. Owner and members are sourced from the project settings tab.

The criteria used for the search are listed above the table along with the number of projects containing the criteria.

The project table displays optional columns as the project name, owner, your role, the members that have access to the project, date last modified, size, number of samples, and number of cells. The drop down contains a thumbnail, short description, samples contained within the project and queue status (Figure 10).

The project settings tab within each project can be used to modify details such as the project name, thumbnail, description, species, members, and owner.

Additional Assistance

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

Importing Data

Partek Flow can import a wide variety of data types including raw counts, matrices, microarray, variant call files as well as unaligned and aligned NGS data.

Navigating the file browser to transfer files to the server
Associate fastq files for multi-omic data
SFTP File Transfer Instructions
Sample Table from a Text File
Import single cell data
Importing 10x Genomics Matrix Files
Importing and Demultiplexing Illumina BCL Files
Partek Flow Uploader for Ion Torrent
Importing 10x Genomics .bcl Files
Import a GEO / ENA project

The following file types are valid and will be recognized by the Partek Flow file browser.

bam
bcf
bcl
bgx
bpm
cbcl
CEL
csv
fa
fasta
fastq
fcs
fna
fq
gz
h5ad
h5 matrix
idat
loom
mtx
probe_tab
qual
raw
rds
sam
sff
sra
tar
tsv
txt
vcf
zip

In cases where paired end fastq data is present, files will also be automatically recognized and their paired relationship will be maintained throughout the analysis.

Matching on paired end files is based on file names: every character in both file names must match, except for the section that determines whether a file is the first or the second file. For instance, if the first file contains "_R1", "_1", "_F3", "_F5" in the file name, the second file must contain something in the lines with the following: "_R2", "_2", "_F5", "_F5-P2", "_F5-BC", "_R3", "_R5" etc. The identifying section must be separated from the rest of the filename with underscores or dots. If two conflicting identifiers are present then the file is treated as single end. For example, s_1_1 matches s_1_2, as described above. However, s_2_1 does not mate with s_1_2 and the files will be treated as two single-end files.

Apart from paired-end data, files with conventional filename suffixes that indicate that they belong to the same sample are consolidated. These suffixes include:

Adapter sequences
- "bbbbbb" followed by "" or at the end of the file name, where each "b" is "A", "C", "G", or "T"
Lane numbers
- "L###" followed by "" or at the end of the file name, where each "#" is a digit 0 to 9
Dates
- in the form "####-##-##" preceded or followed by a period or underscore
Set number
- of the form "_###" from the end

Navigating the file browser to transfer files to the server

The file browser is used to transfer files to the server so that these files can be added to a project for analysis. If you are importing a Bioproject from GEO/ENA or using URLs for data import, there is no need to transfer the files to the server.

To access the file browser and upload data to the server, use any of these options:

from the settings, go to Access management > Transfer files

Using the file browser to transfer files to the server:

Do not exit the browser tab or let the computer go to sleep or shut down until the transfer has completed

File size displayed in the table is binary format, not decimal format (e.g. GB displayed in the table is gigibyte not gigabyte. 1 gigibyte is 1,073,741,824 bytes. 1 gigabyte is 1,000,000,000 bytes. 1 gigibyte is 1.074 gigabytes).

Associate fastq files for multi-omic data

There are projects with more than one file type, such as single cell multi-omic assays that generate protein and RNA together. In these cases, files need to be associated with each other if starting in fastq format. If we start with processed data, there is no need to associate these files.

Define the type of data the file represents when importing files into the project.

If this step is skipped, the data type can be changed after import by right clicking the data node.

After importing both types of data, associate fastqs with the already imported data. (e.g. associate RNA fastqs with ATAC fastqs already imported into Partek Flow.

Additional Assistance

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

SFTP File Transfer Instructions

Introduction
SFTP with WinSCP
SFTP with FileZilla
SFTP command line usage
Points of Caution

Only use this method if encounter issues using Transfer file function on Flow homepage, contact support@partek.com to obtain private key.

Introduction

The following instructions detail the use of SFTP (Secured File Transfer Protocol) to transfer data to and from your Partek Flow instance. SFTP offers significant performance and security enhancements over FTP for file transfers. It also enables the use of robust file syncing utilities, e.g. RSYNC, and is compatible with common file transfer programs such as FileZilla and WinSCP.

To transfer files with SFTP, you will need to have your Partek Flow:

Server Name. Example: myname.partek.com
Username. Example: flowloginname
Private authentication key

This information should have been e-mailed to you from the Partek licensing team. If you lose this information, contact Partek support and we will resend your authentication key to you.

SFTP with WinSCP

WinSCP is an open source, free SFTP client for Windows. Its main function is file transfer between a local and a remote computer.

Downloading WinSCP

To download WinSCP, visit WinSCP's official site: https://winscp.net/eng/download.php On the WinSCP page you may need to scroll a bit down, to reach the green button Download WinSCP.

Connecting to your Partek server with WinSCP

Download and install WinSCP on your local computer and then launch the program.

On the Login page click on the New Site icon.

Type in the Host Name, which is the same as the web address that you use to access your instance of Partek Flow

The web address for your instance of Partek Flow has been sent to you by Partek's Licensing team. In this example, the web address is ilukic5i.partek.com.

Type in the User name, that has also been sent to you (and is the same user name that you use to log on to Partek Flow). In this example, the web address is lukici.

To proceed click on the Advanced... button
Then select the Authentication in the SSH section of the Advanced Site Settings dialog

Select the ... button (under Private key file) to browse for the id_rsa file.

The file has been sent to you by Partek's licensing team attached to the same email that gave you your URL and username.

If you do not see it in the Select private key file browser, switch to All Files (.)

Click Open

WinSCP will ask you to confirm file format conversion

Click OK.

WinSCP will create a file in .ppk format.

Click Save to save the converted key file, id_rsa.ppk, to a secure location on your local computer.
Click OK again to confirm the change.

Your private key has been saved in .ppk format and added to WinSCP

Click OK to proceed

Click Save to save the new WinSCP settings.

This will open the Save session as site dialog. You can accept the default name (in this example lukici@ilukic5i.partek.com) or add a custom name. The name that you specify here will appear in the left panel of the Login dialog.

Once you have made your edits, click OK.

On the Login page, select your newly created site (in this example: lukici@ilukic5i.partek.com) and click the Login button.

The first time you connect, a warning message will appear, asking you whether you want to connect to an unknown server.

Click Yes to proceed.

The progress towards establishing a connection will be displayed in a dialog. This process is automatic and you do not need to do anything.

The WinSCP interface includes is split into two panels. The panel on the left shows the directory structure of your local computer and the panel on the right shows the directory structure of your Partek Flow file server.

To transfer a file, just drag and drop the file from one panel to the other. The progress of your transfer will be shown on the screen.

SFTP with FileZilla

FileZilla is a graphical file transfer tool that runs on Windows, OSX, and Linux. It is great when needing to do bulk transfers as all transfers are added to a queue and processed in the background. It is possible to browse your files on the Partek Flow server while transfers are active. This is also the best solution when you are not on a computer with command line access or you are uncomfortable with command line operations.

Downloading FileZilla

We recommend downloading the FileZilla install packages from us. They are also available from download aggregator sites (e.g. CNET, download.com, sourceforge) but these sites have been known to bundle adware and other unwanted software products into the downloads they provide, so avoid them.

Mac OSX: http://packages.partek.com/bin/filezilla/fz-osx.app.tar.bz2
Windows 32-bit: http://packages.partek.com/bin/filezilla/fz-win32.exe
Windows 64-bit: http://packages.partek.com/bin/filezilla/fz-win64.exe
Linux (Please use your distribution's package manager to install Filezilla):
Ubuntu:

$ sudo apt-get update

$ sudo apt-get install filezilla

RedHat, see the following guide: http://juventusitprofessional.blogspot.com/2013/09/linux-install-filezilla-on-centos-or.html
OpenSuse, see: https://software.opensuse.org/package/filezilla

Connecting to your Partek server with FileZilla

After starting FileZilla, click on the Site Manager icon located at the top left corner of the FileZilla window.

Click on the New Site button on the left of the popup dialog.

Type in a name for the connection. Example: “Partek SFTP”.

The connection details to the right need to be changed to reflect the information you received via email. The default settings will NOT work.

Set Host: to your partek server name
Leave Port: blank
Change Protocol: to SFTP - SSH File Transfer Protocol
Change User: to your Partek Flow login name
Change Logon Type: to Key File and select the key file received via email.

When selecting your key file, change the file selection from its default of PPK files to All files. Otherwise you key file will not be visible in the file browser.

After selecting your key file, click the Connect button.

Click the checkbox to always trust this host and click OK. Once connected, you can begin to browse and transfer files. The files and folders to the left are on your computer, the ones on the right are on the Flow server.

SFTP command line usage

Importing your private authentication key

You will receive a file called id_rsa via email. Download this file, note where you downloaded it to, then use ssh-add to import the key. If you logout or reboot your computer, you will need to re-run the commands below. After key import, you will not be asked a password when transferring files to your Partek Flow server.

