LogoLogo
Illumina KnowledgeIllumina SupportSign In
Partek
  • Home
Partek
  • Overview
  • Partek Flow
    • Frequently Asked Questions
      • General
      • Visualization
      • Statistics
      • Biological Interpretation
      • How to cite Partek software
    • Quick Start Guide
    • Installation Guide
      • Minimum System Requirements
      • Single Cell Toolkit System Requirements
      • Single Node Installation
      • Single Node Amazon Web Services Deployment
      • Multi-Node Cluster Installation
      • Creating Restricted User Folders within the Partek Flow server
      • Updating Partek Flow
      • Uninstalling Partek Flow
      • Dependencies
      • Docker and Docker-compose
      • Java KeyStore and Certificates
      • Kubernetes
    • Live Training Event Recordings
      • Bulk RNA-Seq Analysis Training
      • Basic scRNA-Seq Analysis & Visualization Training
      • Advanced scRNA-Seq Data Analysis Training
      • Bulk RNA-Seq and ATAC-Seq Integration Training
      • Spatial Transcriptomics Data Analysis Training
      • scRNA and scATAC Data Integration Training
    • Tutorials
      • Creating and Analyzing a Project
        • Creating a New Project
        • The Metadata Tab
        • The Analyses Tab
        • The Log Tab
        • The Project Settings Tab
        • The Attachments Tab
        • Project Management
        • Importing a GEO / ENA project
      • Bulk RNA-Seq
        • Importing the tutorial data set
        • Adding sample attributes
        • Running pre-alignment QA/QC
        • Trimming bases and filtering reads
        • Aligning to a reference genome
        • Running post-alignment QA/QC
        • Quantifying to an annotation model
        • Filtering features
        • Normalizing counts
        • Exploring the data set with PCA
        • Performing differential expression analysis with DESeq2
        • Viewing DESeq2 results and creating a gene list
        • Viewing a dot plot for a gene
        • Visualizing gene expression in Chromosome view
        • Generating a hierarchical clustering heatmap
        • Performing biological interpretation
        • Saving and running a pipeline
      • Analyzing Single Cell RNA-Seq Data
      • Analyzing CITE-Seq Data
        • Importing Feature Barcoding Data
        • Data Processing
        • Dimensionality Reduction and Clustering
        • Classifying Cells
        • Differentially Expressed Proteins and Genes
      • 10x Genomics Visium Spatial Data Analysis
        • Start with pre-processed Space Ranger output files
        • Start with 10x Genomics Visium fastq files
        • Spatial data analysis steps
        • View tissue images
      • 10x Genomics Xenium Data Analysis
        • Import 10x Genomics Xenium Analyzer output
        • Process Xenium data
        • Perform Exploratory analysis
        • Make comparisons using Compute biomarkers and Biological interpretation
      • Single Cell RNA-Seq Analysis (Multiple Samples)
        • Getting started with the tutorial data set
        • Classify cells from multiple samples using t-SNE
        • Compare expression between cell types with multiple samples
      • Analyzing Single Cell ATAC-Seq data
      • Analyzing Illumina Infinium Methylation array data
      • NanoString CosMx Tutorial
        • Importing CosMx data
        • QA/QC, data processing, and dimension reduction
        • Cell typing
        • Classify subpopulations & differential expression analysis
    • User Manual
      • Interface
      • Importing Data
        • SFTP File Transfer Instructions
        • 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
      • Task Menu
        • Task actions
        • Data summary report
        • QA/QC
          • Pre-alignment QA/QC
          • ERCC Assessment
          • Post-alignment QA/QC
          • Coverage Report
          • Validate Variants
          • Feature distribution
          • Single-cell QA/QC
          • Cell barcode QA/QC
        • Pre-alignment tools
          • Trim bases
          • Trim adapters
          • Filter reads
          • Trim tags
        • Post-alignment tools
          • Filter alignments
          • Convert alignments to unaligned reads
          • Combine alignments
          • Deduplicate UMIs
          • Downscale alignments
        • Annotation/Metadata
          • Annotate cells
          • Annotation report
          • Publish cell attributes to project
          • Attribute report
          • Annotate Visium image
        • Pre-analysis tools
