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

Was this helpful?

Export as PDF
  1. Partek Flow
  2. Blog Posts

Pathway Analysis: ANOVA vs. Enrichment Analysis

PreviousSingle Cell Multiomics Analysis: Strategies for IntegrationNextStudying Immunotherapy with Multiomics: Simultaneous Measurement of Gene and Protein

Last updated 4 months ago

Was this helpful?

One of the main steps in nearly every bulk RNA-Seq or single cell RNA-Seq project is some sort of statistical testing to compare gene expression levels across samples. After which a list of significant genes is generated by applying a filter strategy, the most common being fold change > |2| and false discovery rate (FDR) < 0.05 (the exact values depend on the research question).

There are different ways to interpret the resulting list of significant genes. The most straightforward is to perform enrichment analysis, which identifies gene sets (e.g., pathways or gene ontology categories) that are overrepresented in the list of the significant genes. Then a 2 ✕ 2 contingency table is created for each gene set: the number of genes present in the list of significant genes and in the gene set and the number of genes present in the list of significant genes but not in the gene set, etc. A statistical test (like Fisher’s exact test) can be invoked on the contingency table. Most researchers transform the p-value into an enrichment score (enrichment score = -ln(p-value)) to make it easier to read (e.g., 6.9, instead of 0.001). Let’s have a look at a simple hypothetical example; a list of significant genes consists of 291 genes. Of those genes, 35 (12%) belong to a particular gene set. The set itself consists of 80 genes and almost half (35/80) are present in the list of significant genes (Table 1). Based on the contingency table, Fisher's exact test statistic p-value is 8.28×10-24. Converted to the enrichment score, the value is 53.15. In other words, the number of genes in gene sets that are also in the list of the significant genes exceeds the expectation.

Genes in list
Genes not in list
Total

Genes in set

35

45

80

Genes not in set

256

4,929

5,185

Total

291

4,974

5,265

To illustrate biological interpretation, we used a spatial transcriptomics data set, consisting of two human prostate samples: a cancer sample and a normal sample harvested from another individual. The samples were processed by the 10x Genomics Visium platform and are . Using the graph-based clustering approach, tissue spots were classified as cancer or normal. Differential expression testing was performed and a list of significant genes was created using fold change > |2| with a false discovery rate (FDR) < 0.05. The list was then interpreted by pathway enrichment with as the pathways database.

Figure 1 shows the top ten enriched pathways. The first pathway really stands out as a “focal adhesion”. Two additional pathways relate to the adhesion of cells to either other cells or components of the extracellular matrix; dysregulation of genes involved in adhesion is a well-known feature of prostate cancer (particularly important for metastases).

Although the enrichment results make sense, they fail to reveal all the biological nuances. It is important to note that enrichment focuses on genes that show considerable differences in expression level between the conditions, while the genes with subtle changes simply go unnoticed. But what if within a pathway several genes are slightly up-regulated to the baseline condition? For instance, in a signaling pathway, even slight changes in expression levels may have profound biological consequences.

One way to address the limitation of gene set enrichment is to use a strategy that could be described as “differential set expression”. For any gene set (e.g., a pathway) expression values of all the genes within the set are added up and then those sums are compared between the samples (basically, pathway ANOVA). This is the principle behind Pathway Analysis as implemented in Partek Flow (Figure 2).

Using the same project to illustrate the concept, cancer and normal samples were compared using Pathway Analysis. The resulting pathways are sorted by fold change (descending) and the top ten pathways are shown in Figure 3.

Examination of one of the pathways (ɑ-linolenic acid metabolism, Figure 4) sheds light on the difference between pathway analysis and pathway enrichment. All the genes in the pathway are indeed expressed at a higher level in the cancer samples (blue), but the difference in terms of fold change (per gene) is modest, at best, and below the usual cut-off value of 2. Hence, it is not surprising that the enrichment score value for the ɑ-linolenic acid metabolism pathway is 1.34 (corresponding to Fisher’s exact test p-value of 0.26).

In summary, enrichment is a valid and valuable approach to contextualize gene lists (sometimes it is the only available method, e.g., if a gene list is obtained from a publication), but it has limitations. In the current example, pathway enrichment yielded valuable results, but additional biological insight was obtained when applying pathway ANOVA. We encourage you to try it with your next experiment and share your experience with us.

If you are not familiar with the biology of prostate cancer, these results may be unexpected: half of the pathways with the highest fold change values relate to lipid metabolism. It turns out, dysregulation of lipid metabolism is one of the hallmarks of prostate cancer (e.g., ).

Poulose et al., Nat Gen 2018
available here
KEGG pathways
Figure 1. Pathway enrichment results invoked on a list of differentially expressed genes between normal human prostate tissue and cancer tissue (top ten pathways are shown)
Figure 2. Differential Analysis section of the toolbox in Partek Flow, showing the Pathway Analysis tool
Figure 3. Pathway analysis results obtained by comparing a normal human prostate and cancer tissues (results sorted by descending fold change, top ten pathways are shown)
Figure 4. Expression levels of genes in the ɑ-linolenic acid metabolism pathway in the cancer (blue dots) and normal sample (red dots)