$ cd directory/with/key

$ chmod 600 id_rsa

$ eval $(ssh-agent)

$ ssh-add id_rsa

Copying files and folders between your Partek Flow server and local computer

RSYNC usage

RSYNC is useful when resuming a failed transfer. Instead of re-uploading or downloading what has already been transferred, RSYNC will copy only what it needs.

The command below will sync the folder "local_folder" with the "remote_folder" on Partek's servers. To transfer in the other direction, reverse the last two parameters.

$ rsync -avr --progress ./local_folder/ flowloginname@myname.partek.com:~/remote_folder/

With rsync, don't forget the trailing '/' on directory names.

Before moving the files, we strongly advise you to use FileZilla to explore the directory structure of the Partek server and then create a new directory to transfer the files to.

Points of Caution

When you delete files from the Partek Flow server they are gone and can not be recovered.
Please use Partek Flow to delete projects and results. Manually removing data using SFTP could break your server.
Wait until ALL input data for a particular project has been transferred to the Partek Flow server before importing data via Partek Flow. If you try to import samples while the upload is occurring the import job will crash.
When upload raw data to Partek hosted Flow server, we recommend to a create subfolder for each experiment at the same level of "FlowData" folder or inside "FlowData" folder.

Additional Assistance

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

Import single cell data

Import single cell data for different assay types and formats
Import single cell data from count matrix text file(s) using Full count matrix as the data format

Import single cell data for different assay types and formats

Select Single cell, choose the assay type (scRNA-Seq, Spatial transcriptomics, scATAC-Seq, V(D)J, Flow/Mass cytometry), and select the data format (Figure 1). Use the Next button to proceed with import.

Import single cell data from count matrix text file(s) using Full count matrix as the data format

Partek Flow supports single cell data analysis in count matrix text format using the Full count matrix data format (Figure 2). Each matrix text file is assumed to represent on sample, each value in the matrix represents expression value of a feature (e.g. a gene, or a transcript) in a cell. The expression value can be raw count, or normalized count. The requirement of the format of each text file should be the same as count matrix data.

Specify text file location, only one text file (in other words one sample) can be imported at once, preview of the file will be displayed, configuration of the file format is the same as Import count matrix data. In addition, you need to specify the details about this file.

Click Finish, the sample will be imported, on the data tab, number of cells in the sample will be displayed.

To import multiple samples, repeat the above steps by clicking Import data on the Metadata tab or within the task menu (toolbox) on the Analyses tab. Make the same previous selections using the cascading menu.

Additional Assistance

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

Importing 10x Genomics Matrix Files

Importing single cell data
- Importing matrices into Partek Flow (this Market Exchange Format is popular for public repositories)
- Importing matrices in h5 format (this Hierarchical Data Format is recommended for multiple samples)
Importing spatial data
- Importing Xenium Output Bundle

Importing single cell data

Partek Flow supports the import of filtered gene-barcode matrices generated by 10x Genomics' Cell Ranger pipeline.

Below is a video summarizing the import of these files:

Importing matrices into Partek Flow (this Market Exchange Format is popular for public repositories)

To import the matrices into Partek Flow, create a new project and click Add data then select Import scRNA count feature-barcode-mtx under Single cell > scRNA-Seq.

Samples can be added using the Add sample button. Each sample should be given a name and three files should be uploaded per sample using the Browse button.

If you have not already, transfer the files to the server to be accessed when you click Browse. Follow the directions here to add files to the server. Make sure the files are decompressed before they are uploaded to the server.

By default, the Cell Ranger pipeline output will have a folder called filtered_gene_bc_matrices (Figure 3). It is helpful to rename and organize the files prior to transfer using the File browser.

There are folders nested within the matrix folder, typically representing the reference genome it was aligned to. Navigate to the lowest subfolder, this should contain three files:

barcodes.tsv
genes.tsv
matrix.mtx

Select all 3 files for import into Partek Flow

Specify the annotation file used when running the pipeline for additional information such as mitochondrial counts (Figure 4). Other information can also be specified, such as the count value format. All features can be reported or features with non-zero values across all samples can be reported and the read count threshold can be modified to make the import more efficient.

Click Finish when you have completed configuration. This will queue the import task.

Importing matrices in h5 format (this Hierarchical Data Format is recommended for multiple samples)

The Cell Ranger pipeline can also generate the same filtered gene barcode matrix in h5 format. This gives you the ability to select just one file per matrix and select multiple matrices to import in batch. To import an h5 matrix, select the Import scRNA full count matrix or h5 option (Figure 1). Browse for the files and modify any configuration options. Remember the files need to be transferred to the server.

This feature is also useful for importing multiple samples in batch. Simply put all h5 files from your experiment on a single folder, navigate to the folder and select all the matrices you would like to import.

Configure all the relevant sample metadata, including sample name and the annotation that was used to generate the matrices, and click Finish when completed. Note that all matrices must have been generated using the same reference genome and annotation to be imported into the same project.

Importing spatial data

Importing Xenium Output Bundle

Raw output data generated by the 10x Genomics' Xenium Onboard Analysis pipeline consists of decoded transcript counts and morphology images. The raw output and other standard output files derived from them are compiled into a zipped file called Xenium Output Bundle.

To import the Xenium Output Bundle into Partek Flow, create a new project and click Add data, then select Import 10x Genomics Xenium under Single cell > Spatial, click Next.

Samples can be added using the Add sample button. Each sample should be given a name and a folder containing the required 6 files: cell_feature_matrix.h5, cells.csv.gz, cell_boundaries.csv.gz, nucleus_boundaries.csv.gz, transcripts.csv.gz, morphology_focus.ome.tif should be uploaded per sample using the Browse button. The required 6 files should be all included in the Xenium Output Bundle folder.

If you have not already, transfer the files to the server to be accessed when you click Browse. Follow the directions here to add files to the server. You will need to decompress the Xenium Output Bundle zip file before they are uploaded to the server. After decompression, you can drag and drop the entire folder into the Transfer files dialog, all individual files in the folder will be listed in the Transfer files dialog after drag & drop, with no folder structure. The folder structure will be restored after upload is completed.

Once you have uploaded the folder into the server, you can continue to select the folder for each sample from Browse. Once the folder is selected, the Cells and Features values will auto-populate. You can choose an annotation file that matches what was used to generate the feature count. Then, click Finish to start importing the data into your project.

Additional Assistance

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

Importing and Demultiplexing Illumina BCL Files

Primary sequencing output of an Illumina sequencer are per-cycle base call (bcl) files, which first need to be converted to fastq format, so that the data can be pushed to downstream applications. Partek Flow software comes with a conversion tool that can be used to import data in the bcl file format . In addition to the file conversion, this tool also demultiplexes the bcl files in the same step and outputs demultiplexed fastq files as the result.

We recommend you start by transferring the entire Illumina run folder to the Partek Flow server. To start a new project with bcl files, first select bcl under the Other import tab (Figure 1)

The resulting window shows the configuration dialog (Figure 2).

The bcl files hold the base calls and are in the Data directory within the whole Illumina run folder. Note that the Data directory file path needs to point to the directory, not to an individual bcl file.

The RunInfo.xml file is generated by the primary analysis software and contains information on the run, flow cell, instrument, time stamp, and the read structure (number of reads, number of cycles per read, whether a read is an index read). This file is typically stored at the top level in the Illumina run folder.

The SampleSheet.csv file provides the information on the relationship between the samples and indices specified during library creation. Although it has four sections, two sections (Settings and Data) are important for the data import and conversion. For more information on the files, consult Illumina documentation.

Selecting the Configure option under the Advanced options section enables a granular control of the import (Figure 3).

The Select tiles option (--tiles) enables the user to process only a subset of tiles available in the flow cell. The input for this option is a comma-separated list of regular expressions.

Min trimmed read length (--minimum-trimmed-read-length) specifies the minimum read length after adapter removal.

Mask short adapter reads (--mask-short-adapter-reads) applies when a read is trimmed below the length specified by Min trimmed read length. If the number of bases after adapter removal is less than Min trimmed read length, it forces the read length to be equal to Min trimmed read length by replacing the adapter bases that fall below the specified length by Ns. If the number of remaining bases falls below Mask short adapter sequences, then it replaces all the bases in a read with Ns.

Adapter stringency (--adapter-stringency) specifies the minimum match rate that triggers the masking or trimming of adapters. The rate is calculated as MatchCount / (MatchCount + MismatchCount). Only the reads exceeding the specified rate of sequence identity with adapters are trimmed.

Barcode mismatches (--barcode-mismatches) controls the number of allowed mismatches per index sequence.

Use bases mask (--use-bases-mask) defines a custom read structure that may be different to the structure specified in the RunInfo.xml file. The input for this option is a comma-separated list where Y and I are followed by a number indicating how many sequencing cycles to include in the fastq file. For example, if the option is set to Y26,I8,Y98, 26 cycles (26bp) will be used to generate the R1 sequence, 8 cycles (8bp) will be used for the sample index, and 98 cycles (98bp) will be used to generate the R2 sequence.

Do not split files by lane (--no-lane-splitting) prevents splitting of fastq files by lane, i.e. the converter will merge multiple lanes and generate one fastq file per sample.

