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1. CINS: Cell Interaction Network inference from Single cell expression data

   https://doi.org/10.1371/journal.pcbi.1010468  

   Yuan, Ye ; Cosme, Carlos ; Adams, Taylor Sterling ; Schupp, Jonas ; Sakamoto, Koji ; Xylourgidis, Nikos ; Ruffalo, Matthew ; Li, Jiachen ; Kaminski, Naftali ; Bar-Joseph, Ziv ( September 2022 , PLOS Computational Biology)

   Hawrylycz, Michael (Ed.)

   Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging mouse dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions improving on prior methods suggested for cell interaction predictions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS. 

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2. Topological and geometric analysis of cell states in single-cell transcriptomic data

   https://doi.org/10.1093/bib/bbae176  

   Huynh, Tram ; Cang, Zixuan ( April 2024 , Briefings in Bioinformatics)

   Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures. Here, we introduce scGeom, a tool that exploits the multiscale and multidimensional structures in scRNA-seq data by analyzing the geometry and topology through curvature and persistent homology of both cell and gene networks. We demonstrate the utility of these structural features to reflect biological properties and functions in several applications, where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and the differentiation potential of cells. We also illustrate that structural characteristics can improve the classification of cell types.

    

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3. Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data

   https://doi.org/10.1186/s12864-020-06938-8  

   Dadaneh, Siamak Zamani ; de Figueiredo, Paul ; Sze, Sing-Hoi ; Zhou, Mingyuan ; Qian, Xiaoning ( September 2020 , BMC Genomics)

   null (Ed.)

   Abstract Background Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecular heterogeneity and shed light into the underlying cellular process to better understand development and disease mechanisms. The unique analytic challenge is to appropriately model highly over-dispersed scRNA-seq count data with prevalent dropouts (zero counts), making zero-inflated dimensionality reduction techniques popular for scRNA-seq data analyses. Employing zero-inflated distributions, however, may place extra emphasis on zero counts, leading to potential bias when identifying the latent structure of the data. Results In this paper, we propose a fully generative hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating the need for explicitly modeling zero inflation. At the same time, hGNB can naturally account for covariate effects at both the gene and cell levels to identify complex latent representations of scRNA-seq data, without the need for commonly adopted pre-processing steps such as normalization. Efficient Bayesian model inference is derived by exploiting conditional conjugacy via novel data augmentation techniques. Conclusion Experimental results on both simulated data and several real-world scRNA-seq datasets suggest that hGNB is a powerful tool for cell cluster discovery as well as cell lineage inference. 

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4. 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 ( November 2023 , Nature Machine Intelligence)

   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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5. Single-cell transcriptional diversity is a hallmark of developmental potential

   https://doi.org/10.1126/science.aax0249  

   Gulati, Gunsagar S. ; Sikandar, Shaheen S. ; Wesche, Daniel J. ; Manjunath, Anoop ; Bharadwaj, Anjan ; Berger, Mark J. ; Ilagan, Francisco ; Kuo, Angera H. ; Hsieh, Robert W. ; Cai, Shang ; et al ( January 2020 , Science)

   Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential—the number of expressed genes per cell—and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

    

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