Recursive Random Lasso (RRLasso) for Identifying Anti-Cancer Drug Targets

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess sev...

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Bibliographic Details
Published in:PloS one 2015-11, Vol.10 (11), p.e0141869-e0141869
Main Authors: Park, Heewon, Imoto, Seiya, Miyano, Satoru
Format: Article
Language:English
Subjects:
Analysis
Antineoplastic Agents - pharmacology
Antineoplastic Agents - therapeutic use
Bioinformatics
Biological effects
Biomarkers
Breast cancer
Cancer
Cancer genetics
Colorectal cancer
Computational Biology - methods
Computer simulation
Data analysis
Data processing
Datasets
Dimensional analysis
Drug therapy
Epigenetics
Gene expression
Genes
Genomes
Genomics
Information management
Medical research
Molecular Targeted Therapy
Monte Carlo Method
Monte Carlo methods
Neoplasms - drug therapy
Neoplasms - genetics
Ovarian cancer
Parameter estimation
Regression analysis
Regularization
Regularization methods
Science
Sensitivity analysis
Statistical analysis
Statistics
Variables
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Summary:Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the "Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project" show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0141869