Create fastq for index reads (--create-fastq-for-index-reads) creates an extra fastq file for each sample containing the sample index sequence for each read. This will be imported as an extra sample into the project.

Ignore missing bcls (--ignore-missing-bcl) will interpret missing base call files as N.

Ignore missing filter (--ignore-missing-filter) will ignore missing filter files and assume all clusters pass the filter.

Ignore missing positions (--ignore-missing-positions) will write new, unique coordinates into the header line if the cluster location files are missing.

Ignore missing controls (--ignore-missing-control) will interpret missing control files as missing not-set control bits.

Save undetermined fastq will take the reads that could not be assigned to a sample index and collect them into an Undetermined_S0.fastq file, which will be imported as a new sample.

The result of the import is an Unaligned reads data node, containing demultiplexed fastq files.

Additional Assistance

Partek Flow Uploader for Ion Torrent

The Partek Flow Uploader is a Torrent Browser plugin that lets users upload run results to Partek Flow for further analysis.

Quick Video Tutorial on running the Plugin

The clip above (video only, no audio) shows the Partek Flow Uploader plugin in action.

Downloading the Plugin

Download the Partek® Flow® Uploader from the links below:

This is a compressed zipped file. Do not unzip.

Installation of the Plugin

Installation only needs to be performed once per Torrent Browser. All users of the same instance of Torrent Browser will be able to use the plugin. For future versions of the plugin, the steps below can also be used for updating.

To install the plugin, first log into Torrent Browser (Figure 1).

Navigate to Plugins under dropdown menu in upper-right corner under the gear icon (Figure 2).

Click the Install or Upgrade Plugin button (Figure 3).

Verify that the Partek Flow Uploader is listed and that the Enabled checkbox is selected (Figure 5).

From the plugins table, click the (Manage) gear icon for the Partek Flow Uploader and select Configure in the drop-down menu (Figure 6).

Global Partek Flow configuration settings can be entered into the plugin. When set, it will serve as the default for all users of the Torrent Browser. If multiple Partek Flow users are expected to run the plugin, it is recommended to leave the username and password fields blank so that individual users can enter them as needed.

In the configuration dialog (Figure 7), enter the Partek Flow URL, your username and your password. Clicking on Check configuration would verify your credentials and indicate if a valid username and password has been entered. Click Save when done.

Click the Rescan Plugins for Changes button. Rescanning the plugins will finish the installation and save the configuration.

Adding the Plugin to your Run Plan

In the Torrent Browser, you can configure a Run Plan to include the Partek Flow Uploader. You can create a new Run Plan (from a Sample or a Template) or edit an existing Run Plan. In the example in Figure 8, the Partek Flow Uploader will be included in an existing Run Plan. From the Planned Runs page, click the gear icon in the last column, and choose Edit.

In the Edit Plan page, go to the Plugins tab (Figure 9) and select the checkbox next to the PartekFlowUploader.

Click the Configure hyperlink next to the PartekFlowUploader (Figure 10). If necessary, enter the Partek Flow URL, your username and your password. These are the same credentials you use to access Partek Flow directly on a web browser. Note that some fields may already be pre-populated depending on the global plugin configuration, you can edit the entries as needed. All fields are required to successfully run the plugin.

The Project Name field will be used in Partek Flow to create a new project where the run results will be exported. However, if a project with that name already exists, the samples will be added to that existing project. This enables you to combine multiple runs into one project. Project Names are limited to 30 characters. If not specified, the plugin will use the Run Name as the Project Name. Click the Check configuration button to see if you typed a valid username and password. When ready, click Save Changes to proceed.

Proceed with your Run Plan. The plugin will wait for the base calling to be finished before exporting the data to Partek Flow.

Once the Run Plan is executed, data will be automatically exported to the Partek Flow Server. In the Run Report, go to the Plugin Summary tab and the plugin status will be displayed. An example of a successful Plugin upload is shown in Figure 11.

To access the project, click on the Partek Flow hyperlink in the plugin results (Figure 11). You can also go directly to Partek Flow in a new browser window and access your account. In your Partek Flow homepage (Figure 12), you will now see the project created by the Partek Flow Uploader.

Running the Plugin from a Report

You can manually invoke the plugin from a completed run report. This allows you to export the data from the Torrent Server if you did not include the plugin in the original run plan. This also gives you the flexibility to export the same run results onto different project(s). Open the run report and scroll down to the bottom of the page (Figure 13). In the Plugin Summary tab, click the Select Plugins to Run button.

From the plugin list (Figure 14), select the PartekFlowUploader plugin.

Configure the Partek Flow Uploader. Enter the Partek Flow URL, your username and your password (Figure 15). These are the same credentials you use to access Partek Flow directly on a web browser. Although some fields may already be pre-populated depending on the global plugin configuration, you can edit the entries as needed. All fields are required to successfully run the plugin.

When ready, click Export to Partek Flow to proceed. If you wish to cancel, click on the X on the lower right of the dialog box.

Note that configuring the Plugin from a report (Figure 15) is very similar to configuring it as part of a Run Plan (Figure 10) with two notable differences:

The Check configuration has been replaced by Export to Partek Flow button, which when clicked, immediately proceeds to the export.
The Save changes button has been removed so any change in the configuration cannot be saved (compared to editing a run plan where plugin settings are saved)

Once the plugin starts running, it will indicate that it is Queued on the upper right corner of the Plugins Summary (Figure 16). There will also be a blue Stop button to cancel the operation.

Click the Refresh plugin status button to update. The plugin status will show Completed once the export is done and the data is available in Partek Flow (Figure 11).

Sample table created by the Plugin

The Partek Flow Uploader plugin sends the unaligned bam files to the Partek Flow server. For each file, a Sample of the same name will be created in the Data tab (Figure 17).

The data transferred by the Partek Flow Uploader is stored in a directory created for the Project within the user's default project output directory. For example, in Figure 17, the data for this project is stored in: /home/flow/FlowData/jsmith/Project_CHP Hotspot Panel.

Conversion of UBAM to FASTQ files

The plugin transfers the Unaligned BAM data from the Torrent Browser. The UBAM file format retains all the information of the Ion Torrent Sequencer. In the Partek Flow Project, the Analyses tab would show a circular data node named Unaligned bam. Click on the data node and the context-sensitive task menu will appear on the right (Figure 18).

Unaligned BAM files are only compatible with the TMAP aligner, which can be selected in the Aligners section of the Task Menu. If you wish to use other aligners, you can convert the unaligned BAM files to FASTQ using the Convert to FASTQ task under Pre-alignment tools. Some information specific to Ion Torrent Data (such as Flow Order) are not retained in the FASTQ format. However, those are only relevant to Ion Torrent developed tools (such as the Torrent Variant Caller) and are not relevant to any other analysis tools.

Once converted, the reads can then be aligned using a variety of aligners compatible with FASTQ input (Figure 19). You can also perform other tasks such as Pre-alignment QAQC or run an existing pipeline. Another option is to include the Convert to FASTQ task in your pipeline and you can invoke the pipeline directly from an Unaligned bam data node.

Additional Assistance

Importing 10x Genomics .bcl Files

Partek Flow supports .bcl files based on 10x Genomics library preparation. The following document will guide you through the steps.

To start the import, create a new project and then select Import Data > Import bcl files. The Import bcl dialog will come up (Figure 1).

Use the Data directory option to point to the location of the directory holding the data. It is located at the top level of the run directory and is typically labeled Data. Please see the tool tip for more info.

Use the Run info file option to point to the RunInfo.xml file. It is located at the top level of the run directory.

Use the Sample sheet file to point to the sample sheet file, which is usually a .csv file. Partek Flow can accept 10X Genomics' "simple" and Illumina Experiment Manager (IEM) sample sheet format, which utilize 10X Genomics' sample index set codes. Each index set code corresponds to a mixture of four sample index sequences per sample. Alternatively, Partek Flow will also accept a sample sheet file that has been correctly formatted using the sample sheet generator provided by 10X Genomics.

The click on the Configure link and make the following changes (Figure 2).

Min trimmed read length: 8
Mask short adapter reads: 8
Use bases mask: see below
Create fastq for index reads: OFF
Ignore missing bcls: ON
Ignore missing filter: ON
Ignore missing positions: ON
Ignore missing controls: ON

For the Use bases mask option, the read structure for Chromium Single cell 3' v2 prep kit is typically Y26,I8,Y98. The settings for Chromium Single cell 3' v3/v3.1 is typically Y28,I8,Y91. Please check the read structure detailed in the RunInfo.xml file and adjust the values to match your data.

Click Apply to accept and then Finish to import your files.

Additional Assistance

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

Import a GEO / ENA project

How to import a study from GEO / ENA

If a project is publicly available in the Gene Expression Omnibus (GEO) and European Nucleotide Archive (ENA) databases, you can import associated FASTQ files and sample attributes automatically into Partek Flow.

On the Homepage click New Project to create a project and give the project a name

Click Add data

Select fastq as the file type after choosing between Single cell or Bulk as the assay types

Click Next
Choose GEO / ENA
Enter the BioProject ID of the data set you would like to download. The format of a BioProject ID is PRJNA followed by one to six numbers (e.g. PRJNA381606)

A GEO ID can also be used in the format GSE followed by one to five numbers (e.g. GSE71578).