          • Generate group cell counts
          • Pool cells
          • Split matrix
          • Hashtag demultiplexing
          • Merge matrices
          • Descriptive statistics
          • Spot clean
        • Aligners
        • Quantification
          • Quantify to annotation model (Partek E/M)
          • Quantify to transcriptome (Cufflinks)
          • Quantify to reference (Partek E/M)
          • Quantify regions
          • HTSeq
          • Count feature barcodes
          • Salmon
        • Filtering
          • Filter features
          • Filter groups (samples or cells)
          • Filter barcodes
          • Split by attribute
          • Downsample Cells
        • Normalization and scaling
          • Impute low expression
          • Impute missing values
          • Normalization
          • Normalize to baseline
          • Normalize to housekeeping genes
          • Scran deconvolution
          • SCTransform
          • TF-IDF normalization
        • Batch removal
          • General linear model
          • Harmony
          • Seurat3 integration
        • Differential Analysis
          • GSA
          • ANOVA/LIMMA-trend/LIMMA-voom
          • Kruskal-Wallis
          • Detect alt-splicing (ANOVA)
          • DESeq2(R) vs DESeq2
          • Hurdle model
          • Compute biomarkers
          • Transcript Expression Analysis - Cuffdiff
          • Troubleshooting
        • Survival Analysis with Cox regression and Kaplan-Meier analysis - Partek Flow
        • Exploratory Analysis
          • Graph-based Clustering
          • K-means Clustering
          • Compare Clusters
          • PCA
          • t-SNE
          • UMAP
          • Hierarchical Clustering
          • AUCell
          • Find multimodal neighbors
          • SVD
          • CellPhoneDB
        • Trajectory Analysis
          • Trajectory Analysis (Monocle 2)
          • Trajectory Analysis (Monocle 3)
        • Variant Callers
          • SAMtools
          • FreeBayes
          • LoFreq
        • Variant Analysis
          • Fusion Gene Detection
          • Annotate Variants
          • Annotate Variants (SnpEff)
          • Annotate Variants (VEP)
          • Filter Variants
          • Summarize Cohort Mutations
          • Combine Variants
        • Copy Number Analysis (CNVkit)
        • Peak Callers (MACS2)
        • Peak analysis
          • Annotate Peaks
          • Filter peaks
          • Promoter sum matrix
        • Motif Detection
        • Metagenomics
          • Kraken
          • Alpha & beta diversity
          • Choose taxonomic level
        • 10x Genomics
          • Cell Ranger - Gene Expression
          • Cell Ranger - ATAC
          • Space Ranger
          • STARsolo
        • V(D)J Analysis
        • Biological Interpretation
          • Gene Set Enrichment
          • GSEA
        • Correlation
          • Correlation analysis
          • Sample Correlation
          • Similarity matrix
        • Export
        • Classification
        • Feature linkage analysis
      • Data Viewer
      • Visualizations
        • Chromosome View
          • Launching the Chromosome View
          • Navigating Through the View
          • Selecting Data Tracks for Visualization
          • Visualizing the Results Using Data Tracks
          • Annotating the Results
          • Customizing the View
        • Dot Plot
        • Volcano Plot
        • List Generator (Venn Diagram)
        • Sankey Plot
        • Transcription Start Site (TSS) Plot
        • Sources of variation plot
        • Interaction Plots
        • Correlation Plot
        • Pie Chart
        • Histograms
        • Heatmaps
        • PCA, UMAP and tSNE scatter plots
        • Stacked Violin Plot
      • Pipelines
        • Making a Pipeline
        • Running a Pipeline
        • Downloading and Sharing a Pipeline
        • Previewing a Pipeline
        • Deleting a Pipeline
        • Importing a Pipeline
      • Large File Viewer
      • Settings
        • Personal
          • My Profile
          • My Preferences
          • Forgot Password
        • System
          • System Information
          • System Preferences
          • LDAP Configuration
        • Components
          • Filter Management
          • Library File Management
            • Library File Management Settings
            • Library File Management Page
            • Selecting an Assembly
            • Library Files
            • Update Library Index
            • Creating an Assembly on the Library File Management Page
