Spiking Neural Networks (SNNs) are the promising models of neuromorphic vision recognition. The mean square error (MSE) and cross-entropy (CE) losses are widely applied to supervise the training of SNNs on neuromorphic datasets. However, the relevance between the output spike counts and predictions is not well modeled by the existing loss functions. This paper proposes a Spike Count Maximization (SCM) training approach for the SNN-based neuromorphic vision recognition model based on optimizing the output spike counts. The SCM is achieved by structural risk minimization (SRM) and a specially designed spike counting loss. The spike counting loss counts the output spikes of the SNN by using the L0-norm, and the SRM maximizes the distance between the margin boundaries of the classifier to ensure the generalization of the model. The SCM is non-smooth and non-differentiable, and we design a two-stage algorithm with fast convergence to solve the problem. Experiment results demonstrate that the SCM performs satisfactorily in most cases. Using the output spikes for prediction, the accuracies of SCM are 2.12%~16.50% higher than the popular training losses on the CIFAR10-DVS dataset. The code is available at https://github.com/TJXTT/SCM-SNN.

MOTIVATION: Single-cell DNA methylation sequencing can assay DNA methylation at single-cell resolution. However, incomplete coverage compromises related downstream analyses, outlining the importance of imputation techniques. With a rising number of cell samples in recent large datasets, scalable and efficient imputation models are critical to addressing the sparsity for genome-wide analyses. RESULTS: We proposed a novel graph-based deep learning approach to impute methylation matrices based on locus-aware neighboring subgraphs with locus-aware encoding orienting on one cell type. Merely using the CpGs methylation matrix, the obtained GraphCpG outperforms previous methods on datasets containing more than hundreds of cells and achieves competitive performance on smaller datasets, with subgraphs of predicted sites visualized by retrievable bipartite graphs. Besides better imputation performance with increasing cell number, it significantly reduces computation time and demonstrates improvement in downstream analysis. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/yuzhong-deng/graphcpg.git.

Advances in high throughput sequencing have enabled DNA methylation profiling at single-cell resolution. The generation of single-cell methylation sequencing (scM-Seq) data provides unprecedented opportunities for a comprehensive dissection of epigenetic heterogeneity. An important step of exploring epigenetic heterogeneity is clustering cells according to their single-cell methylation profiles. However, the inherent sparsity and stochastic measurement characteristic of the data make it challenging. To this end, we introduce SINCEF, using spectral embedding fusion to reconstruct cell-to-cell pairwise distance for clustering single-cell methylation data. SIN CEF first calculates multiple basic distance matrices to capture cell-to-cell methylation dissimilarity relationships according to the global methylation status. Then it adopts spectral embedding to transform these basic distance matrices into the latent representations, pooling information from the basic distance measures. Finally, it reconstructs a novel distance matrix and implements hierarchical clustering to yield cell partitions. Assessments on several public scM-Seq datasets demonstrated that SINCEF could generate a more appropriate distance matrix to measure the methylation distance between cells, which considerably improved the clustering performance. As an additional benefit, the reconstructed novel distance matrix could help to visually assess the heterogeneity across cell populations through presenting the block structures in the hierarchical clustering heat maps. SINCEF is freely available on GitHub at https://github.com/TQBio/SINCEF.

MOTIVATION: Single-cell DNA methylation sequencing detects methylation levels with single-cell resolution, while this technology is upgrading our understanding of the regulation of gene expression through epigenetic modifications. Meanwhile, almost all current technologies suffer from the inherent problem of detecting low coverage of the number of CpGs. Therefore, addressing the inherent sparsity of raw data is essential for quantitative analysis of the whole genome. RESULTS: Here, we reported CaMelia, a CatBoost gradient boosting method for predicting the missing methylation states based on the locally paired similarity of intercellular methylation patterns. On real single-cell methylation datasets, CaMelia yielded significant imputation performance gains over previous methods. Furthermore, applying the imputed data to the downstream analysis of cell-type identification, we found that CaMelia helped to discover more intercellular differentially methylated loci that were masked by the sparsity in raw data, and the clustering results demonstrated that CaMelia could preserve cell-cell relationships and improve the identification of cell types and cell subpopulations. AVAILABILITY AND IMPLEMENTATION: Python code is available at https://github.com/JxTang-bioinformatics/CaMelia. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.