کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6487051 | 1416276 | 2017 | 33 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel feature selection for RNA-seq analysis
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this study, we presented a novel feature selection method: nonnegative singular value approximation (NSVA) to enhance RNA-seq differential expression analysis by taking advantage of RNA-seq count data's non-negativity. As a variance-based feature selection method, it selects genes according to its contribution to the first singular value direction of input data in a data-driven approach. It demonstrates robustness to depth bias and gene length bias in feature selection in comparison with its five peer methods. Combining with state-of-the-art RNA-seq differential expression analysis, it contributes to enhancing differential expression analysis by lowering false discovery rates caused by the biases. Furthermore, we demonstrated the effectiveness of the proposed feature selection by proposing a data-driven differential expression analysis: NSVA-seq, besides conducting network marker discovery.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Biology and Chemistry - Volume 71, December 2017, Pages 245-257
Journal: Computational Biology and Chemistry - Volume 71, December 2017, Pages 245-257
نویسندگان
Henry Han,