| Chapter title |
Effective Removal of Noisy Data Via Batch Effect Processing
|
|---|---|
| Chapter number | 14 |
| Book title |
Bioinformatics in MicroRNA Research
|
| Published in |
Methods in molecular biology, May 2017
|
| DOI | 10.1007/978-1-4939-7046-9_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7044-5, 978-1-4939-7046-9
|
| Authors |
Ryan G. Benton Ph.D., Ryan G. Benton |
| Editors |
Jingshan Huang, Glen M. Borchert, Dejing Dou, Jun (Luke) Huan, Wenjun Lan, Ming Tan, Bin Wu |
| Abstract |
In order to have faith in the analysis of data, a key factor is to have confidence that the data is reliable. In the case of microRNA, reliability includes understanding the collection methods, ensuring that the analysis is appropriate, and ensuring that the data itself is accurate. A key element in ensuring data accuracy is the removal of noise. While there can be several sources of noise, a common source of noise is the batch effect, which can be defined as systematic variability in the data caused by non-biological factors. This chapter will present various techniques designed to remove variability caused by batch effects and the potential effectiveness. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 29% |
| Student > Ph. D. Student | 1 | 14% |
| Student > Postgraduate | 1 | 14% |
| Other | 1 | 14% |
| Unknown | 2 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 29% |
| Chemical Engineering | 1 | 14% |
| Physics and Astronomy | 1 | 14% |
| Unknown | 3 | 43% |