To search, Click below search items. |
| All Published Papers Search Service |
|
|
|
Title |
Dimensionality Reduction of RNA-Seq Data |
|
Author |
Isra Al-Turaiki |
| Citation |
Vol. 21 No. 3 pp. 31-36 |
|
Abstract |
RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results. |
|
Keywords |
Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Self-Organizing Maps (SOM), RNA-Seq, Dimensionality Reduction |
|
URL |
