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Screening genome-scale genetic and epigenetic data
| Content Provider | Scilit |
|---|---|
| Author | Zhang, Hongmei |
| Copyright Year | 2020 |
| Description | Data mining based on cluster analyses in general is to visualize the patterns in the data and is descriptive, although hypotheses testing on cluster profile differentiation are usually conducted as well. This Chapter introduces commonly used non-parametric and parametric clustering methods. In both non-parametric and parametric clustering methods, the objects to be clustered (clustering objects) can be rows (subjects) or columns (variables) determined by research interest. Expression data from arrays and RNAseq are utilized to demonstrate the methods. Book Name: Analyzing High-Dimensional Gene Expression and DNA Methylation Data with R |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2015-0-79093-5&isbn=9780429155192&doi=10.1201/9780429155192-4&format=pdf |
| Ending Page | 55 |
| Page Count | 17 |
| Starting Page | 39 |
| DOI | 10.1201/9780429155192-4 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-05-14 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Analyzing High-dimensional Gene Expression and Dna Methylation Data with R Mathematical and Computational Biology Differentiation Epigenetic Interest Rnaseq Columns Chapter Rows Clustering Objects Parametric Clustering |
| Content Type | Text |
| Resource Type | Chapter |
