A novel matched-pairs feature selection method considering with tumor purity for differential gene expression analyses.

Tissue-based gene expression data analyses, while most powerful, represent a significantly more challenging problem compared to cell-based gene expression data analyses, even for the simplest differential gene expression analyses. The result in determining if a gene is differentially expressed in tumor vs. non-tumorous control tissues does not only depend on the two expression values but also on the percentage of the tissue cells being tumor cells, i.e., the tumor purity. We developed a novel matched-pairs feature selection method, which takes into full consideration of the tumor purity when deciding if a gene is differentially expressed in tumor vs. control experiments, which is simple, effective, and accurate. To evaluate the validity and performance of the method, we have compared it with four published methods using both simulated datasets and actual cancer tissue datasets and found that our method achieved better performance with higher sensitivity and specificity than the other methods. Our method was the a matched-pairs feature selection method on gene expression analysis under matched case-control design which takes into consideration the tumor purity information, which can set a foundation for further development of other gene expression analysis needs.