Click Finish

It may take a while for the download to complete depending on the size of the data. FASTQ files are downloaded from the ENA BioProject page.

FASTQ files will be added as an Unaligned reads data node in the Analyses tab

Common Issues

Error Message - The project did not yield any data. Double-check the project ID, or try importing the data manually

If the study is not publicly available in both GEO and ENA, project import will not succeed.

The project was imported, but the Analyses tab is empty and there are no FASTQ files

If there is an ENA project, but the FASTQ files are not available through ENA, the project will be created, but data will not be imported.

Something is missing or the import failed

A variety of other issues and irregularities can cause imports to not succeed or partially succeed, including, but not limited to, a BioProject having multiple associated GSE IDs, incomplete information on the GEO or ENA page, and either the GEO or ENA project not being publicly available.

FAQ

What are GEO and ENA?

The Gene Expression Omnibus (GEO) and the European Nucleotide Archive (ENA) are web-accessible public repositories for genomic data and experiments. Access and learn more about their resources at their respective websites:

How do I know if a GEO project is also in ENA?

You can search ENA using the GEO ID (e.g., GSE71578) to check if there is a matching ENA project.

Open the Study result to view the BioProject ID (e.g., PRJNA381606) and a table with information about the samples and files included in the project

Additional Assistance

Task Menu

The Task Menu lists all the tasks that can be performed on a specific node. It can be invoked from either a Data or Task node and appears on the right hand side of the Analyses tab. It is context-sensitive, meaning that it will only present tasks that the user can perform on the selected node. For example, selecting an Aligned reads data node will not present aligners as options.

Clicking a Data node presents a variety of tasks:

Clicking a Task node gives you the option to view the Task results or perform Task actions such as rerunning the task (Figure 1).

Additional Assistance

Task actions

Left single clicking on any task (the rectangles) in the analysis pipeline will cause a Task Actions section to appear in the pop-up menu. This allows users to:

Rerun tasks: rerun the selected task, the task dialog will pop-up and users can change parameters of the task. Previous downstream analysis of the selected task will not be rerun.
Rerun with downstream tasks: rerun the selected task, the task dialog will pop-up, users can change the parameters of the current task and the downstream analysis will be rerun with the same configuration as the previous one.
Edit description: the description of the task can be replaced by manually typing in string.
Change color: choose a color to apply only on the selected task by clicking on Apply. Click Apply to downstream to change the selected task and the downstream pipeline color to the newly selected color.
Delete task: this option is only available if the user is the owner of the project or the owner of the task. When a task is deleted, all downstream tasks, including tasks from other users, will be deleted. Users may check the box to choose to delete the task's output files. If delete output files is not checked, the task will be removed from the pipeline, but the output files of the task will remain on the disk.
Restart task: this option is only available on failed tasks and requires an admin role to perform, but does not require that you have a user account. Since you are logged in as an admin, restarting a task will not take up a concurrent seat and the disk space consumed by the output files will count towards the original owner of the task's storage space.

Additional Assistance

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

Data summary report

The Data summary report in Partek Flow provides an overview of all tasks performed as part of a pipeline. This is particularly useful for report writing, record keeping and revisiting projects after a long period of time.

This user guide will cover the following topics:

Viewing the Data Summary Report

Click on an output data node under the Analyses tab of a project and choose Data summary report from the context sensitive menu on the right (Figure 1). The report will include details from all of the tasks upstream of the selected the node. If tasks have been performed downstream of the selected data node, they will not be included in the report.

Saving the Data Summary Report

The Data summary report can be saved in different formats via the web browser. The instructions below are for Google Chrome. If you are using a different browser, consult your browser's help for equivalent instructions.

Save as a PDF

On the Data summary report, expand all sections and show all task details. Right-click anywhere on the page and choose Print... from the menu (Figure 4) or use Ctrl+P (Command+P on Mac). In the print dialog, click Change… (Figure 5) and set the destination to Save as PDF. Select the Background graphics checkbox (optional), click the blue Save button (Figure 5) and choose a file location on your local machine.

The PDF can be attached to an email and/or opened in a PDF viewer of your choice.

Save as HTML

On the Data summary report, right-click anywhere on the page and choose Save as… from the menu (Figure 6) or use Ctrl+S (Command+S on Mac). Choose a file location on your local machine and set the file type to Web Page, Complete.

The HTML file can be opened in a browser of your choice.

Quick Video Demo of the Data Summary Report

The short video clip below (with audio) shows a tutorial of looking at the Data Summary Report

Additional Assistance

QA/QC

Partek Flow contains a number of quality control tools and reports that can be used to evaluate the current status of your analysis and decide downstream steps. Quality control tools are organized under the Quality Assurance / Quality Control (QA/QC) section of the context-sensitive menu and are available for unaligned and aligned reads data nodes.

This section will illustrate:

In addition to the tools listed above, many other functionalities can also be interpreted in sense of quality control. For instance, principal components analysis, hierarchical clustering (on sample level), variant detection report, and quantification report.

Additional Assistance

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

Pre-alignment QA/QC

Selecting a node with unaligned reads (either Unaligned reads or Trimmed reads) shows the QA/QC section in the context sensitive menu, with two options (Figure 1). To assess the quality of your raw reads, use Pre-alignment QA/QC.

Pre-alignment QA/QC setup dialog is given in Figure 2. Examine reads allows you to control the number of reads processed by the tool; All reads, or a subset (One of every n reads). The latter option is obviously not as thorough, but is much faster than All reads.

If selected, K-mer length creates a per-sample report with the position of the most frequent k-mers (i.e. sequences of k nucleotides) of the length specified in the dialog. The range of input values is from one to 10.

The last control refers to .fastq files. Partek® Flow® can automatically detect the quality encoding scheme (Auto detect) or you can use one of the options available in the drop-down list. However, the auto-detection is only applicable for Phred+33 and Phred+64 type of quality encoding score. For early version of Solexa quality encoding score, select Solexa+64 from the Quality encoding drop down list. For a paired-end data, the pre-alignment QA/QC will be done on each read in pair separately and the results will be shown separately as well.

Most sequencing applications now use the phred quality score. This score indicates the probability that the base was accurately identified. The table below shows the corresponding base call accuracies for each score:

The task report is organised in two tiers. The initial view shows project-level report with all the samples. An overview table is at the top, while matching plots are below.

Two project-level plots are Average base quality per position and Average base quality score per read (Figure 4). The latter plot presents the proportion of reads (y-axis) with certain average quality score (meaning all the base qualities within a read are averaged; x-axis). Mouse over a data point to get the matching readouts. The Save icon saves the plot in a .svg format to the local machine. Each line on the plot represents a data file and you can select the sample names from the legend to hide/un-hide individual lines.

A sample-level report begins with a header, which is a collection of typical quality metrics (Figure 5).

Below the header you will find four plots: Base composition, Average base quality score per position (same as above, but on the sample level), Distribution of base quality scores (the same as Average base quality score per read, but on the sample level), and Distribution of read lengths.

Distribution of read lengths shows a single column for fixed length data (e.g. Illumina sequencing). However, for quality-trimmed data or non-fixed length data (like Ion Torrent sequencing), expect to see a read’s length distribution (Figure 7).

If K-mer length option was turned on when setting up the task, an additional plot will be added to the sample-level report, i.e. K-mer Content (Figure 8). For each position, K-mer composition is given, but only the top six most frequent K-mers are reported; high frequency of a K-mer at a given site (enrichment) indicates a possible presence of sequencing adapters in the short reads.

The pre-alignment QA/QC report as described above is generally available for the NGS data of fastq format. For other types of data, the report may differ depending on the availability of information. For example, for fasta format, there is no base quality score information and therefore all the figures or graphs related to base or read quality score will be unavailable.

Additional Assistance

Post-alignment QA/QC

Post-alignment QA/QC is available for data nodes containing aligned reads (Aligned reads) and has no special control dialog. Similar to the pre-alignment QA/QC report, the post-alignment contains two tiers, i.e. project-level report and sample-level report.

The project-level report starts with a summary table (Figure 1). Unlike pre-alignment QA/QC report, each row now corresponds to a sample (sample names are hyperlinks to sample-level report). Table allows for a quick comparison across all the samples within the project. Any outlying sample can, therefore, easily be spotted.

Note that the summary table reflects the underlying chemistry. While Figure 1 shows a summary table for single-end sequencing, an example table for paired-end sequencing is given in Figure 2. Common features are discussed first.

The first two columns contain total number of reads (Total reads) and total number of alignments (Total alignments). Theoretically, for single-end chemistry, total number of reads equals total number of alignments. For double-end reads, theoretical result is to have twice as many alignments as reads (the term “read” refers to the fragment being sequenced, and since each fragment is sequenced from two directions, one can expect to get two alignments per fragment). When counting the actual number of alignments (Total alignments), however, reads that align more than once (multimappers) are also taken into account. Next, the Aligned column contains the fraction of all the reads that were aligned to the reference assembly.

