Partek
  • Overview
  • Partek Flow
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      • Creating Restricted User Folders within the Partek Flow server
      • Updating Partek Flow
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      • Docker and Docker-compose
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    • 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
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        • SFTP File Transfer Instructions
        • Import single cell data
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        • Partek Flow Uploader for Ion Torrent
        • Importing 10x Genomics .bcl Files
        • Import a GEO / ENA project
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        • Pre-alignment tools
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        • Annotation/Metadata
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          • Annotation report
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        • Pre-analysis tools
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          • Quantify regions
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          • Salmon
        • Filtering
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        • Normalization and scaling
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          • Normalization
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          • Scran deconvolution
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        • Batch removal
          • General linear model
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          • Transcript Expression Analysis - Cuffdiff
          • Troubleshooting
        • Survival Analysis with Cox regression and Kaplan-Meier analysis - Partek Flow
        • Exploratory Analysis
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        • Variant Callers
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          • Annotate Variants
          • Annotate Variants (SnpEff)
          • Annotate Variants (VEP)
          • Filter Variants
          • Summarize Cohort Mutations
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        • Copy Number Analysis (CNVkit)
        • Peak Callers (MACS2)
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      • Data Viewer
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        • Chromosome View
          • Launching the Chromosome View
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          • Visualizing the Results Using Data Tracks
          • Annotating the Results
          • Customizing the View
        • Dot Plot
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      • 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
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    • User Manual
      • Lists
        • Importing a text file list
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        • Starting with a list of genomic regions
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        • Implementation Details
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      • 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
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  1. Partek Flow
  2. User Manual
  3. Task Menu
  4. Correlation

Sample Correlation

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Last updated 7 months ago

Sample correlation plot is a data visualization used to compare a number of variables across two samples. A hypothesis underlying many gene expression experiments (next generation sequencing or microarray) is that most genes/transcripts are not differentially regulated between the conditions, causing most of the data points to fall on the diagonal (i.e. regression line with slope of 1). If that is not the case, a normalization method should be applied before the statistical analysis. Therefore, you may want to run sample correlation plots and your data set before and after the normalization.

Sample correlation in Partek Flow can be performed after quantification by selecting a Gene counts or Transcript counts data node, or on a Normalized counts node in case that you want to assess its effect on the data. The Sample correlation option is visible in the Correlation section of the task menu (Figure 1). The task has no particular setup dialog (and creates no task node), but launches immediately.

When the Sample correlation page opens, you will be asked to select two samples for comparison (Figure 2). The sample in the left box will be shown on the horizontal axis, while the sample in the right box will be shown on the vertical axis. Click on the sample names and then hit OK to proceed.

An example of the resulting scatterplot is in Figure 3. Each dot is a feature (gene/transcript) while the expression values in the two samples can be read off the coordinate axes, in the same units as present in the data node. For instance, if you normalized your RNA-seq data by transcripts per million (TPM), the coordinate axis will give you expression in TPMs. Pearson’s correlation coefficient and the slope of the regression line are in the upper left corner of the plot.

To visualize a different pair of samples, select another sample from the X axis or Y axis list on the left and push Apply.

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Figure 1. Sample correlation tool in the Correlation section of the toolbox
Figure 2. Selecting samples for the initial comparison (an example is shown)
Figure 3. Sample correlation plot. Each dot is a sample, the line is the regression line of sample shown on the y-axis over the sample shown on the x-axis. The plot is based on the same expression units as the data that the plot was invoked on