Characteristic Direction Approach to Identify Differentially Expressed Genes

TL;DR

The paper introduces the Characteristic Direction, a geometrical method that improves the identification of differentially expressed genes from genome-wide expression data, outperforming traditional t-tests across multiple case studies.

Contribution

It presents a novel geometrical approach called the Characteristic Direction for identifying differentially expressed genes, demonstrating superior performance over t-tests.

Findings

More relevant genes identified with Characteristic Direction

Better matching of transcription factor targets in case studies

Improved enrichment analysis results

Abstract

Genome-wide gene expression profiles, as measured with microarrays or RNA-Seq experiments, have revolutionized biological and biomedical research by providing a quantitative measure of the entire mRNA transcriptome. Typically, researchers set up experiments where control samples are compared to a treatment condition, and using the t-test they identify differentially expressed genes upon which further analysis and ultimately biological discovery from such experiments is based. Here we describe an alternative geometrical approach to identify differentially expressed genes. We show that this alternative method, called the Characteristic Direction, is capable of identifying more relevant genes. We evaluate our approach in three case studies. In the first two, we match transcription factor targets determined by ChIP-seq profiling with differentially expressed genes after the same transcription factor knockdown or over-expression in mammalian cells. In the third case study, we evaluate the quality of enriched terms when comparing normal epithelial cells with cancer stem cells. In conclusion, we demonstrate that the Characteristic Direction approach is much better in calling the significantly differentially expressed genes and should replace the widely currently in used t-test method for this purpose. Implementations of the method in MATLAB, Python and Mathematica are available at: http://www.maayanlab.net/CD.