            • Adding Library Files on the Library File Management Page
            • Adding a Reference Sequence
            • Adding a Cytoband
            • Adding Reference Aligner Indexes
            • Adding a Gene Set
            • Adding a Variant Annotation Database
            • Adding a SnpEff Variant Database
            • Adding a Variant Effect Predictor (VEP) Database
            • Adding an Annotation Model
            • Adding Aligner Indexes Based on an Annotation Model
            • Adding Library Files from Within a Project
            • Microarray Library Files
            • Adding Prep kit
            • Removing Library Files
          • Option Set Management
          • Task Management
          • Pipeline managment
          • Lists
        • Access
          • User Management
          • Group Management
          • Licensing
          • Directory Permissions
          • Access Control Log
          • Failed Logins
          • Orphaned files
        • Usage
          • System Queue
          • System Resources
          • Usage Report
      • Server Management
        • Backing Up the Database
        • System Administrator Guide (Linux)
        • Diagnosing Issues
        • Moving Data
        • Partek Flow Worker Allocator
      • Enterprise Features and Toolkits
        • REST API
          • REST API Command List
      • Microarray Toolkit
        • Importing Custom Microarrays
      • Glossary
    • Webinars
    • Blog Posts
      • How to select the best single cell quality control thresholds
      • Cellular Differentiation Using Trajectory Analysis & Single Cell RNA-Seq Data
      • Spatial transcriptomics—what’s the big deal and why you should do it
      • Detecting differential gene expression in single cell RNA-Seq analysis
      • Batch remover for single cell data
      • How to perform single cell RNA sequencing: exploratory analysis
      • Single Cell Multiomics Analysis: Strategies for Integration
      • Pathway Analysis: ANOVA vs. Enrichment Analysis
      • Studying Immunotherapy with Multiomics: Simultaneous Measurement of Gene and Protein
      • How to Integrate ChIP-Seq and RNA-Seq Data
      • Enjoy Responsibly!
      • To Boldly Go…
      • Get to Know Your Cell
      • Aliens Among Us: How I Analyzed Non-Model Organism Data in Partek Flow
    • White Papers
      • Understanding Reads in RNA-Seq Analysis
      • RNA-Seq Quantification
      • Gene-specific Analysis
      • Gene Set ANOVA
      • Partek Flow Security
      • Single Cell Scaling
      • UMI Deduplication in Partek Flow
      • Mapping error statistics
    • Release Notes
      • Release Notes Archive - Partek Flow 10
  • Partek Genomics Suite
    • Installation Guide
      • Minimum System Requirements
      • Computer Host ID Retrieval
      • Node Locked Installation
        • Windows Installation
        • Macintosh Installation
      • Floating/Locked Floating Installation
        • Linux Installation
          • FlexNet Installation on Linux
        • Installing FlexNet on Windows
        • License Server FAQ's
        • Client Computer Connection to License Server
      • Uninstalling Partek Genomics Suite
      • Updating to Version 7.0
      • License Types
      • Installation FAQs
    • User Manual
      • Lists
        • Importing a text file list
        • Adding annotations to a gene list
        • Tasks available for a gene list
        • Starting with a list of genomic regions
        • Starting with a list of SNPs
        • Importing a BED file
        • Additional options for lists
      • Annotation
      • Hierarchical Clustering Analysis
      • Gene Ontology ANOVA
        • Implementation Details
        • Configuring the GO ANOVA Dialog
        • Performing GO ANOVA
        • GO ANOVA Output
        • GO ANOVA Visualisations
        • Recommended Filters
      • Visualizations
        • Dot Plot
        • Profile Plot
        • XY Plot / Bar Chart
        • Volcano Plot
        • Scatter Plot and MA Plot
        • Sort Rows by Prototype
        • Manhattan Plot
        • Violin Plot
      • Visualizing NGS Data
      • Chromosome View
      • Methylation Workflows
      • Trio/Duo Analysis
      • Association Analysis
      • LOH detection with an allele ratio spreadsheet
      • Import data from Agilent feature extraction software
      • Illumina GenomeStudio Plugin
        • Import gene expression data
        • Import Genotype Data
        • Export CNV data to Illumina GenomeStudio using Partek report plug-in
        • Import data from Illumina GenomeStudio using Partek plug-in
