Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

scBPGRN: Integrating single-cell multi-omics data to construct gene regulatory networks based on BP neural network

The deterioration and metastasis of cancer involve various aspects of genomic changes, including genomic DNA changes, epigenetic modifications, gene expression, and other complex interactions. Therefore, integrating single-cell multi-omics data to construct gene regulatory networks containing more omics information is of great significance for understanding the pathogenesis of cancer. In this article, an algorithm integrating single-cell RNA sequencing data and DNA methylation data to construct a gene regulatory network based on the back-propagation (BP) neural network (scBPGRN) is proposed. This algorithm uses biweight extreme correlation coefficients to measure the correlation between factors and uses neural networks to calculate generalized weights to construct gene regulation networks. Finally, the node strength is calculated to identify the genes associated with cancer. We apply the scBPGRN algorithm to hepatocellular carcinoma (HCC) data. We construct a regulatory network and identify top-ranked genes, such as MYCBP, KLHL35, PRKCZ, and SERPINA6, as the key HCC-related genes. We analyze the top 100 genes, and the HCC-related genes are concentrated in the top 20. In addition, the single cell data is found to consist of two subpopulations. We also apply scBPGRN to two subpopulations. We analyze the top 50 genes in them, and the HCC-related genes are concentrated in the top 20. The consequences of functional enrichment analysis indicate that the gene regulatory network we have constructed is valid. Our results have been verified in several pieces of literature. This study provides a reference for the integration of single-cell multi-omics data to construct gene regulatory networks.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

Generating weighted and thresholded gene coexpression networks using signed distance correlation

Even within well-studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes or proteins, using a network of gene coexpression data that includes functional annotations. Signed distance correlation has proved useful for the construction of unweighted gene coexpression networks. However, transforming correlation values into unweighted networks may lead to a loss of important biological information related to the intensity of the correlation. Here we introduce a principled method to construct weighted gene coexpression networks using signed distance correlation. These networks contain weighted edges only between those pairs of genes whose correlation value is higher than a given threshold. We analyse data from different organisms and find that networks generated with our method based on signed distance correlation are more stable and capture more biological information compared to networks obtained from Pearson correlation. Moreover, we show that signed distance correlation networks capture more biological information than unweighted networks based on the same metric. While we use biological data sets to illustrate the method, the approach is general and can be used to construct networks in other domains. Code and data are available on https://github.com/javier-pardodiaz/sdcorGCN.

Toward modeling metabolic state from single-cell transcriptomics

BACKGROUND

Single-cell metabolic studies bring new insights into cellular function, which can often not be captured on other omics layers. Metabolic information has wide applicability, such as for the study of cellular heterogeneity or for the understanding of drug mechanisms and biomarker development. However, metabolic measurements on single-cell level are limited by insufficient scalability and sensitivity, as well as resource intensiveness, and are currently not possible in parallel with measuring transcript state, commonly used to identify cell types. Nevertheless, because omics layers are strongly intertwined, it is possible to make metabolic predictions based on measured data of more easily measurable omics layers together with prior metabolic network knowledge.

SCOPE OF REVIEW

We summarize the current state of single-cell metabolic measurement and modeling approaches, motivating the use of computational techniques. We review three main classes of computational methods used for prediction of single-cell metabolism: pathway-level analysis, constraint-based modeling, and kinetic modeling. We describe the unique challenges arising when transitioning from bulk to single-cell modeling. Finally, we propose potential model extensions and computational methods that could be leveraged to achieve these goals.

MAJOR CONCLUSIONS

Single-cell metabolic modeling is a rising field that provides a new perspective for understanding cellular functions. The presented modeling approaches vary in terms of input requirements and assumptions, scalability, modeled metabolic layers, and newly gained insights. We believe that the use of prior metabolic knowledge will lead to more robust predictions and will pave the way for mechanistic and interpretable machine-learning models.

Improving Single-Cell RNA-seq Clustering by Integrating Pathways

Single-cell clustering is an important part of analyzing single-cell RNA-sequencing data. However, the accuracy and robustness of existing methods are disturbed by noise. One promising approach for addressing this challenge is integrating pathway information, which can alleviate noise and improve performance. In this work, we studied the impact on accuracy and robustness of existing single-cell clustering methods by integrating pathways. We collected 10 state-of-the-art single-cell clustering methods, 26 scRNA-seq datasets and four pathway databases, combined the AUCell method and the similarity network fusion to integrate pathway data and scRNA-seq data, and introduced three accuracy indicators, three noise generation strategies and robustness indicators. Experiments on this framework showed that integrating pathways can significantly improve the accuracy and robustness of most single-cell clustering methods.

Protein Interaction Network Biology in Neuroscience

Mapping the intricate networks of cellular proteins in the human brain has the potential to address unsolved questions in molecular neuroscience, including the molecular basis of cognition, synaptic plasticity, long-term potentiation, learning, and memory. Perturbations to the protein-protein interaction networks (PPIN) present in neurons, glia, and other cell-types have been linked to multifactorial neurological disorders. Yet while knowledge of brain PPINs is steadily improving, the complexity and dynamic nature of the heterogeneous central nervous system in normal and disease contexts poses a formidable experimental challenge. In this review, the recent applications of functional proteomics and systems biology approaches to study PPINs central to normal neuronal function, during neurodevelopment, and in neurodegenerative disorders are summarized. How systematic PPIN analysis offers a unique mechanistic framework to explore intra- and inter-cellular functional modules governing neuronal activity and brain function is also discussed. Finally, future technological advancements needed to address outstanding questions facing neuroscience are outlined.

Enabling single-cell trajectory network enrichment

Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes. Here, to address this, we have developed Scellnetor; a network-constraint time-series clustering algorithm. It allows extraction of temporal differential gene expression network patterns (modules) that explain the difference in regulation of two developmental trajectories. Using well-characterized experimental model systems, we demonstrate the capacity of Scellnetor as a hypothesis generator to identify putative mechanisms driving haematopoiesis or mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Altogether, Scellnetor allows for single-cell trajectory network enrichment, which effectively lifts scRNA-seq data analysis to a systems biology level.

NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks

A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared with other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions that we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. Based on these insights, we introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.