Name
Title | ||
|---|---|---|
Detection of Cell Types from Single-cell RNA-seq Data using Similarity via Kernel Preserving Learning Embedding |
Abstract | ||
|---|---|---|
The recent advances in single-cell sequencing techniques allow us to study biological issues on cell levels. Detecting cell types from scRNA-seq data analysis is important and meaningful. However, high-level noise and the nonlinearity and sparsity of scRNA-seq data are great challenges. In this paper, we propose a cell-type detection algorithm preserving the overall cell relations named POCR to analyze scRNA-seq data. POCR utilizes a kernel embedding similarity measure to calculate cell-to-cell similarity, by minimizing the reconstruction error of a kernel matrix, rather than the reconstruction error of the original data adopted by other similarity metrics. According to the scale of scRNA-seq datasets, we select Gaussian kernel or linear kernel to calculate the embedding. We then adopt spectral clustering to detect the cell types based on the learned cell-to-cell similarity. The results are further visualized to demonstrate the effectiveness of the cell-type detection algorithm POCR. Further analysis shows that the learned similarity could improve the clustering and visualization of cell types in scRNA-seq data. Our proposed algorithm is compared with five other state-of-the-art cell subtype detection methods. The effectiveness of the algorithms is evaluated by two criteria: ARI and NMI. The experiments show that POCR achieves accurate and robust performance across different scRNA-seq data. Our python implementation of POCR is available at https://github.com/ZeMing-Liu/POCR. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BIBM47256.2019.8983395 | 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
Keywords | Field | DocType |
single-cell RNA sequencing,identification of cell subtypes,kernel-based similarity | Kernel (linear algebra),Spectral clustering,Embedding,Pattern recognition,Similarity measure,Computer science,Visualization,Artificial intelligence,Cluster analysis,Gaussian function,Machine learning,Python (programming language) | Conference |
ISSN | ISBN | Citations |
2156-1125 | 978-1-7281-1868-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeming Liu | 1 | 0 | 0.68 |
| Feng Liu | 2 | 85 | 17.02 |
| Chengzhi Hong | 3 | 0 | 0.34 |
| Meng Gao | 4 | 0 | 0.34 |
| Yi-ping Phoebe Chen | 5 | 1060 | 128.42 |
| Shichao Liu | 6 | 9 | 2.56 |
| Wen Zhang | 7 | 3 | 1.75 |