Self-supervised Low-pass Filted Graph Clustering Networks for Single Cell RNA Sequencing Data
-
摘要: 近年兴起的单细胞RNA测序(single-cell RNA sequencing,scRNA-seq)技术可以测出每个单细胞的转录组表达量,利用单细胞RNA测序数据可以将具有相似生物学状态或相似功能的单细胞聚类成同一细胞群,从而指导下游生物学分析。针对单细胞RNA测序数据的复杂、高维、携带大量噪声的特点,提出了一种自监督低通滤波图聚类网络(Self-supervised Low-pass Filted Graph Clustering Network,SLFGCN)算法用于单细胞RNA测序数据的聚类研究。该方法首先构建了一个低通滤波的图卷积网络,以细胞为节点构建图网络结构,在谱域的图信息经过低通滤波图卷积操作后,获得更加平滑的图信号,即同一簇的细胞提取到更相似的节点特征,从而利于单细胞RNA测序数据聚类;然后,通过图自编码模型,建立自监督模块优化模型,进一步优化聚类效果。通过在单细胞RNA测序数据上与相关算法的对比实验结果表明,提出的方法能更好地获取单细胞RNA表达数据的内在特征,改善聚类效果。Abstract: Single-cell RNA sequencing (scRNA-seq) provides high-resolution observation tools at the cell level for biological domains, such as embryonic development, cancer evolution and cell differentiation. A key step in using scRNA-seq data is to cluster cells with similar biological functions into one group. However, the current clustering methods are not able to perform the clustering task well in a large number of high-dimensional and complex scRNA-seq data, and don’t use the structural relationship information between samples. Here, we propose a GCN based deep clustering framework, named Self-supervised Low-pass Filted Graph Clustering Networks (SLFGCN). Firstly, a new propagation method of graph convolutional network is proposed. For the proposed method, the graph information in the spectral domain passes through the frequency response function of the low-pass filter to obtain smoother node feature representation, which is more conducive to the clustering task. Secondly, we use the self-supervised module to optimize the network based on the representation learned from the low-pass filted GCN module and the representation learned from the graph auto-encoders module, which can obtain better clustering effect. Experiments indicate that our model outperforms the state-of-the-art methods in various evaluation metrics on real datasets. Further, the visualization results show that our model provides representations generating better intra-cluster compactness and inter-cluster separability.
-
Keywords:
- Single-cell RNA sequencing /
- Graph convolutional network /
- Clustering /
- Deep learning
-
-
[1] Navin, N. et al. Tumor evolution inferred by single cell sequencing. Nature 472, 90-94(2011).
[2] Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187-1201(2015).
[3] Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176-182(2018).
[4] Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974-1980(2015)
[5] Wang, B., Zhu, J., Pierson, E., Ramazzotti, D. & Batzoglou, S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat. Methods 14, 414-416(2017).
[6] G. Eraslan, L. M. Simon, M. Mircea, N. S. Mueller, and F. J. Theis, "Single-cell RNA-seq denoising using a deep count autoencoder", Nat Commun, vol. 10, no. 1, p. 390, Jan 232019, doi: 10.1038/s41467-018-07931-2.
[7] T. Tian, J. Wan, Q. Song, and Z. Wei, "Clustering single-cell RNA-seq data with a model-based deep learning approach", Nature Machine Intelligence, vol. 1, no. 4, pp. 191-198, 2019, doi: 10.1038/s42256-019-0037-0.
[8] T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks", arXiv preprint arXiv:1609.02907, 2016.
[9] M. Defferrard, X. Bresson, and P. Vandergheynst, "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering", 2016.
[10] P. Velikovi, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, "Graph Attention Networks", 2017.
[11] S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, "Attention based spatial-temporal graph convolutional networks for traffic flow forecasting", in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp. 922-929.
[12] Song, S. Zheng, Z. Niu, Z.-H. Fu, Y. Lu, and Y. Yang, "Communicative Representation Learning on Attributed Molecular Graphs", presented at the IJCAI, 2020.
[13] D. Bo, X. Wang, C. Shi, M. Zhu, E. Lu, and P. Cui, "Structural Deep Clustering Network", presented at the Proceedings of The Web Conference 2020, 2020.
[14] J. Rao, X. Zhou, Y. Lu, H. Zhao, and Y. Yang, "Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks", biorxiv, 2020, doi: 10.1101/2020.02.05.935296.
[15] Zeng Y, Zhou X, Rao J, et al. Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020.
[16] Kipf T N, Welling M. Variational Graph Auto-Encoders[J]. 2016.
[17] T. Kipf and M. Welling, "Variational graph auto-encoders", NIPS Workshop on Bayesian Deep Learning, 2016.
[18] M. Defferrard, X. Bresson, and P. Vandergheynst, "Convolutional neural networks on graphs with fast localized spectral filtering", in Advances in Neural Information Processing Systems, 2016, pp. 3844-3852.
[19] Fan RK Chung and Fan Chung Graham. Spectral graph theory. Number 92. American Mathematical Society, 1997.
[20] Chen M, Wei Z, Huang Z, et al. Simple and Deep Graph Convolutional Networks[J]. 2020.
[21] Li Q, Han Z, Wu X M. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning[J]. 2018.
[22] M. Krzak, Y. Raykov, A. Boukouvalas, L. Cutillo, and C. Angelini, "Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA Sequencing Clustering Methods", Front Genet, vol. 10, p. 1253, 2019, doi: 10.3389/fgene.2019.01253.
[23] J. M. Zhang, J. Fan, H. C. Fan, D. Rosenfeld, and D. N. Tse, "An interpretable framework for clustering single-cell RNA-Seq datasets", BMC Bioinformatics, vol. 19, no. 1, p. 93, Mar 92018, doi: 10.1186/s12859-018-2092-7.
[24] V. Y. Kiselev et al., "SC3:consensus clustering of single-cell RNA-seq data", Nat Methods, vol. 14, no. 5, pp. 483-486, May 2017, doi: 10.1038/nmeth.4236.
[25] L. van der Maaten and G. Hinton, "Visualizing data using t-SNE,"Journal of Machine Learning Research, vol. 9, no. 86, pp. 2579-2605, 2008.
计量
- 文章访问数: 760
- HTML全文浏览量: 73
- PDF下载量: 74