DFI: gene feature discovery in RNA-seq experiments from multiple sources

Differential expression detection for RNA-seq experiments is often biased by normalization algorithms due to their sensitivity to parametric assumptions on the gene count distributions, extreme values of gene expression, gene length and total number of sequence reads.

To overcome limitations of current methodologies, we developed Differential Feature Index (DFI), a non-parametric method for characterizing distinctive gene features across any number of diverse RNA-seq experiments without inter-sample normalization. Validated with qRT-PCR datasets, DFI accurately detected differentially expressed genes regardless of expression levels and consistent with tissue selective expression. Accuracy of DFI was very similar to the currently accepted methods: EdgeR, DESeq and Cuffdiff.

In this study, we demonstrated that DFI can efficiently handle multiple groups of data simultaneously, and identify differential gene features for RNA-Seq experiments from different laboratories, tissue types, and cell origins, and is robust to extreme values of gene expression, size of the datasets and gene length.