Statistically controlled identification of differentially expressed genes in one-to-one cell line comparisons of the CMAP database for drug repositioning

Journal of Translational Medicine

Research

Haidan Yan¹  Huaping Liu¹  Jun He¹  Qingzhou Guan¹  Rou Chen¹  Xiangyu Li¹  Hao Cai¹  Weicheng Zheng¹  Zheng Guo²  Xianlong Wang²  Kai Song³ 

[1] Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, 350122, Fuzhou, China;Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, 350122, Fuzhou, China;Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, 350122, Fuzhou, China;Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, 150086, Harbin, China;

关键词: The Connectivity Map;    Differentially expressed genes;    Drug repositioning;    Phenformin;    Metformin;   

DOI  :  10.1186/s12967-017-1302-9

 received in 2017-03-28, accepted in 2017-09-19,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe Connectivity Map (CMAP) database, an important public data source for drug repositioning, archives gene expression profiles from cancer cell lines treated with and without bioactive small molecules. However, there are only one or two technical replicates for each cell line under one treatment condition. For such small-scale data, current fold-changes-based methods lack statistical control in identifying differentially expressed genes (DEGs) in treated cells. Especially, one-to-one comparison may result in too many drug-irrelevant DEGs due to random experimental factors. To tackle this problem, CMAP adopts a pattern-matching strategy to build “connection” between disease signatures and gene expression changes associated with drug treatments. However, many drug-irrelevant genes may blur the “connection” if all the genes are used instead of pre-selected DEGs induced by drug treatments.MethodsWe applied OneComp, a customized version of RankComp, to identify DEGs in such small-scale cell line datasets. For a cell line, a list of gene pairs with stable relative expression orderings (REOs) were identified in a large collection of control cell samples measured in different experiments and they formed the background stable REOs. When applying OneComp to a small-scale cell line dataset, the background stable REOs were customized by filtering out the gene pairs with reversal REOs in the control samples of the analyzed dataset.ResultsIn simulated data, the consistency scores of overlapping genes between DEGs identified by OneComp and SAM were all higher than 99%, while the consistency score of the DEGs solely identified by OneComp was 96.85% according to the observed expression difference method. The usefulness of OneComp was exemplified in drug repositioning by identifying phenformin and metformin related genes using small-scale cell line datasets which helped to support them as a potential anti-tumor drug for non-small-cell lung carcinoma, while the pattern-matching strategy adopted by CMAP missed the two connections. The implementation of OneComp is available at https://github.com/pathint/reoa.ConclusionsOneComp performed well in both the simulated and real data. It is useful in drug repositioning studies by helping to find hidden “connections” between drugs and diseases.

【 授权许可】

CC BY   
© The Author(s) 2017