        • Export methylation data to Illumina GenomeStudio using Partek report plug-in
    • Tutorials
      • Gene Expression Analysis
        • Importing Affymetrix CEL files
        • Adding sample information
        • Exploring gene expression data
        • Identifying differentially expressed genes using ANOVA
        • Creating gene lists from ANOVA results
        • Performing hierarchical clustering
        • Adding gene annotations
      • Gene Expression Analysis with Batch Effects
        • Importing the data set
        • Adding an annotation link
        • Exploring the data set with PCA
        • Detect differentially expressed genes with ANOVA
        • Removing batch effects
        • Creating a gene list using the Venn Diagram
        • Hierarchical clustering using a gene list
        • GO enrichment using a gene list
      • Differential Methylation Analysis
        • Import and normalize methylation data
        • Annotate samples
        • Perform data quality analysis and quality control
        • Detect differentially methylated loci
        • Create a marker list
        • Filter loci with the interactive filter
        • Obtain methylation signatures
        • Visualize methylation at each locus
        • Perform gene set and pathway analysis
        • Detect differentially methylated CpG islands
        • Optional: Add UCSC CpG island annotations
        • Optional: Use MethylationEPIC for CNV analysis
        • Optional: Import a Partek Project from Genome Studio
      • Partek Pathway
        • Performing pathway enrichment
        • Analyzing pathway enrichment in Partek Genomics Suite
        • Analyzing pathway enrichment in Partek Pathway
      • Gene Ontology Enrichment
        • Open a zipped project
        • Perform GO enrichment analysis
      • RNA-Seq Analysis
        • Importing aligned reads
        • Adding sample attributes
        • RNA-Seq mRNA quantification
        • Detecting differential expression in RNA-Seq data
        • Creating a gene list with advanced options
        • Visualizing mapped reads with Chromosome View
        • Visualizing differential isoform expression
        • Gene Ontology (GO) Enrichment
        • Analyzing the unexplained regions spreadsheet
      • ChIP-Seq Analysis
        • Importing ChIP-Seq data
        • Quality control for ChIP-Seq samples
        • Detecting peaks and enriched regions in ChIP-Seq data
        • Creating a list of enriched regions
        • Identifying novel and known motifs
        • Finding nearest genomic features
        • Visualizing reads and enriched regions
      • Survival Analysis
        • Kaplan-Meier Survival Analysis
        • Cox Regression Analysis
      • Model Selection Tool
      • Copy Number Analysis
        • Importing Copy Number Data
        • Exploring the data with PCA
        • Creating Copy Number from Allele Intensities
        • Detecting regions with copy number variation
        • Creating a list of regions
        • Finding genes with copy number variation
        • Optional: Additional options for annotating regions
        • Optional: GC wave correction for Affymetrix CEL files
        • Optional: Integrating copy number with LOH and AsCN
      • Loss of Heterozygosity
      • Allele Specific Copy Number
      • Gene Expression - Aging Study
      • miRNA Expression and Integration with Gene Expression
        • Analyze differentially expressed miRNAs
        • Integrate miRNA and Gene Expression data
      • Promoter Tiling Array
      • Human Exon Array
        • Importing Human Exon Array
        • Gene-level Analysis of Exon Array
        • Alt-Splicing Analysis of Exon Array
      • NCBI GEO Importer
    • Webinars
    • White Papers
      • Allele Intensity Import
      • Allele-Specific Copy Number
      • Calculating Genotype Likelihoods
      • ChIP-Seq Peak Detection
      • Detect Regions of Significance
      • Genomic Segmentation
      • Loss of Heterozygosity Analysis
      • Motif Discovery Methods
      • Partek Genomics Suite Security
      • Reads in RNA-Seq
      • RNA-Seq Methods
      • Unpaired Copy Number Estimation
    • Release Notes
    • Version Updates
    • TeamViewer Instructions
  • Getting Help
    • TeamViewer Instructions
Powered by GitBook
On this page
  • Group data by
  • Annotate amino acids by
  • Color by
  • Read histogram Y-axis max
  • Read histogram type
  • Split read histogram by strand
  • Transcript label
  • Track Order
  • Selection Details
  • Additional Assistance