The Coverage column shows the fraction (%) of the reference assembly that was sequenced and the average sequencing coverage (×) of the covered regions is in the Avg. coverage depth column. The Avg. quality is mapping quality, as reported by the aligner (not all aligners support this metric). Avg. length is the average read length and average read quality is given in Avg. quality column. Finally, %GC is the fraction of G or C calls.

In addition, the Post-alignment QA/QC report for single-end reads (Figure 1) contains the Unique column. This refers to the fraction of uniquely aligned reads.

On the other hand, the Post-alignment QA/QC report for paired-end reads (Figure 2) contains these columns:

Unique singleton
- fraction of alignments corresponding to the reads where only one of the paired reads can be uniquely aligned
Unique paired
- fraction of alignments corresponding to the reads where both of the paired reads can be uniquely aligned
Non-unique singleton
- fraction of singletons that align to multiple locations
Non-unique paired
- fraction of paired reads that align to multiple locations

Note: for paired-end reads, if one end is aligned, the mate is not aligned, the alignment rate calculating will include the read as the numerator, also since the mate is not aligned, we will also include this read in the unaligned data node (if the generate unaligned reads data node option is selected) for 2nd stage alignment, this will generate discrepancy between total reads and "unaligned reads + total reads * alignment rate", because reads with only one mate aligned are counted twice.

In addition to the summary table, several graphs are plotted to give a comparison across multiple samples in the project. Those graphs are Alignment breakdown, Coverage, Genomic Coverage, Average base quality per position, Average base quality score per read, and Average alignments per read. Two of those (Average base quality plots) have already been described.

The alignment breakdown chart (Figure 3) presents each sample as a column, and has two vertical axes (i.e. Alignment percent and Total reads). The percentage of reads with different alignment outcomes (Unique paired, Unique singleton, Non-unique, Unaligned) is represented by the left-side y-axis and visualized by stacked columns. The total number of reads in each sample is given using the black line and shown on the right-side y-axis.

The Coverage plot (Figure 4) shows the Average read depth (in covered regions) for each sample using columns and can be red off the left-hand y-axis. Similarly, the Genomic coverage plot shows genome coverage in each sample, expressed as a fraction of the genome.

The last graph is Average alignments per read (Figure 5) and shows the average number of alignments for each read, with samples as columns. For single-end data, the expected average alignments per read is one, while for paired-end data, the expected average alignments per read is two.

Additional Assistance

GSA

GSA stands for gene specific analysis, the goal of which is to identify the statistical model that is the best for a specific gene among all the selected models, and then use that best model to calculate p-value and fold change.

GSA dialog

The first step of GSA is to choose which attributes to include in the test (Figure 1). All sample attributes including numeric and categorical attributes are displayed in the dialog, so use the check button to select between them. An experiment with two attributes Cell type (with groups A and B)and Time (time points 0, 5, 10) is used as an example in this section.

Click Next to display the levels of each attribute to be selected for sub-group comparisons (contrasts).

To compare A vs. B, select A for Cell type on the top, B for Cell type on the bottom and click Add comparison. The specified comparison is added to the table below (Figure 2).

To compare Time point 5 vs. 0, select 5 for Time on the top, 0 for Time on the bottom, and click Add comparison (Figure 3).

To compare cell types at a certain time point, e.g. time point 5, select A and 5 on the top, and B and 5 on the bottom. Thereafter click Add comparison (Figure 4).

Multiple comparisons can be computed in one GSA run; Figure 5 shows the above three comparisons are added in the computation.

In terms of design pool, i.e. choices of model designs to select from, two 2 factors in this example data will lead to seven possibilities in the design pool:

Cell type
Time
Cell type, Time
Cell type, Cell type * Time
Time, Cell type * Time
Cell type * Time
Cell type, Time, Cell type * Time

In GSA, if a 2nd order interaction term is present in the design, then all first order terms must be present, which means, if Cell type * Time interaction is present, the two factors must be included in the model. In the other words, the following designs are not considered:

Cell type, Cell type * Time
Time, Cell type * Time
Cell type * Time

If a comparison is added, some models that don't have the comparison factors will also be eliminated. E.g. if a comparison on Cell type A vs. B is added, only designs that have Cell type factor included will be in the computation. These are:

Cell type
Cell type, Time
Cell type, Time, Cell type * Time

The more comparisons on different terms are added, the fewer models will be included in the computation. If the following comparisons are added in one GSA run:

A vs B (Cell type)
5 vs 0 (Time)

only the following two models will be computed:

Cell type, Time
Cell type, Time, Cell type * Time

If comparisons on all the three terms are added in one GSA run:

A vs B (Cell type)
5 vs 0 (Time)
A*5 vs B*5 (Cell type * Time)

then only one model will be computed:

Cell type, Time, Cell type * Time

If GSA is invoked from a quantification output data node directly, you will have the option to use the default normalization methods before performing differential expression detection (Figure 6).

If invoked from a Partek E/M method output, the data node contains raw read counts and the default normalization is:
- Normalize to total count (RPM)
- Add 0.0001 (offset)
If invoked from a Cufflinks method output, the data node contains FPKM and the default normalization is:
- Add 0.0001 (offset)

If advanced normalization needs to be applied, perform the Normalize counts task on a quantification data node before doing differential expression detection (GSA or ANOVA).

GSA advanced options

Click on Configure to customize Advanced options (Figure 7).

Low-expression feature

Low -expression feature section allows you to specify criteria to exclude features that do not meet requirements for the calculation. If there is filter feature task performed in the upstream analysis, the default of this filter is set to "None", otherwise, the default is Lowest average coverage is set to 1.

Lowest average coverage: the computation will exclude a feature if its geometric mean across all samples is below than the specified value
Lowest maximum coverage: the computation will exclude a feature if its maximum across all samples is below the specified value
Minimum coverage: the computation will exclude a feature if its sum across all samples is below than the specified value
None: include all features in the computation

Multiple test correction

Multiple test correction can be performed on the p-values of each comparison, with FDR step-up being the default (1). Other options like Storey q-value (2), and Bonferroni are provided, select one method at a time; None means no multiple test correct will be performed.

FDR step-up:

Suppose there are n p-values (n is the number of features). The p-values are sorted by ascending order and m represents the rank of a p-value. The calculation compares p-value*(n/m) with the specified alpha level, and the cut-off p-value is the one that generates the last product that is less than the alpha level. The goal of step up method is to find:

Define the step-up value as:

Then an equivalent definition for K* is :

So when

the step up value is

In order to find K* , start with Sn* and then go up the list until you find the first step up value that is less or equal to alpha.

Storey q-value:

q-value is the minimum "positive false discovery rate" (pFDR) that can occur when rejecting a statistic.

For an observed statistic T=t and nested set of rejection area {C},

Bonferroni:

Report option

This section configures how to select the best model for a feature. There are two options for Model selection criterion: AICc (Akaike Information Criterion corrected) and AIC (Akaike Information Criterion). AICc is recommended for small sample size, while AIC is recommended for medium and large sample size What about large samples?(3). Note that when sample size grows from small to medium, AICc converges to AIC. Taking the AICc/AIC value into account, GSA considers the model with the lowest information criterion as the best choice.

In the results, the best model's Akaike weight is also generated. The model's weight is interpreted as the probability that the model would be picked as the best if the study were reproduced. The range of Akaike weight is between 0 to 1, where 1 means the best model is very superior to the other candidates from the model pool; if the best model's Akaike weight is close to 0.5 on the other hand, it means the best model is likely to be replaced by other candidates if the study were reproduced. One still uses the best shot model, however, the accuracy of the best shot is fairly low.

The default value for Enable multimodel approach is Yes. It means that the estimation will utilize all models in the pool by assigning weights to them based on AIC or AICc. If No is selected instead, the estimation is based on only one best model which has the smallest AIC or AICc. The output p-value will be different depending on the selected option for multimodel, but the fold change is the same. Multimodel approach is recommended when the best model's Akaike weight is not close to 1, meaning that the best model is not compelling.

There are situations when a model estimation procedure does not outright fail, but still encounters some difficulties. In this case, it can even generate p-value and fold change for the comparisons, but those values are not reliable, and can be misleading. It is recommended to use only reliable estimation results, so the default option for Use only reliable estimation results is set Yes.

Model types configuration

Partek Flow provides five response distribution types for each design model in the pool, namely:

Normal
Lognormal (the same as ANOVA task)
Lognormal with shrinkage (the same as limma-trend method 4)
Negative binomial
Poisson

We recommend to use lognormal with shrinkage distribution (the default), and an experienced user may want to click on Custom to configure the model type and p-value type (Figure 8).

If multiple distribution types are selected, then the number of total models that is evaluated for each feature is the product of the number of design models and the number of distribution types. In the above example, suppose we have only compared A vs B in Cell type as in Figure 2, then the design model pool will have the following three models:

Cell type
Cell type, Time
Cell type, Time, Cell type * Time

If we select Lognormal with shrinkage and Negative binomial, i.e. two distribution types, the best model fit for each feature will be selected from 3 * 2 = 6 models using AIC or AICc.

