High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI).
BACKGROUND
High-throughput technology could generate thousands to millions biomarker measurements in one experiment. However, results from high throughput analysis are often barely reproducible due to small sample size. Different statistical methods have been proposed to tackle this "small n and large p" scenario, for example different datasets could be pooled or integrated together to provide an effective way to improve reproducibility. However, the raw data is either unavailable or hard to integrate due to different experimental conditions, thus there is an emerging need to develop a method for "knowledge integration" in high-throughput data analysis.


RESULTS
In this study, we proposed an integrative prescreening approach, SKI, for high-throughput data analysis. A new rank is generated based on two initial ranks: (1) knowledge based rank; and (2) marginal correlation based rank. Our simulation shows the SKI outperforms other methods without knowledge-integration in terms of higher true positive rate given the same number of variables selected. We also applied our method in a drug response study and found its performance to be better than regular screening methods.


CONCLUSIONS
The proposed method provides an effective way to integrate knowledge for high-throughput analysis. It could easily implemented with our provided R package named SKI.
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.|SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.
Cong
C
Liu
SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.|Department of Bioinformatics and Biostatistics, College of Life Science, Shanghai Jiao Tong University, Shanghai, China.
Jianping
J
Jiang
SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.|Department of Bioinformatics and Biostatistics, College of Life Science, Shanghai Jiao Tong University, Shanghai, China.|Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China.
Jianlei
J
Gu
SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China.|Department of Bioinformatics and Biostatistics, College of Life Science, Shanghai Jiao Tong University, Shanghai, China.
Zhangsheng
Z
Yu
SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China. neowangtao@sjtu.edu.cn.|Department of Bioinformatics and Biostatistics, College of Life Science, Shanghai Jiao Tong University, Shanghai, China. neowangtao@sjtu.edu.cn.
Tao
T
Wang
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA. huilu.bioinfo@gmail.com.|SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China. huilu.bioinfo@gmail.com.|Department of Bioinformatics and Biostatistics, College of Life Science, Shanghai Jiao Tong University, Shanghai, China. huilu.bioinfo@gmail.com.|Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China. huilu.bioinfo@gmail.com.
Hui
H
Lu
D003627:Data Interpretation, Statistical; D023281:Genomics / Q000379:methods*; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics; D018511:Systems Integration; D016896:Treatment Outcome
Dimension reduction; Knowledge integration; SKI; Sure independence screening; Variable selection