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We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools. The challenge of simulating multiomic single-cell data is addressed by a probabilistic model.

Background Drug-target interaction prediction is of great significance for narrowing down the scope of candidate medications, and thus is a vital step in drug discovery. Because of the particularity of biochemical experiments, the development of new drugs is not only costly, but also time-consuming. Therefore, the computational prediction of drug target interactions has become an essential way in the process of drug discovery, aiming to greatly reducing the experimental cost and time. Results We propose a learning-based method based on feature representation learning and deep neural network named DTI-CNN to predict the drug-target interactions. We first extract the relevant features of drugs and proteins from heterogeneous networks by using the Jaccard similarity coefficient and restart random walk model. Then, we adopt a denoising autoencoder model to reduce the dimension and identify the essential features. Third, based on the features obtained from last step, we constructed a convolutional neural network model to predict the interaction between drugs and proteins. The evaluation results show that the average AUROC score and AUPR score of DTI-CNN were 0.9416 and 0.9499, which obtains better performance than the other three existing state-of-the-art methods. Conclusions All the experimental results show that the performance of DTI-CNN is better than that of the three existing methods and the proposed method is appropriately designed.

The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics. Despite being widely performed in exploring cell heterogeneity and gene expression stochasticity, single cell RNA-seq analysis is complicated by excess zero counts (dropouts). Here, Li and Li develop scImpute for statistical imputation of dropouts in scRNA-seq data.

Three papers in this issue of Nature use highly sensitive ChIP–seq assays to describe the dynamic patterns of histone modifications during early mouse embryogenesis, showing that oocytes have a distinctive epigenome and providing insights into how the maternal gene expression program transitions to the zygotic program. Chromatin states in embryogenesis Genomic analysis of chromatin states in early embryos has been technically difficult, owing to the limited number of cells available for analysis. Three papers in this issue of Nature use highly sensitive ChIP–seq assays to describe the dynamic patterns of histone modifications during early mouse embryogenesis. Arne Klungland and colleagues find that the oocyte genome is associated with broad non-canonical domains of histone H3K4me3 which seem to function in preventing deposition of DNA methylation. Wei Xie and colleagues find that the oocyte genome is associated with broad non-canonical domains of histone H3K4me3 which overlap with domains of low DNA methylation and seem to contribute to gene silencing. Shaorong Gao and colleagues map histone H3K4me3 and H3K27me3 modifications in pre-implantation embryos and focus on the re-establishment of histone modifications during zygotic genome activation. They find that the breadth of H3K4me3 domains is highly dynamic and that H3K4me3 re-establishes rapidly on promoter regions whereas H3K27me3 is mostly absent from these regions. Taken together—and with previously published work—these studies show that the oocyte has a distinctive epigenome and provide insights into how the maternal gene expression program transitions to the zygotic program. Histone modifications have critical roles in regulating the expression of developmental genes during embryo development in mammals 1 , 2 . However, genome-wide analyses of histone modifications in pre-implantation embryos have been impeded by the scarcity of the required materials. Here, by using a small-scale chromatin immunoprecipitation followed by sequencing (ChIP–seq) method 3 , we map the genome-wide profiles of histone H3 lysine 4 trimethylation (H3K4me3) and histone H3 lysine 27 trimethylation (H3K27me3), which are associated with gene activation and repression 4 , 5 , respectively, in mouse pre-implantation embryos. We find that the re-establishment of H3K4me3, especially on promoter regions, occurs much more rapidly than that of H3K27me3 following fertilization, which is consistent with the major wave of zygotic genome activation at the two-cell stage. Furthermore, H3K4me3 and H3K27me3 possess distinct features of sequence preference and dynamics in pre-implantation embryos. Although H3K4me3 modifications occur consistently at transcription start sites, the breadth of the H3K4me3 domain is a highly dynamic feature. Notably, the broad H3K4me3 domain (wider than 5 kb) is associated with higher transcription activity and cell identity not only in pre-implantation development but also in the process of deriving embryonic stem cells from the inner cell mass and trophoblast stem cells from the trophectoderm. Compared to embryonic stem cells, we found that the bivalency (that is, co-occurrence of H3K4me3 and H3K27me3) in early embryos is relatively infrequent and unstable. Taken together, our results provide a genome-wide map of H3K4me3 and H3K27me3 modifications in pre-implantation embryos, facilitating further exploration of the mechanism for epigenetic regulation in early embryos.

The Chinese novelist and playwright Li Yu 李漁 (1610~1680) enjoyed great fame in Japan since the 1690s when he was introduced to Japanese readers of the Tokugawa period. Particularly important in the reception history of Li Yu in Japan was Jieziyuan huazhuan, the Mustard Seed Garden Manual of Painting. The reproduction and reinterpretation of Jieziyuan huazhuan in Tokugawa Japan shaped Li Yu’s reputation as a literatus ideal among his Japanese readers in spite of his obscure reputation among his Chinese contemporaries. Through a wide range of primary materials, this article examines the idolization of Li Yu in the middle and late Tokugawa period and argues that it was a result of the misrepresentation of Li Yu as a literati painting master, as well as a hermit fiction writer. The close connection established between him and Jieziyuan huazhuan led to the recognition of him in Tokugawa Japan as one of the greatest literati painting artists. Meanwhile, the imagination of him as a hermit further established his image as the ideal of literati spirit among his Japanese admirers. Such idolization in turn contributed to his reputation in early modern China when his works were re-introduced to Chinese readers in the 1930s. 

Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.

To investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p -values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p -values. Comprehensive simulations and real-data applications verify that PseudotimeDE outperforms existing methods in false discovery rate control and power.

When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary. This paper proposes three improvement strategies for this type of “information leakage” problem: sliding window decomposition (SW-EMD), single training and multiple decomposition (STMP-EMD), and multiple training and multiple decomposition (MTMP-EMD). They are combined with a bidirectional multiscale temporal convolutional network (MSBTCN), bidirectional long- and short-term memory network (BiLSTM), and attention mechanism (DMAttention), which introduces a dependency matrix based on cosine similarity to be applied to water quality prediction. The experimental results show that the model achieves good performance in the prediction of three water quality indicators (pH, DO and KMnO 4 ), and the accuracies of the three models proposed in this paper are improved by 1.958% and 0.853% in terms of the RMSE and MAPE, respectively, compared with those of the mainstream LSTM models. The key contributions of this study include the following: (1) three methods are proposed to improve the class EMD decomposition, which can effectively solve the problem of “information leakage” that exists in the current models via class EMD decomposition; (2) the CEEMDAN-MSBTCN-BiLSTM-DMAttention model structure is innovated by combining improved class EMD decomposition methods; and (3) the three improved decomposition methods proposed in this paper can effectively solve the problem of “information leakage” and optimize the prediction model at the same time. This study provides an effective experimental method for water quality prediction and can effectively address the problem of “overfitting” models via class EMD decompositions during model training and testing.