  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.