Was this helpful?

Export as PDF
  1. Partek Flow
  2. User Manual
  3. Visualizations
  4. Chromosome View

Customizing the View

PreviousAnnotating the ResultsNextDot Plot

Last updated 7 months ago

Was this helpful?

Controls

Chromosome view can be customized by using the control panel on the left (Figure 1). The Attribute and Order By controls show options depending on the current project, while the content of the Annotate amino acids control depends on the annotation files associated with the current genome build in the Library File Management. In order for any change to take place, push the Apply button.

Figure 1. Control panel (an example is shown)

Group data by

The first option, Group data by, specifies the number of Alignments tracks (Figure 2). All will result in only one track, with all the samples on it. Sample creates one track per sample, while Attribute produces one Alignments track per level of the Attribute (i.e. one track per group).

All

Sample

Attribute

Figure 2. Group data by: All creates one Alignments track for the entire project, Sample creates one Alignments track for each sample, Attribute creates one Alignments track for each group (an example is shown)

Annotate amino acids by

Annotate amino acids by controls the appearance of the Amino acids track and allows you to pick the transcript database that will be used to plot codons (Figure 3). The drop down list shows the databases currently available for the selected genome (additional databases can be added via Library File Management).

Figure 3. Annotate amino acids by: transcript models currently associated with the chosen genome are displayed in the drop-down list and can be used to plot Amino acids track (an example is shown)

Color by

Color by option affects the colouring of the Alignments track and Isoform proportion track. When Sample is selected from the drop-down list, individual samples will be shown on the aforementioned tracks, each sample being given a different colour. If attributes were assigned to samples, they will also be visible in the Color by drop-down (Figure 4) and you will be able to highlight levels of the selected attribute (Figure 5).

Figure 4. Color by: the options control colouring of Alignments and Isoform proportion tracks. Sample, Base, and Match options are present by default. If attributes have been assigned to samples, they will appear in the drop-down list. In this example, that is the "Tissue" attribute

Color by Sample

Color by

Figure 5. Difference between Color by Sample and Color by . Color by Sample uses different colours to depict individual samples; Color by uses different colours to depict levels of the selected sample attribute (as present in the Data tab). Alignments and Isoform proportion tracks are shown (an example)

The effect of the option to Color by Base can be seen with high power magnification (Figure 6). Individual base calls are highlighted by different colours. When that option is chosen at low power magnification, all the bases are shown in grey.

Figure 6. Color by Base highlights the base calls by colours. Different colours are visible with high power magnification; otherwise all the bases are shown in gray (an example)

Finally, Color by Match can be used to quickly identify mismatches against the reference genome. A matching base is coloured in blue, while mismatch bases are shown in yellow.

Read histogram Y-axis max

The maximum of the y-axis of Alignments tracks is set by Read histogram Y axis scales option (Figure 7). When using Project max, the y-axis for each track is set individually, based on the maximum within that sample. On the other hand, Track max uses the maximum across all the samples and uses that value as the maximum for all.

Project max

Track max

Figure 7. Read histogram Y axis scales. When set to Linked, all the tracks have the same Y axis maximum, which depends on the sample with the highest coverage. Using Independent sets Y axis maximum independently for each sample.

Read histogram type

Read histogram type changes the presentation of the Alignments track and should be used in conjunction with the Group data by and Color by tracks to get the desired visualisation.

When set to Sum, the Read histogram type shows the sum of base calls at each position, i.e. total coverage per position. Figure 8 shows an Alignments track with three samples. With the Sum option, the number of reads at each base in each sample is added and displayed. The contribution of individual samples is not visible since the track is Colored by Group (but that would make sense in this example).

Figure 8. Alignments track: total coverage per locus is shown by using "Read histogram type" set to "Sum" and "Group data by" set to

To show the average coverage per locus, switch Read histogram type to Average and leave Color by as is (i.e. by group) (Figure 9). With this setting, Chromosome view will calculate the average by dividing the total coverage per locus by the number of samples. Note that using Color by Sample would not make sense here. Although Figure 8 looks quite like Figure 7, the y-axis range is different.

