User Guide
Introduction
This web-based interactive application wraps the popular clusterProfiler package which implements methods to analyze and visualize functional profiles of genomic coordinates, genes and gene clusters.
Users can upload their own differential gene expression (DGE) data from DESeq2 or import data from the upstream Deseq2Shiny app.
This application is meant to provide an intuitive interface for researchers to easily perform Over-representation analysis of GO-Terms and KEGG pathways with no prior programming experience in R.
Visuals produced include dot plots, wordclouds, category net plots, enrichment map plots, GO induced graphs, and enriched KEGG pathway plots using the Pathview package.
The application follows this tutorial closely
See Figure 1 below for example output plots (Click on image to enlarge).
Figure 1: Example plots
Input Data Types
This application accepts the following types of input data:
1. Example data (Demo):
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For demo purposes, you can select “Example data”
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You can follow the steps afterwards to run the analysis mirroring the tutorial in order to get familiar with the app
2. Upload your own data (gene counts):
A .csv/.txt file that contains a table of differential gene expression (DGE) data
Eg. the output of DESeq2
The file can be either comma or tab delimited
The required columns are Gene name/id, Log2 Fold Change, p-adjusted values
You will have to select the column names that match the above required columns
For a sample file, click here
Figure 2: Eg. DGE data file
2. Visualization
Various forms of visualizations are included for either Go/KEGG:
- Bar Plot
- Dot Plot
- Enrichment Plot Map
- Enriched GO induced graph (goplot, GO only)
- Enriched GO induced graph (cnetplot)
- Pathview Plots (KEGG)
- Word Clouds
Upload Data
CSV counts file
Initialize Parameters
EnrichGO object Parameters
Note: if there are more than 20 columns, only the first 20 will show here