The design pool can also be restricted by Min error degrees of freedom. When "Model types configuration" is set to Default , this is automated as follows: it is desirable to keep the error degrees of freedom at or above six. Therefore, we automatically set to the largest k, 0 <= k <=6 for which admissible models exist. Admissible model is one that can be estimated given the specified contrasts. In the above example, when we compare A vs B in Cell type, there are three possible design models. The error degree of freedom of model Cell type is largest and the error degree of freedom of model Cell type, Time, Cell type * Time is the smallest:

k(Cell type) > k(Cell type, Time) > k (Cell type, Time, Cell type*Time)

If the sample size is big, k >=6 in all three models, all the models will be evaluated and the best model will be selected for each feature. However, if the sample size is too small, none of the models will have k >=6, then only the model with maximal k will be used in the calculation. If the maximal k happens to be zero, we are forced to use Poisson response distribution only.

There are two types of p-value, F and Wald., Poisson, negative binomial and normal models can generate p-value using either Wald or F statistics. Lognormal models always employ the F statistics; the more replicates in the study, the less the difference between the two options. When there are no replicates, only Poisson can be used to generate p-value using Wald.

Note: Partek Flow keeps tracking the log status of the data, and no matter whether GSA is performed on logged data or not, the LSMeans, ratio and fold change calculation are always in linear scale. Ratio is the ratio of the two LSMeans from the two groups in the comparison (left is the numerator, right is the denominator); Fold change is converted from ratio: when ratio is greater than 1, fold change is same as ratio; when ratio is less than one, fold change is -1/ratio. In other words - fold change value is always >=1 or <=-1, there is no fold change value between -1 and 1. When the LSmean of numerator group is greater than that of denominator group, fold change is greater than 1; when LSmean of numerator group is less than denominator group, fold change is less than 1; when the group groups are the same, fold change is 1. Logratio is ratio is log2 transformed, which is equivalent to logfoldchange is some other software.

GSA report

If there are multiple design models and multiple distribution types included in the calculation, the fraction of genes using each model and type will be displayed as pie charts in the task result (Figure 9).

Feature list with p-value and fold change generated from the best model selected is displayed in a table with other statistical information (Figure 10). By default, the gene list table is sorted by the first p-value column.

The following information is included in the table by default:

Feature ID information: if transcript level analysis was performed, and the annotation file has both transcript and gene level information, both gene ID and transcript ID are displayed. Otherwise, the table shows only the available information.
Total counts: total number of read counts across all the observations from the input data.
Each contrast outputs p-value, FDR step up p-value, ratio and fold change in linear scale, LSmean of each group comparison in linear scale

When you click on the Optional columns link on the top-left corner of the table, extra information will be displayed in the table when select:

Maximum count: maximum number of reads counts across all the observations from the input data.
Geometric mean: geometric mean value of the input counts across all observations.
Arithmetic mean: arithmetic mean value of input counts across all observations.

By clicking on Optional columns, you can retrieve more statistics result information, e.g. Average coverage which is the geometric mean of normalized reads in linear scale across all the samples; fold change lower/upper limits generated from 95% confidence interval; feature annotation information if there are any more annotation fields in the annotation model you specified for quantification, like genomic location, strand information etc.

Select the check box of the field and specify the cutoff by typing directly or using the slider. Press Enter to apply. After the filter has been applied, the total number of included features will be updated on the top of the panel (Result).

Note that for the LSMean, there are two columns corresponding to the two factors in the contrast. The cutoffs can be specified for the left and right columns separately. For example, in Figure 6, the LSMean (left) corresponds to A while the LSMean(right) is for the B.

The filtered result can be saved into a filtered data node by selecting the Generate list button at the lower-left corner of the table. Selecting the Download button at the lower-right corner of the table downloads the table as a text file to the local computer.

References

Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing, JRSS, B, 57, 289-300.
Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035.
Auer, 2011, A two-stage Poisson model for testing RNA-Seq
Burnham, Anderson, 2010, Model selection and multimodel inference
Law C, Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 2014 15:R29.
Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biology, 2010

Additional Assistance

Survival Analysis with Cox regression and Kaplan-Meier analysis - Partek Flow

Introducing Survival Analysis
Cox Regression
- Configuring the Cox Regression Dialogue
Kaplan-Meier Survival Curve
- Getting started with the Kaplan-Meier task
- Choosing stratification factors
Troubleshooting
References

Introducing Survival Analysis

Survival analysis is a branch of statistics that deals with modeling of time-to-event. In the context of “survival,” the most common event studied is death; however, any other important biological event could be analyzed in a similar fashion (e.g., spreading of the primary tumor or occurrence/relapse of disease). Survival analysis tries to answer questions such as: What is the proportion of a population who will survive past a certain time (i.e., what is the 5-year survival rate)? What is the rate at which the event occurs? Do particular characteristics have an impact on survival rates (e.g., are certain genes associated with survival)? Is the 5-year survival rate improved in patients treated by a new drug? Cox regression and Kaplan-Meier analysis are two techniques which are commonly used to assess survival analysis.

In survival analysis, the event should be well-defined with two levels and occur at a specific time. Because the primary outcome of the event is typically unfavorable (e.g., death, metastasis, relapse, etc.), the event is called a “hazard.” The hazard ratio is used to assess the likelihood of the event occurring while controlling for other co-predictors (co-variables/co-factors) if added to the model. In other words, the hazard ratio is how rapidly an event is experienced by comparing the hazard between groups. A hazard ratio greater than 1 indicates a shorter time-to-event (increase in the hazard), a hazard ratio less than 1 is associated with a greater time-to-event (reduction in the hazard), and a hazard ratio of 1 indicates no effect on time-to-event. For example, if the hazard ratio is 2 then there is twice a chance of occurrence compared to the other group. In cancer studies, a hazard ratio greater than 1 is considered a bad prognostic factor while a hazard ratio less than 1 is a good prognostic factor. Click here for more information on hazard ratios.

An important aspect of survival analysis is “censored” data. Censored data refers to subjects that have not experienced the event being studied. For example, medical studies often focus on survival of patients after treatment so the survival times are recorded during the study period. At the end of the study period, some patients are dead, some patients are alive, and the status of some patients is unknown because they dropped out of the study. Censored data refers to the latter two groups. The patients who survived until the end of the study or those who dropped out of the study have not experienced the study event "death" and are listed as "censored".

Cox Regression

Cox regression (Cox proportional-hazards model) tests the effects of factors (predictors) on survival time. Predictors that lower the probability of survival at a given time are called risk factors; predictors that increase the probability of survival at a given time are called protective factors. The Cox proportional-hazards model are similar to a multiple logistic regression that considers time-to-event rather than simply whether an event occurred or not. Cox regression should not be used for a small sample size because the events could accidently concentrate into one of the cohorts which will not produce meaningful results. Click here to read more about hazard ratio estimation in small samples.

Configuring the Cox Regression Dialogue

Open the Cox Regression task in the task menu under Statistics for any counts node.
Next, select the Time, Event, and Event status. Partek Flow will automatically guess factors that might be appropriate for these options. Click Next to proceed with the task.

<figure><img src="../../../.gitbook/assets/image (2) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1).png" alt=""><figcaption><p>Figure 1. Cox regression dialog</p></figcaption></figure>

The predictors (factors or variables) and co-predictors in the model must be defined. Co-predictors are numeric or categorical factors that will be included in the cox regression model. Time-to-event will be performed on features (e.g. genes) by default unless Use feature expression as predictor is unchecked (Figure 2). If unchecked, select a factor and Add factors that is not features to model a different variable. Using the default setting, Use feature expression as predictor, lets the user Add factors to the model that act to explain the relationship for time-to-event (co-predictor) in addition to features. Choose Add interaction to add co-predictors with known dependencies. If factors are added here, they cannot be added as stratification factors. Click Next to proceed with the task.

<figure><img src="../../../.gitbook/assets/image (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1).png" alt=""><figcaption><p>Figure 2. Select predictor and co-predictors</p></figcaption></figure>

Next, the user can define comparisons for the co-predictors if they have been added. Configure contrasts by moving factors into the numerator (e.g. experimental factor) or denominator (e.g. control factor / reference), choose Combine or Pairwise, and add the comparison which will be displayed below. Combine all numerator levels and combine all denominator levels in a single comparison or choose Pairwise to split all numerator levels and split all denominator levels into a factorial set of comparisons meaning every numerator will be paired with every denominator. Multiple comparisons from different factors can be added with Add comparison. Low value filter can be used to filter by excluding features; choose a filter or select none. Click Next to proceed with the task (Figure 3).

<figure><img src="../../../.gitbook/assets/image (2) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1).png" alt=""><figcaption><p>Figure 3. Add comparisons</p></figcaption></figure>

The user can select categorical factors to perform stratification if needed. Stratification is needed because the proportional odds assumption holds only within each stratum, but not across the strata. When stratification factors are included, the proportional hazard assumption will hold for each combination of levels of stratification factor; a separate submodel is estimated for each level combination and the results are aggregated. Click Finish to complete the task (Figure 4).

<figure><img src="../../../.gitbook/assets/image (3) (1) (1) (1) (1) (1) (1) (1) (1) (1) (1).png" alt=""><figcaption><p>Figure 4. Select stratification factors</p></figcaption></figure>

The results of Cox regression analysis provide key information to interpret, including:
- Hazard ratio (HR): if the HR = 0.5 then half as many patients are experiencing the event compared to the control group, if the HR = 1 the event rates are the same in both groups, and if the HR = 2 then twice as many are experiencing an event compared to the control group.
- HR limit: this is the confidence interval of the hazard ratio.
- P-value: the lower the p-value, the greater the significance of the observation.

