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1. Reconstructing growth and dynamic trajectories from single-cell transcriptomics data

   https://doi.org/10.1038/s42256-023-00763-w  

   Sha, Yutong; Qiu, Yuchi; Zhou, Peijie; Nie, Qing (January 2024, Nature Machine Intelligence)

   Abstract Time-series single-cell RNA sequencing (scRNA-seq) datasets provide unprecedented opportunities to learn dynamic processes of cellular systems. Due to the destructive nature of sequencing, it remains challenging to link the scRNA-seq snapshots sampled at different time points. Here we present TIGON, a dynamic, unbalanced optimal transport algorithm that reconstructs dynamic trajectories and population growth simultaneously as well as the underlying gene regulatory network from multiple snapshots. To tackle the high-dimensional optimal transport problem, we introduce a deep learning method using a dimensionless formulation based on the Wasserstein–Fisher–Rao (WFR) distance. TIGON is evaluated on simulated data and compared with existing methods for its robustness and accuracy in predicting cell state transition and cell population growth. Using three scRNA-seq datasets, we show the importance of growth in the temporal inference, TIGON’s capability in reconstructing gene expression at unmeasured time points and its applications to temporal gene regulatory networks and cell–cell communication inference. 

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2. COFFEE: consensus single cell-type specific inference for gene regulatory networks

   https://doi.org/10.1093/bib/bbae457  

   Lodi, Musaddiq K; Chernikov, Anna; Ghosh, Preetam (September 2024, Briefings in Bioinformatics)

   Abstract The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee 

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3. GRNFormer: accurate gene regulatory network inference using graph transformer

   https://doi.org/10.1093/bioinformatics/btag144  

   Hegde, Akshata; Cheng, Jianlin (April 2026, Bioinformatics)

   Mathelier, Anthony (Ed.)

   Abstract MotivationDeciphering gene regulatory networks (GRNs) from single-cell transcriptomics data remains a fundamental challenge in computational biology. It is hindered by data sparsity, high dimensionality, and the lack of scalable, generalizable inference models. To address this, we present GRNFormer, a generalizable graph transformer framework for accurate GRN inference from transcriptomics data across species, cell types, and platforms without requiring cell-type annotations or prior regulatory information. ResultsGRNFormer integrates a transformer-based gene expression encoder (Gene-Transcoder) with a variational graph autoencoder (GraViTAE) employing pairwise attention to jointly learn the representations of genes (nodes) and their co-expression relationships (edges). Leveraging TF-Walker, a transcription factor-anchored subgraph sampling strategy, it effectively captures gene regulatory interactions from either single-cell or bulk RNA-seq datasets. Benchmarking on standard datasets demonstrates that GRNFormer outperforms existing traditional and deep learning state-of-the-art methods in blind evaluations, achieving average sampled area under the receiver operating characteristic curve (Sampled_AUROC) and sampled area under the precision–recall curve (Sampled_AUPRC) values between 0.90 and 0.98 as well as 0.87–0.98 average sampled F1 score. The model robustly recovers both known and novel regulatory networks, including pluripotency circuits in human embryonic stem cells (hESCs) and immune cell modules in peripheral blood mononuclear cells (PBMCs). The architecture enables scalable, biologically interpretable GRN inference across various datasets, cell types, and species, establishing GRNFormer as a robust and transferable tool for network biology. Availability and implementationGRNFormer is available on GitHub (https://github.com/BioinfoMachineLearning/GRNformer); the version used in this work is archived on Zenodo (https://doi.org/10.5281/zenodo.18868395), with evaluation resources for reproducibility. 

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4. CeSpGRN: Inferring cell-specific gene regulatory networks from single cell multi-omics and spatial data

   https://doi.org/10.1101/2022.03.03.482887  

   Zhang, Ziqi; Han, Jongseok; Song, Le; Zhang, Xiuwei (November 2023, bioRxiv)

   Abstract Single cell profiling techniques including multi-omics and spatial-omics technologies allow researchers to study cell-cell variation within a cell population. These variations extend to biological networks within cells, in particular, the gene regulatory networks (GRNs). GRNs rewire as the cells evolve, and different cells can have different governing GRNs. However, existing GRN inference methods usually infer a single GRN for a population of cells, without exploring the cell-cell variation in terms of their regulatory mechanisms. Recently, jointly profiled single cell transcriptomics and chromatin accessibility data have been used to infer GRNs. Although methods based on such multi-omics data were shown to improve over the accuracy of methods using only single cell RNA-seq (scRNA-seq) data, they do not take full advantage of the single cell resolution chromatin accessibility data. We propose CeSpGRN (CellSpecificGeneRegulatoryNetwork inference), which infers cell-specific GRNs from scRNA-seq, single cell multi-omics, or single cell spatial-omics data. CeSpGRN uses a Gaussian weighted kernel that allows the GRN of a given cell to be learned from the sequencing profile of itself and its neighboring cells in the developmental process. The kernel is constructed from the similarity of gene expressions or spatial locations between cells. When the chromatin accessibility data is available, CeSpGRN constructs cell-specific prior networks which are used to further improve the inference accuracy. We applied CeSpGRN to various types of real-world datasets and inferred various regulation changes that were shown to be important in cell development. We also quantitatively measured the performance of CeSpGRN on simulated datasets and compared with baseline methods. The results show that CeSpGRN has a superior performance in reconstructing the GRN for each cell, as well as in detecting the regulatory interactions that differ between cells. CeSpGRN is available athttps://github.com/PeterZZQ/CeSpGRN. 

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5. DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data

   https://doi.org/10.3390/biom14070766  

   Zhou, Xiguo; Pan, Jingyi; Chen, Liang; Zhang, Shaoqiang; Chen, Yong (July 2024, Biomolecules)

   Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types. 

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