Gaoshi Li

In single-cell RNA sequencing data analyses, massive dropout events markedly degrade the accuracy of downstream studies. Although various imputation methods have been developed, they suffer from two major limitations: an inability to balance local and global information, and a tendency to overlook the inherent complexity of biological data, often falling into local optima. To improve imputation performance, we propose DCLM-impute, a novel framework that integrates a dual contrastive learning architecture with a multi-constraint optimization strategy. The dual contrastive architecture is designed to capture and balance both local and global cellular features, while the multi-constraint strategy guides the model to capture the complex nature of biological systems and avoid local optima. DCLM-impute is comprehensively compared to 13 representative methods on five real datasets and one simulated dataset. The results demonstrate that DCLM-impute consistently outperforms all baselines across a wide range of downstream experiments, including the identification of dropout and real biological zeros, gene expression recovery, cell clustering, differential expression analysis, and trajectory inference. For example, under a 40% dropout rate on the Zeisel dataset, DCLM-impute achieves Pearson correlation coefficient improvements of 3.7%–31.0% and L1-distance improvementsof 2.1%–35.4% in gene expression recovery. Furthermore, ablation studies validate that both the dual contrastive learning architecture and multi-constraint optimization are essential for achieving the stateof-the-art performance.

Gene regulatory networks (GRNs) exhibit the complex regulatory relationships among genes, which are essential for understanding developmental biology and uncovering the fundamental aspects of various biological phenomena. It is an effective and economical way to infer GRNs from single-cell RNA sequencing (scRNA-seq) with computational methods. Recent researches have been done on the problem by using variational autoencoder (VAE) and structural equation model (SEM). Due to the shortcoming of VAE generating poor-quality data, in this paper, a soft introspective adversarial gene regulatory network unsupervised inference model, called SIGRN, is proposed by introducing adversarial mechanism in building a variational autoencoder model. SIGRN applies “soft” introspective adversarial mode to avoid training additional neural networks and adding additional training parameters. It demonstrates superior inference accuracy across most benchmark datasets when compared to nine leading-edge methods. In addition, method SIGRN also achieves better performance on representing cells and generating scRNA-seq data in most datasets. All of which have been verified via substantial experiments. The SIGRN method shows promise for generating scRNA-seq data and inferring GRNs.

Due to the high heterogeneity of cancers, it is rather essential to explore driver modules with the help of gene mutation data as well as known interactions between genes/proteins. Unfortunately, latent false positive interactions are inevitable in the Protein-Protein Interaction (PPI) network. Hence in the presented method, a new weight evaluation index, based on the gene-microRNA network as well as somatic mutation profile, is introduced for weighting the PPI network first. Subsequently, the vertices in the weighted PPI network are hierarchically clustered by measuring the Mahalanobis distance of their feature vectors, extracted with the graph embedding method Node2vec. Finally, a heuristic process with dropping and extracting is conducted on the gene clusters to produce a group of gene modules. Numerous experiment results demonstrate that the proposed method exhibits superior performance to four cutting-edge identification methods in most cases regarding the capability of recognizing the acknowledged cancer-related genes, generating modules having relatively high coverage and mutual exclusivity, and are significantly enriched for specific types of cancers. The majority of the genes in the identified modules are involved in cancer-related signaling pathways, or have been reported to be carcinogenic in the literature. Furthermore, many cancer related genes detected by the proposed method are actually omitted by the four comparison methods, which has been verified in the experiments.

Background: Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. Results: In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein–Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with “inclusion” relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the “inclusion” relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. Conclusions: The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.