(e.g. If you have selected both a co-predictor and strata factor then a comparison using the co-predictors and Type III p-value for the co-predictor will be generated in the Cox regression report.)

Kaplan-Meier Survival Curve

The Kaplan-Meier task is used for comparing the survival curves among two or more groups of samples. The groups are defined by one or more categorical attributes (factors) specified by the user. Like in the case of Cox Regression, it is possible to use feature expression data, if available. In that case, quantitative feature expression is converted into a feature-specific categorical attribute. Each combination of the attribute levels corresponds to a distinct group. If one selects three factors with 2, 3 and 5 levels, respectively, then the total count of compared groups is 2*3*5 = 30. Therefore, selecting too many factors and/or factors with many levels may not work since the total number of samples may be not enough to fill all of the groups.

To perform Kaplan-Meier survival analysis, at least two pieces of information must be provided for each sample: time-to-event (a numeric factor) and event status (categorical factor with two levels). Time-to-event indicates the time elapsed between the enrollment of a subject in the study and the occurrence of the event. Event status indicates whether the event occurred or the subject was censored (did not experience the event). The survival curve is not straight lines connecting each point, instead a staircase pattern is used. The event status will determine the staircase pattern where each drop in the staircase represents the event occurrence.

Getting started with the Kaplan-Meier task

The Kaplan-Meier task begins similar to the Cox regression task, then differs when selecting categorical attributes to define the compared groups.

For each feature (e.g. gene), the expression values are sorted in ascending order and placed into B bins of (roughly) equal size. As a result, a feature-specific categorical attribute with B levels is constructed which can be used by itself or in combination with other categorical attributes. For instance, for B = 2 (Figure 5) , we take a given feature and compute its median expression. The samples are separated into two bins, depending on whether the expression in the sample is below or above the median. if two percentiles are chosen, the bins are automatically labeled "Low" and "High" but the text box can be used to re-label the bins. The bins are feature-specific since this procedure is repeated for each feature separately.

For each group, the survival curve (aka survival function) is estimated using Kaplan-Meier estimator [1]. For instance, if one selects ER status which has two levels and we choose two feature expression bins, four survival curves are displayed in the Data Viewer (Figure 6). The Grouping configuration option can be used to split and modify the connections.

To see whether the survival curves are statistically different, Kaplan-Meier task runs Log-rank and Wilcoxon (aka Wilcoxon-Gehan) tests. The null hypothesis is that the survival curves do not differ among the groups (the computational details are available in [2]). When feature expression is used, the p-values are also feature specific (Figure 7). Select the step-plot icon under View to visualize the Kaplan-Meier survival curves for each gene.

Choosing stratification factors

Like in Cox Regression task, it is possible to choose stratification factor(s), but the purpose and meaning of stratification are not the same as in Cox Regression. Suppose we want to compare the survival among the four groups defined by the two levels of ER status and the two bins of feature expression. We can select the two factors on “Select group factor(s)” page. In that case, the reported p-values will reflect the statistical difference among the four survival curves that are due to both ER status and the feature expression. Imagine that our primary interest is the effect of feature expression on survival. Although ER status can be important and therefore should be included in the model, we want to know whether the effect of feature expression is significant after the contribution of ER status is taken into account. In other words, the goal is to treat ER status as a nuisance factor and the binned feature expression as a factor of interest.

In qualitative terms, it is possible to obtain an answer if we group the survival curves by the level of ER status. This can be achieved in the Data Viewer by choosing Grouping > Split by under Configure. That makes it easy to compare the survival curves that have the same level of ER status and avoid the comparison of curves across different levels of ER status.

If in Figure 8, we see one or more subplots where the survival curves differ a lot, that is evidence that the feature expression affects the survival even after adjusting for the contribution of ER status. To obtain an answer in terms of adjusted Log-rank and Wilcoxon p-values, one should deselect ER status as a “group factor” (Figure 4) and mark it as a stratification factor instead (Figure 9).

The computation of stratification adjusted p-values is elaborated in [2].

Suppose when the feature expression and ER status are selected as “group factors”, Log-rank p-value is 0.001, and when ER status is marked as stratification factor, the p-value becomes 0.70. This means that ER status is very useful for explaining the difference in survival while the feature factor is of no use if ER status is already in the model. In other words, the marginal contribution of the binned expression factor is low.

If more than two attributes are present, it is possible to measure the marginal contribution of any single factor in a similar manner: the attribute of interest should be selected as “group factor” and the other attributes should be marked as stratification factors. There is no limit on the count of factors that can be selected as “group” or stratification, except that all of the selected factors are involved in defining the groups and the groups should contain enough samples (at least, be non-empty) for the results to be reliable.

Troubleshooting

If the task fails (no report is produced), please follow the directions in Reporting a problem.

If the task report is produced, but the results are missing for some features, it may be possible to fix the issue by following the directions in the Differential Analysis Troubleshooting section.

References

[1] Kaplan-Meier (product limit) estimator: https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator

[2] Klein, Moeschberger (1997), Survival Analysis: Techniques for Censored and Truncated Data. ISBN-13: 978-0387948294

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Hierarchical Clustering

Hierarchical clustering is a statistical method used to assign similar objects into groups called clusters. It is typically performed on results of statistical analyses, such as a list of significant genes/transcripts, but can also be invoked on the full data set, as a part of exploratory analysis.

Hierarchical clustering is an unsupervised technique, meaning that the number of clusters is not specified upfront. In the beginning, each row and/or column is considered a cluster. The two most similar clusters are combined and continue to combine until all objects are in the same cluster. Hierarchical clustering produces a tree (called a dendrogram) that shows the hierarchy of the clusters.

This tutorial will illustrate how to:

Invoking Hierarchical Clustering
Heatmap
Bubble map
Configuration
In-plot controls

Invoking Hierarchical Clustering

To invoke hierarchical clustering, select a data node containing count data (e.g. Gene counts, Normalized counts, Single cell counts), or a Feature list data node (to cluster significant genes/transcripts) and then click on the Hierarchical clustering / heat map option in the context sensitive menu (Figure 1).

The hierarchical clustering setup dialog (Figure 2) enables you to control the clustering algorithm. Starting from the top, you can choose to plot a Heatmap or a Bubble map (clustering can be performed on both plot types). Next, perform Ordering by selecting Cluster for either feature order (genes/transcripts/proteins) or cell/sample/group order or both. Note the context-sensitive image that helps you decide to either perform hierarchical clustering (dendrogram) or assign order (arrow) for the columns and rows to help you orient yourself and make decisions (In Figure 2 below, Cluster is selected for both options so a dendrogram is shown in the image).

When choose Assign order, the Default order of cells/samples/groups (rows) is based upon the labels as displayed in the Data tab and features (columns) are dependent on the input data of the data node.

Feature order can be assigned by selecting a managed list (e.g. generate saved feature lists from report nodes or add lists under list management in the settings) in the drop-down which will limit the features to only those in the list and the features will be ordered as they are listed. If a feature is not available, based on the input of the data node, it will not be shown in the plot (in other words, if the features from the list are not there they will not be plotted). Note that If no features are available from the data node, the task will not be able to perform and an error message will be shown.

Cell/Sample/Group order can also be assigned by choosing an attribute from the drop down list. Click and drag to rearrange categorical attributes; numeric attributes can be sorted in ascending or descending order (note the arrows in the image which are different from the dendrogram for Cluster) (Figure 3).

Another way to invoke a heatmap without performing clustering is via the data viewer. When you select the Heatmap icon in the available plots list, data nodes that contain two-dimensional matrices can be used to draw this type of plot. A bubble map can also be similarly plotted (use the arrow from the heatmap icon to select a Bubble map for descriptive statistics that have been generated in the data analysis pipeline.

If you do not want to cluster all the samples, but select a subset based on a specific sample or cell attribute (i.e. group membership), check Filter cells under Filtering and set a filtering rule using the drop down lists (Figure 4). Notice the drop-down lists allow more than one factor (when available) to be selected at a time. When configuring the filtering rule, use AND to ensure all conditions pass for inclusion and use OR for any conditions to pass.

Hierarchical clustering uses distance metrics to sort based on similarity and is set to Average Linkage by default. This can be adjusted by clicking Configure under Advanced options (Figure 5). You can choose how the data is scaled (sometimes referred to as normalized). There are three Feature scaling options, Standardize (default for a heatmap) will make each column mean as zero and standard deviation as 1 in all features. This is the default scaling for a heatmap and it makes all of the features (e.g., genes or proteins) have equal weight; standardized values are also known as Z-scores. The scaling mode Shift will make each column mean as zero. Choose None to not scale and perform clustering on the values in the input data node (this is the default for a bubble map). If a bubble map is scaled, scaling will be performed on the group summary method (color).