Figure 9. Alignments track: average coverage per locus is shown by using "Read histogram type" set to "Average", "Group data by" set to "Attribute", and "Color by" set to

Finally, the option Overlay is useful if you want to directly compare base counts over several samples (or groups) as each will be represented by a line (i.e. no stacking). The example in Figure 10 is based on microarray data, showing three groups on the same Alignments track. The red group has the highest base counts, while the counts in the blue group are much lower.

Figure 10. Alignments track: coverage per locus is shown by using "Read histogram type" set to "Overlay". Each plot is a single experimental condition ("Group data by" set to "Attribute", "Color by" set to ). Lines are rectangular since microarray data is used (an example)

Split read histogram by strand

To view the reads histogram grouped by the specific strand that they've mapped to, click the Split read histogram by strand checkbox. This displays the forward reads at the top half of the track and the reverse reads on the bottom half of the track (Figure 11). This track can be helpful in studies such as ChIP-Seq, where strand-specific read distributions can display hallmarks of DNA-protein interactions.

Figure 11. Viewing reads by strand along the reads histogram

Transcript label

You can use the Transcript label selector to specify labels on the reference transcript track and Isoform proportion track (Figure 12).

Transcript label: Gene

Transcript label: Transcript

Figure 12. ranscript label: setting the control to Gene shows only gene label, while Transcript shows transcript labels. Both transcript database and Isoform proportion tracks are affected Short sequencing reads can be coloured by strand (Reads pileup color: Strand) or by base (Reads pileup color: Base).

Reads pileup and probe color

Reads pileup color: Strand

Reads pileup color: Base

Figure 13. Reads pileup color: colouring of the short sequencing reads by Strand or by Base

Probe color control customizes the appearance of Probe intensities track (Figure 14). When set to Intensity, the color of a probe reflects its intensity, using a color gradient from white (low) to admiral (high). Alternatively, when Strand is turned on, probes on the reverse strand are in parakeet green, while probe on the forward strand is in sky blue.

Probe color: Intensity

Probe color: Strand

Figure 14. Probe color: "intensities" colors probes proportionally to their intensity, "strand" uses colors to indicate probe positioning (an example is shown)

To change any of the colors on the canvas, use the Customize track colors tool. A resulting dialog will help you to pick another color (drop-down button opens the color-picker) (Figure 15).

Figure 15. Customize colors dialog: selecting a drop-down arrow opens the color-picker tool

Track Order

A track can be hidden (meaning it will not be visible) by selecting the red minus, or unhidden by selecting the green plus icon.

The tracks can be reordered by drag and drop.

Figure 16. Track order tool: To change the position of a track drag and drop to the new position. To pin a track to the top / bottom of the canvas, use the up and down arrows. To unpin a track, select the pin icon. A track can be hidden by clicking on the red minus symbol and unhidden by selecting the green plus. Coloured dot by a track names indicates the layers to which the track belongs (an example is shown)

Selection Details

At the bottom of the control panel you will find the Selection details section (Figure 17). It is used to display information on the element selected on the canvas (using the Pointer mode).

Figure 17. Selection details showing information on the element selected on the canvas. The example shows details of a microarray probe. Note the two link-outs ("Browse on UCSC" and "BLAST this sequence")

Additional Assistance

If a variant database is available for the current genome, the variants can be added to the track. To show the variants, point the Variant database control to the database of your choice.

The position of the tracks on canvas can be controlled by using the Track order tool. If you want a track to be visible all the time, i.e. while scrolling up or down, pin it to the top or to the bottom. Below shows the Cytoband track pinnned to the top of the canvas and Reference genome track pinned to the bottom of the canvas. To unpin a track, click on the pin icon (). The track will be unpinned and a message No tracks are pinnned to the top / bottom will appear. To pin a track, drag the track name to the No tracks… message. Alternatively, you can use the green arrows () to pin a track. When you mouse over an arrow, the new position of the track will be highlighted on the canvas; click on the arrow to accept.

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

Reference genome
our support page