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Overview

iDEP (integrated Differential Expression and Pathway analysis) is a web-based tool for analyzing RNA-seq data, available at http://ge-lab.org/idep/. It reads in gene-level read counts or FPKM data, performs exploratory data analysis (EDA), differential expression, and pathway analysis. iDEP also accepts DNA microarray data or other gene-level expression data, such as those from Chip-seq or proteomics studies.

iDEP is a user-friendly Shiny app based powered by many widely-used R/Bioconductor packages for analyzing gene expression data. For EDA, it performs hierarchical clustering, k-means clustering, and principal component analysis (PCA). iDEP detects differentially expressed genes using the limma and DESeq2 packages. For a group of co-expressed genes, it identifies enriched gene ontology (GO) terms as well as transcription factor binding motifs in promoter sequences. Pathway analysis can be performed using GSEA(Gene Set Enrichment Analysis), PAGE (Parametric Analysis of Gene Set Enrichment), or ReactomePA (Reactome Pathway Analysis). Gene expression data can be visualized on KEGG pathway diagrams using pathview. We essentially packed all of the R/Bioconductor packages we often use for analyzing gene expression data into a graphical user interface (GUI).

iDEP can recognize 159 types of common gene IDs from 111 species. It has a knowledge-base derived from the annotation of 69 metazoa and 42 plant genomes in Ensembl as of 11/15/2016. In addition to gene ontology (GO) annotation from Ensembl, additional data are retrieved from KEGG, Reactome, MSigDB (human), GSKB (mouse), and araPath (arabidopsis).

Together with ultra-fast quantification methods like Kallisto or platforms like Galaxy, it is now possible to complete the analyses of RNA-seq data in hours, from raw sequences to pathways, on your laptop and under GUI.

For feedbacks or data contributions (genes and GO mapping of any species), please contact us, or visit our homepage. Send us suggestions or any error message to help us improve iDEP.