期刊
FRONTIERS IN GENETICS
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2018.00321
关键词
differentially expressed genes; tumor purity; generalized least square; the Wald test; generalized least square
资金
- Hainan Provincial Natural Science Foundation of China [618MS057]
- National Natural Science Foundation of China [61762034, 11661003]
- Natural Science Foundation of Hunan, China [2018JJ2461]
- Science and Technology Project of Education Department of Jiangxi Province [GJJ170445]
- Key Program of Hunan Provincial Education Department [15A026]
- General Program of Hunan Provincial Philosophy and Social Science Planning Fund office [15YBA035]
Identifying differentially expressed genes (DEGs) between tumor and normal samples is critical for studying tumorigenesis, and has been routinely applied to identify diagnostic, prognostic, and therapeutic biomarkers for many cancers. It is well-known that solid tumor tissue samples obtained from clinical settings are always mixtures of cancer and normal cells. However, the tumor purity information is more or less ignored in traditional differential expression analyses, which might decrease the power of differential gene identification or even bias the results. In this paper, we have developed a novel differential gene calling method called DECtp by integrating tumor purity information into a generalized least square procedure, followed by the Wald test. We compared DECtp with popular methods like t-test and limma on nine simulation datasets with different sample sizes and noise levels. DECtp achieved the highest area under curves (AUCs) for all the comparisons, suggesting that cancer purity information is critical for DEG calling between tumor and normal samples. In addition, we applied DECtp into cancer and normal samples of 14 tumor types collected from The Cancer Genome Atlas (TCGA) and compared the DEGs with those called by limma. As a result, DECtp achieved more sensitive, consistent, and biologically meaningful results and identified a few novel DEGs for further experimental validation.
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