Data simulation and regulatory network reconstruction from time-series microarray data using stepwise multiple linear regression

Joint learning of multiple gene networks from single-cell gene expression data

Inferring gene networks from gene expression data is important for understanding functional organizations within cells. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is possible to infer gene networks at single cell level. However, due to the characteristics of scRNA-seq data, such as cellular heterogeneity and high sparsity caused by dropout events, traditional network inference methods may not be suitable for scRNA-seq data. In this study, we introduce a novel joint Gaussian copula graphical model (JGCGM) to jointly estimate multiple gene networks for multiple cell subgroups from scRNA-seq data. Our model can deal with non-Gaussian data with missing values, and identify the common and unique network structures of multiple cell subgroups, which is suitable for scRNA-seq data. Extensive experiments on synthetic data demonstrate that our proposed model outperforms other compared state-of-the-art network inference models. We apply our model to real scRNA-seq data sets to infer gene networks of different cell subgroups. Hub genes in the estimated gene networks are found to be biological significance.

Regulation of proinflammatory genes by the circulating microRNA hsa-miR-939

Circulating microRNAs are beneficial biomarkers because of their stability and dysregulation in diseases. Here we sought to determine the role of miR-939, a miRNA downregulated in patients with complex regional pain syndrome (CRPS). Hsa-miR-939 is predicted to target several proinflammatory genes, including IL-6, VEGFA, TNFα, NFκB2, and nitric oxide synthase 2 (NOS2A). Binding of miR-939 to the 3' untranslated region of these genes was confirmed by reporter assay. Overexpression of miR-939 in vitro resulted in reduction of IL-6, NOS2A and NFκB2 mRNAs, IL-6, VEGFA, and NOS2 proteins and NFκB activation. We observed a significant decrease in the NOS substrate l-arginine in plasma from CRPS patients, suggesting reduced miR-939 levels may contribute to an increase in endogenous NOS2A levels and NO, and thereby to pain and inflammation. Pathway analysis showed that miR-939 represents a critical regulatory node in a network of inflammatory mediators. Collectively, our data suggest that miR-939 may regulate multiple proinflammatory genes and that downregulation of miR-939 in CRPS patients may increase expression of these genes, resulting in amplification of the inflammatory pain signal transduction cascade. Circulating miRNAs may function as crucial signaling nodes, and small changes in miRNA levels may influence target gene expression and thus disease.

Effective gene expression data generation framework based on multi-model approach

OBJECTIVE

Overcome the lack of enough samples in gene expression data sets having thousands of genes but a small number of samples challenging the computational methods using them.

METHODS AND MATERIAL

This paper introduces a multi-model artificial gene expression data generation framework where different gene regulatory network (GRN) models contribute to the final set of samples based on the characteristics of their underlying paradigms. In the first stage, we build different GRN models, and sample data from each of them separately. Then, we pool the generated samples into a rich set of gene expression samples, and finally try to select the best of the generated samples based on a multi-objective selection method measuring the quality of the generated samples from three different aspects such as compatibility, diversity and coverage. We use four alternative GRN models, namely, ordinary differential equations, probabilistic Boolean networks, multi-objective genetic algorithm and hierarchical Markov model.

RESULTS

We conducted a comprehensive set of experiments based on both real-life biological and synthetic gene expression data sets. We show that our multi-objective sample selection mechanism effectively combines samples from different models having up to 95% compatibility, 10% diversity and 50% coverage. We show that the samples generated by our framework has up to 1.5x higher compatibility, 2x higher diversity and 2x higher coverage than the samples generated by the individual models that the multi-model framework uses. Moreover, the results show that the GRNs inferred from the samples generated by our framework can have 2.4x higher precision, 12x higher recall, and 5.4x higher f-measure values than the GRNs inferred from the original gene expression samples.

CONCLUSIONS

Therefore, we show that, we can significantly improve the quality of generated gene expression samples by integrating different computational models into one unified framework without dealing with complex internal details of each individual model. Moreover, the rich set of artificial gene expression samples is able to capture some biological relations that can even not be captured by the original gene expression data set.