Robust gene selection methods using weighting schemes for microarray data analysis

BMC Bioinformatics

Methodology Article

Jongwoo Song¹  Suyeon Kang¹ 

[1] Department of Statistics, Ewha Womans University, Seoul, South Korea;

关键词: Microarray data;    Gene selection method;    Significance analysis of microarrays;    Noisy data;    Robustness;    False discovery rate;   

DOI  :  10.1186/s12859-017-1810-x

 received in 2017-03-23, accepted in 2017-08-27,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundA common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates.ResultsWe have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays.ConclusionsThe results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

【 授权许可】

CC BY   
© The Author(s). 2017

【 图 表 】

12864_2016_2821_Article_IEq13.gif

12864_2016_2682_Article_IEq32.gif

12888_2017_1557_Article_IEq9.gif

12864_2016_2682_Article_IEq31.gif

12888_2017_1557_Article_IEq8.gif

12864_2017_4363_Article_IEq6.gif

12864_2015_2055_Article_IEq55.gif

12864_2017_4363_Article_IEq5.gif

12864_2017_4020_Article_IEq26.gif

12864_2017_3821_Article_IEq1.gif

12864_2017_3500_Article_IEq6.gif

12864_2017_3492_Article_IEq27.gif

12864_2017_3492_Article_IEq26.gif

12864_2017_3487_Article_IEq68.gif

12864_2017_3492_Article_IEq24.gif

12864_2017_3487_Article_IEq66.gif

12864_2016_3440_Article_IEq14.gif

12864_2016_2380_Article_IEq1.gif

12864_2015_2198_Article_IEq18.gif

12864_2017_4133_Article_IEq42.gif

12864_2017_4269_Article_IEq3.gif

12864_2015_2198_Article_IEq15.gif

12864_2017_3605_Article_IEq20.gif

12864_2017_4133_Article_IEq38.gif

12864_2017_3487_Article_IEq58.gif

12864_2017_3487_Article_IEq57.gif

12864_2017_3487_Article_IEq56.gif

12864_2017_3487_Article_IEq55.gif

12864_2015_2073_Article_IEq8.gif

12864_2017_4346_Article_IEq1.gif

12864_2015_2213_Article_IEq1.gif

12864_2017_4359_Article_IEq9.gif

12864_2016_3440_Article_IEq72.gif

12864_2017_3920_Article_IEq7.gif

12864_2015_2129_Article_IEq33.gif