Pathway Enrichment Analysis

Pathway enrichment analysis

Necessary Disclaimers and Legal

The user is responsible for reviewing and complying with the license requirements of the software, notebooks, and data referenced in this documentation.

Users are responsible for the costs associated with analyzing notebooks and its storage in their project spaces.

Instance type availability and pricing are subject to the contract between the user or the user’s organization and DNAnexus.

Overview

Pathway enrichment analysis is a computational method used to identify biological pathways or gene sets that are statistically overrepresented in a list of genes of interest, such as differentially expressed genes from an RNA-seq experiment. By mapping genes to known biological pathways, enrichment analysis provides mechanistic insight into the underlying biology driving observed gene expression changes.

DNAnexus platform provides two notebooks for pathway enrichment analysis:

• ClusterProfiler: a comprehensive R package for statistical analysis and visualization of functional profiles, supporting Gene Ontology (GO), Reactome, and MSigDB pathway databases via Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA).

• gget enrichr: a Python-based tool that queries the Enrichr database directly, enabling fast and flexible enrichment analysis with support for custom background gene sets.

Citations for ClusterProfiler and gget

ClusterProfiler:

• Using clusterProfiler to characterize multiomics data (2024)

• clusterProfiler: an R package for comparing biological themes among gene clusters (2012)

• Official documentation: Biomedical Knowledge Mining using GOSemSim and clusterProfiler

gget:

• Efficient querying of genomic reference databases with gget (2023)

• Official documentation: gget Introduction

Running analyses on DNAnexus

Copying enrichment analysis notebooks into a project

To explore the demo analysis, copy the notebooks from the public project into your own project space. Follow these steps to copy the notebooks:

1. Create a project for your enrichment analysis, billed to your own organization. Tutorials on project setup can be found here.

2. Navigate to the Resources tab, locate the project titled "Public Datasets AWS US (East)," and select the "ClusterProfiler" folder.

1. Select the two notebooks and one example dataset.

2. Click "Copy" in the top-right menu and select the project created in Step 1.

3. Return to your project space to begin exploring the notebooks.

4. To run the notebooks, refer to the JupyterLab section of the Academy documentation, which includes instructions for the standard environment.

Example of enrichment analysis notebooks

We have provided two notebooks for the demo enrichment analysis: "ClusterProfiler_ReactomePA_R_dxjupyterlab.ipynb" and "gget_enrich_notebook_dxjupyterlab.ipynb". Both are optimized for the JupyterLab with Python, R, Stata, ML, Image Processing environment (version 2.11).

When starting the job, use the snapshot snapshot-target_validation-jupyterlab-2026-04-08.tar.gz, which is available in the "Notebook_snapshot" folder within "Public Datasets AWS US (East)".

To use the snapshot, choose this snapshot in the Snapshot box when you start the JupyterLab.

After launching, select the appropriate kernel and instance type from the table below.

Notebook	Instance Type	Kernel
gget_enrich_notebook_dxjupyterlab.ipynb	mem1_ssd1_v2_x16
ClusterProfiler_ReactomePA_R_dxjupyterlab.ipynb	mem1_ssd1_v2_x16	Clusterprofile (R 4.4.3)

Note: Notebook filenames may include a timestamp. If multiple versions are available, please use the most recent one.