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1. EnGRaiN : a supervised ensemble learning method for recovery of large-scale gene regulatory networks

   https://doi.org/10.1093/bioinformatics/btab829  

   Aluru, Maneesha; Shrivastava, Harsh; Chockalingam, Sriram P.; Shivakumar, Shruti; Aluru, Srinivas; Martelli, ed., Pier Luigi (December 2021, Bioinformatics)

   Abstract MotivationReconstruction of genome-scale networks from gene expression data is an actively studied problem. A wide range of methods that differ between the types of interactions they uncover with varying trade-offs between sensitivity and specificity have been proposed. To leverage benefits of multiple such methods, ensemble network methods that combine predictions from resulting networks have been developed, promising results better than or as good as the individual networks. Perhaps owing to the difficulty in obtaining accurate training examples, these ensemble methods hitherto are unsupervised. ResultsIn this article, we introduce EnGRaiN, the first supervised ensemble learning method to construct gene networks. The supervision for training is provided by small training datasets of true edge connections (positives) and edges known to be absent (negatives) among gene pairs. We demonstrate the effectiveness of EnGRaiN using simulated datasets as well as a curated collection of Arabidopsis thaliana datasets we created from microarray datasets available from public repositories. EnGRaiN shows better results not only in terms of receiver operating characteristic and PR characteristics for both real and simulated datasets compared with unsupervised methods for ensemble network construction, but also generates networks that can be mined for elucidating complex biological interactions. Availability and implementationEnGRaiN software and the datasets used in the study are publicly available at the github repository: https://github.com/AluruLab/EnGRaiN. Supplementary informationSupplementary data are available at Bioinformatics online. 

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2. MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework

   https://doi.org/10.1093/bioinformatics/btad373  

   Pan, Tony C.; Chockalingam, Sriram P.; Aluru, Maneesha; Aluru, Srinivas; Cowen, ed., Lenore (June 2023, Bioinformatics)

   Abstract MotivationGene network reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective gene network reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes. ResultsWe developed maximum capacity path (MCP) score, a novel maximum-capacity-path-based metric to quantify the relative strengths of direct and indirect gene–gene interactions. We further present MCPNet, an efficient, parallelized gene network reconstruction software based on MCP score, to reverse engineer networks in unsupervised and ensemble manners. Using synthetic and real Saccharomyces cervisiae datasets as well as real Arabidopsis thaliana datasets, we demonstrate that MCPNet produces better quality networks as measured by AUPRC, is significantly faster than all other gene network reconstruction software, and also scales well to tens of thousands of genes and hundreds of CPU cores. Thus, MCPNet represents a new gene network reconstruction tool that simultaneously achieves quality, performance, and scalability requirements. Availability and implementationSource code freely available for download at https://doi.org/10.5281/zenodo.6499747 and https://github.com/AluruLab/MCPNet, implemented in C++ and supported on Linux. 

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3. GLIDER: function prediction from GLIDE-based neighborhoods

   https://doi.org/10.1093/bioinformatics/btac322  

   Devkota, Kapil; Schmidt, Henri; Werenski, Matt; Murphy, James M.; Erden, Mert; Arsenescu, Victor; Cowen, Lenore J.; Valencia, ed., Alfonso (May 2022, Bioinformatics)

   Abstract MotivationProtein function prediction, based on the patterns of connection in a protein–protein interaction (or association) network, is perhaps the most studied of the classical, fundamental inference problems for biological networks. A highly successful set of recent approaches use random walk-based low-dimensional embeddings that tend to place functionally similar proteins into coherent spatial regions. However, these approaches lose valuable local graph structure from the network when considering only the embedding. We introduce GLIDER, a method that replaces a protein–protein interaction or association network with a new graph-based similarity network. GLIDER is based on a variant of our previous GLIDE method, which was designed to predict missing links in protein–protein association networks, capturing implicit local and global (i.e. embedding-based) graph properties. ResultsGLIDER outperforms competing methods on the task of predicting GO functional labels in cross-validation on a heterogeneous collection of four human protein–protein association networks derived from the 2016 DREAM Disease Module Identification Challenge, and also on three different protein–protein association networks built from the STRING database. We show that this is due to the strong functional enrichment that is present in the local GLIDER neighborhood in multiple different types of protein–protein association networks. Furthermore, we introduce the GLIDER graph neighborhood as a way for biologists to visualize the local neighborhood of a disease gene. As an application, we look at the local GLIDER neighborhoods of a set of known Parkinson’s Disease GWAS genes, rediscover many genes which have known involvement in Parkinson’s disease pathways, plus suggest some new genes to study. Availability and implementationAll code is publicly available and can be accessed here: https://github.com/kap-devkota/GLIDER. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. ACTIVA : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders

   https://doi.org/10.1093/bioinformatics/btac095  

   Heydari, A. Ali; Davalos, Oscar A.; Zhao, Lihong; Hoyer, Katrina K.; Sindi, Suzanne S.; Mathelier, ed., Anthony (February 2022, Bioinformatics)

   Abstract MotivationSingle-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies. ResultsWe train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models. Availability and implementationThe codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA. Supplementary informationSupplementary data are available at Bioinformatics online. 

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5. Learning Attribute Distributions Through Random Walks

   https://doi.org/10.1007/978-3-031-21131-7_2  

   Antunes, N.; Bhamidi, S.; Pipiras, V. (January 2023, Complex Networks and Their Applications XI)

   Cherifi, H.; Mantegna, R.N.; Rocha, L.M.; Cherifi, C.; Micciche, S. (Ed.)

   We investigate the statistical learning of nodal attribute distributions in homophily networks using random walks. Attributes can be discrete or continuous. A generalization of various existing canonical models, based on preferential attachment is studied, where new nodes form connections dependent on both their attribute values and popularity as measured by degree. We consider several canonical attribute agnostic sampling schemes such as Metropolis-Hasting random walk, versions of node2vec (Grover and Leskovec 2016) that incorporate both classical random walk and non-backtracking propensities and propose new variants which use attribute information in addition to topological information to explore the network. The performance of such algorithms is studied on both synthetic networks and real world systems, and its dependence on the degree of homophily, or absence thereof, is assessed. 

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