Cluster distance metric for cells/samples and features is used to determine how the distance between two clusters will be calculated:

Single Linkage: the distance between two clusters is determined by the distance of the closest objects in the two clusters
Complete Linkage: the distance between two clusters is equal to the distance between the two furthest members of those clusters
Average Linkage: the average distance between all the pairs of objects in the two different clusters is used as the measure of distance between the two clusters
Centroid method: the distance between two clusters is equal to the distance between the centroids of those clusters
Ward's method: the distance between two clusters is designed to minimize the size of an error measure based on the sum of squares

Point distance metric is used to determine the distance between two rows or columns. For more detailed information about the equations, we refer you to the distance metrics chapter below.

Heatmap

The output of a Hierarchical clustering task can be a heatmap (Figure 6) or a bubble map with or without dendrograms depending on whether you performed clustering on cells/samples/groups or features. By default, samples are on rows (sample labels are displayed as seen in the Data tab) and features (depending on the input data) are on columns. Colors are based on standardized expression values (default selection; performed on the fly). Dendrograms show clustering of rows (samples) and columns (variables).

Depending on the resolution of your screen and the number of samples and variables (features) that need to be displayed, some binning may be involved. If there are more samples/genes than pixels, values of neighboring rows/columns will be averaged together. Use the mouse wheel to zoom in and out. When you zoom in to certain level on the heatmap, you will see each cell represent one sample/gene. When you mouse over the row dendrogram or label area and zoom, it will only zoom in/out on the rows. The binning on the columns will remain the same. Similarly, when you mouse over the column dendrogram or label area and zoom, it will only zoom in/out on the columns. The binning on the rows will remain the same. To move the map around when zoomed in, press down the left mouse button and drag the map. The plot can be saved as a full-size image or as a current view; when Save image is clicked, a prompt will ask how you would like to save the image.

Bubble map

The Hierarchical clustering task can also be used to plot a bubble map. Let's go through the steps to make a bubble map (Figure 7):

Choose to plot a Bubble map (note the selection of a bubble map in the image which is different from the heatmap). This will open the Bubble map settings.
Configure the Bubble map settings. First, Group cells by an available categorical attribute (e.g. cell type). Next, summarize the group’s first dimension by color (Group summary method) then choose an additional dimension to plot size (Additional statistic) by using the drop down lists. If these settings are not adjusted, the default dimensions will generate two descriptive statistic measurements that plot the group mean by color and size by the percent of cells. Hierarchical clustering can be performed on the first assigned dimension (by color) which is the Group summary method. The second dimension (size) which is an Additional statistic is not required but it is selected by default (this can be unchecked with the checkbox).
Ordering the plot columns (Feature order) and rows (Group order) behaves the same as a heatmap. In this example, Ordering for both features and groups by Cluster uses hierarchical clustering to perform distance metrics (default settings will be used but these metrics can be changed under Configure in the Advanced options section). Alternatively, Assign order to features using a managed (saved) feature list or the default order which is dependent on the input data. Assign order to groups can be used to rearrange the attribute by drag and drop, ascending or descending order, or default order which is how the labels as displayed in the Data tab.
Filtering can be applied to the groups by checking Filter cells then specifying the logical operations to filter by (this is the same as a heatmap).
Advanced options let the user perform Feature scaling (e.g. Standardize by a z-score) but in a bubble map the default is set to None. It also allows the user to change the Group clustering and Feature clustering options by altering the Cluster distance metrics and Point distance metrics (similar to a heatmap).

<figure><img src="../../../../.gitbook/assets/image (113).png" alt=""><figcaption><p>Figure 7. The bubble map dialogue, similar to the heatmap dialogue, has groups (cells/samples) on rows and features (variables) on columns. By default, the bubble map color (group summary method) is mean and size (additional statistic) is the percent of cells which can be changed using the drop down list.</p></figcaption></figure>

Configuration

There are plot Configuration/Action options for the Hierarchical clustering / heatmap task which apply to both the heatmap and bubble map in the Data viewer (below): Axes, Heatmap, Dendrograms, Annotations, and Description. Click on the icon to open these configuration options.

Axes

This section controls the Content or data source used to draw the values in the heatmap or bubble map and also the ability to transpose the axes. The plot is a color representation of the values in the selected matrix. Most of the data nodes contain only one matrix, so it will just say Matrix for the chosen data node. However, if a data node contains multiple matrices (e.g. descriptive statistics were performed on cluster groups for every gene like mean, standard deviation, percent of cells, etc) each statistic will be in a separate matrix in the output data node. In this case, you can choose which statistic/matrix to display using the drop-down list (this would be the case in a bubble map).
To change the orientation (switch the columns and rows) of the plot, click on the Transpose toggle switch.
Row labels and Column labels can be turned on or off by clicking the relevant toggle switches.
The label size can be changed by specifying the number of pixels using Max size and Font. If an Ensembl annotation model has been associated with the data, you can choose to display the gene name or the Ensembl ID using the Content option.\

Heatmap

This section is used to configure the color, range, size, and shape of the components in the heatmap.

In the color palette horizontal bar, the left side color represents the lowest value and the right side color represents the highest value in the matrix data. Note that when you zoom in/out the lowest and highest values captured by the color palette may change. By default, there are 3 color stops: minimum, middle, and maximum color value of the default range calculated on the matrix. Left-click on the middle color stop and drag left or right to change the middle value this color stop represents. If you left-click on the middle color stop once, you can change the color and value this color stop represents. Click on the (X) to remove this color stop.\
Click on the color square or the adjacent triangle to choose a color to represent the value. This will display a color picker dialog which allows selection of a color, either by clicking or by typing a HEX color code, then clicking OK.

<figure><img src="../../../../.gitbook/assets/image (118).png" alt=""><figcaption></figcaption></figure>

The min and max color stops cannot be dragged or removed. If you left-click on them, you can choose a different color. When you click on the Palette bar, you can add a new color stop between min and max. Adding a color stop can be useful when there is an outlier value in the data. You can use a different color to represent different value ranges.
Right-clicking a color stop will reveal a list of options. Space colors evenly will rearrange the position of the stops so there is an equal distance between all stops. Center will move a stop to the middle of the two adjacent stops. Delete will remove the stop.
In addition to color, you can also use the Size drop-down list to size by a set of values from another matrix stored in the same data node. Most of the data nodes contain only one matrix, so the only options available in the Size drop down will be None or Matrix. In cases where you have multiple matrices, you might want to use the color of the component in the heatmap to represent one type of statistic (like mean of the groups) and the size of the component to represent the information from a different statistic (like std. dev).
The shape of the heatmap cell (component) can be configured either as a rectangle or circle by selecting the radio button under Shape.

<figure><img src="../../../../.gitbook/assets/image (122).png" alt=""><figcaption></figcaption></figure>

Dendrograms

If cluster analysis is performed on samples and/or features, the result will be displayed as dendrograms. By default, the dendrograms are all colored in black.

The color of the dendrograms can be configured.

Click on the color square or its triangle to choose a different color for the dendrogram.
When the By cluster in the Row/Column color drop-down list, the number of clusters needs to be specified. The top N clusters will be in N different colors.

Annotations

This section allows you to add sample or cell level annotations to the viewer. First, make sure to choose the correct data node which contains the annotation information you would like to use by clicking the circle (). All project level annotations will be available on all data nodes in the pipeline.

Choose an attribute from the Row annotation drop-down list. Multiple attributes can be chosen from the drop-down list and can be reordered by clicking and dragging the groups below the drop-down list. Each attribute is represented as an annotation bar next to the heatmap. Different colors represent the different groups in the attribute.
The width of the annotation bar can be changed using the Block size slider when the Show labels toggle switch is on.
The annotation label font size can be changed by specifying the size in pixels.
The Fill blocks toggle switch adds or removes color from the annotation labels.\

Description

Description is used to modify the Title and toggle on or off the Legend.

In-plot controls

The heatmap has several different mouse modes which modify the way the plot responds to the mouse buttons. The mode buttons are in the upper right corner of the heatmap. Clicking one of these buttons puts the heatmap into that mode.

In point mode (), you can left-click and drag to move around the heatmap (if you are not fully zoomed out). Left-clicking once on the heatmap or on a dendrogram branch will select the associated rows/columns.
In selection mode (), you can click and drag to select a range of rows, columns, or components.
In flip mode (), you can click on a line in the dendrogram (which represents a cluster branch) and the location of the two legs of the branch will be swapped. If no clustering is performed (no dendrogram is generated), in this mode, you can click on the label of an item (observation or feature), drag and drop to manually switch orders of the row or column on the heatmap.
Click on reset view () to reset to the default
Save Image icon () enables you to download the heat map to your local computer. If the heat map contains up to 2.5M cells (features * observations), you can choose between saving the current appearance of the heat map window (Current view) and saving the entire heat map (All data). Depending on the number of features / observations, Partek Flow may not be able to fit all the labels on the screen, due to the limit imposed by the screen resolution. All Data option provides an image file of sufficient size so that all the labels are readable (in turn, that image may not fit the compute screen and the image file may be quite large). If the heat map exceeds 2.5M cells, the Current view option will not be shown, and you will see only a dialog like the one below.
After selecting either Current view (if applicable) or All data button, the next dialog (below) will allow you to specify the image format, size, and resolution.

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