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1. Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

   https://doi.org/10.1093/bioinformatics/btac423  

   Cui, Elvis Han ; Song, Dongyuan ; Wong, Weng Kee ; Li, Jingyi Jessica ; Mathelier, ed., Anthony ( June 2022 , Bioinformatics)

   Modeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models.

   Here, we propose the single-cell generalized trend model (scGTM) for capturing a gene’s expression trend, which may be monotone, hill-shaped or valley-shaped, along cell pseudotime. The scGTM has three advantages: (i) it can capture non-monotonic trends that are easy to interpret, (ii) its parameters are biologically interpretable and trend informative, and (iii) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression datasets using the scGTM and show that scGTM can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying biological processes.

   The Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM.

   Supplementary data are available at Bioinformatics online.

    

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2. Statistical evidence for the presence of trajectory in single-cell data

   https://doi.org/10.1186/s12859-022-04875-9  

   Tenha, Lovemore ; Song, Mingzhou ( August 2022 , BMC Bioinformatics)

   Cells progressing from an early state to a developed state give rise to lineages in cell differentiation. Knowledge of these lineages is central to developmental biology. Each biological lineage corresponds to a trajectory in a dynamical system. Emerging single-cell technologies such as single-cell RNA sequencing can capture molecular abundance in diverse cell types in a developing tissue. Many computational methods have been developed to infer trajectories from single-cell data. However, to our knowledge, none of the existing methods address the problem of determining the existence of a trajectory in observed data before attempting trajectory inference.

   We introduce a method to identify the existence of a trajectory using three graph-based statistics. A permutation test is utilized to calculate the empirical distribution of the test statistic under the null hypothesis that a trajectory does not exist. Finally, ap-value is calculated to quantify the statistical significance for the presence of trajectory in the data.

   Our work contributes new statistics to assess the level of uncertainty in trajectory inference to increase the understanding of biological system dynamics.

    

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3. PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization

   https://doi.org/10.1186/s13059-024-03226-6  

   Skok Gibbs, Claudia ; Mahmood, Omar ; Bonneau, Richard ; Cho, Kyunghyun ( April 2024 , Genome Biology)

   Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.

    

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4. Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach

   https://doi.org/10.1093/bioinformatics/btz676  

   Wu, Yufeng ; Schwartz, Russell ( August 2019 , Bioinformatics)

   Abstract Motivation Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling based and can be very slow for large data. Results In this article, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. Different from most existing approaches, ScisTree works with genotype probabilities of individual genotypes (which can be computed by existing single cell genotype callers). ScisTree assumes the infinite sites model. Given uncertain genotypes with individualized probabilities, ScisTree implements a fast heuristic for inferring cell lineage tree and calling the genotypes that allow the so-called perfect phylogeny and maximize the likelihood of the genotypes. Through simulation, we show that ScisTree performs well on the accuracy of inferred trees, and is much more efficient than existing methods. The efficiency of ScisTree enables new applications including imputation of the so-called doublets. Availability and implementation The program ScisTree is available for download at: https://github.com/yufengwudcs/ScisTree. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. Identification of cell-type specific alternative transcripts in the multicellular alga Volvox carteri

   https://doi.org/10.1186/s12864-023-09558-0  

   Balasubramanian, Ravi Nicholas ; Gao, Minglu ; Umen, James ( October 2023 , BMC Genomics)

   Cell type specialization is a hallmark of complex multicellular organisms and is usually established through implementation of cell-type-specific gene expression programs. The multicellular green algaVolvox carterihas just two cell types, germ and soma, that have previously been shown to have very different transcriptome compositions which match their specialized roles. Here we interrogated another potential mechanism for differentiation inV. carteri, cell type specific alternative transcript isoforms (CTSAI).

   We used pre-existing predictions of alternative transcripts and de novo transcript assembly with HISAT2 and Ballgown software to compile a list of loci with two or more transcript isoforms, identified a small subset that were candidates for CTSAI, and manually curated this subset of genes to remove false positives. We experimentally verified three candidates using semi-quantitative RT-PCR to assess relative isoform abundance in each cell type.

   Of the 1978 loci with two or more predicted transcript isoforms 67 of these also showed cell type isoform expression biases. After curation 15 strong candidates for CTSAI were identified, three of which were experimentally verified, and their predicted gene product functions were evaluated in light of potential cell type specific roles. A comparison of genes with predicted alternative splicing fromChlamydomonas reinhardtii, a unicellular relative ofV. carteri, identified little overlap between ortholog pairs with alternative splicing in both species. Finally, we interrogated cell type expression patterns of 126 V. carteripredicted RNA binding protein (RBP) encoding genes and found 40 that showed either somatic or germ cell expression bias. These RBPs are potential mediators of CTSAI inV. carteriand suggest possible pre-adaptation for cell type specific RNA processing and a potential path for generating CTSAI in the early ancestors of metazoans and plants.

   We predicted numerous instances of alternative transcript isoforms in Volvox, only a small subset of which showed cell type specific isoform expression bias. However, the validated examples of CTSAI supported existing hypotheses about cell type specialization inV. carteri,and also suggested new hypotheses about mechanisms of functional specialization for their gene products. Our data imply that CTSAI operates as a minor but important component ofV. cartericellular differentiation and could be used as a model for how alternative isoforms emerge and co-evolve with cell type specialization.

    

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