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1. Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation

   https://doi.org/10.1371/journal.pcbi.1012476  

   Zhao, Wenjun; Larschan, Erica; Sandstede, Björn; Singh, Ritambhara (May 2025, PLOS Computational Biology)

   Zhang, Xiuwei (Ed.)

   Inferring gene regulatory networks from gene expression data is an important and challenging problem in the biology community. We propose OTVelo, a methodology that takes time-stamped single-cell gene expression data as input and predicts gene regulation across two time points. It is known that the rate of change of gene expression, which we will refer to as gene velocity, provides crucial information that enhances such inference; however, this information is not always available due to the limitations in sequencing depth. Our algorithm overcomes this limitation by estimating gene velocities using optimal transport. We then infer gene regulation using time-lagged correlation and Granger causality via regularized linear regression. Instead of providing an aggregated network across all time points, our method uncovers the underlying dynamical mechanism across time points. We validate our algorithm on 13 simulated datasets with both synthetic and curated networks and demonstrate its efficacy on 9 experimental data sets. 

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2. Assisted estimation of gene expression graphical models

   https://doi.org/10.1002/gepi.22377  

   Yi, Huangdi; Zhang, Qingzhao; Sun, Yifan; Ma, Shuangge (February 2021, Genetic Epidemiology)

   null (Ed.)

   In the study of gene expression data, network analysis has played a uniquely important role. To accommodate the high dimensionality and low sample size and generate interpretable results, regularized estimation is usually conducted in the construction of gene expression Gaussian Graphical Models (GGM). Here we use GeO‐GGM to represent gene‐expression‐only GGM. Gene expressions are regulated by regulators. gene‐expression‐regulator GGMs (GeR‐GGMs), which accommodate gene expressions as well as their regulators, have been constructed accordingly. In practical data analysis, with a “lack of information” caused by the large number of model parameters, limited sample size, and weak signals, the construction of both GeO‐GGMs and GeR‐GGMs is often unsatisfactory. In this article, we recognize that with the regulation between gene expressions and regulators, the sparsity structures of a GeO‐GGM and its GeR‐GGM counterpart can satisfy a hierarchy. Accordingly, we propose a joint estimation which reinforces the hierarchical structure and use the construction of a GeO‐GGM to assist that of its GeR‐GGM counterpart and vice versa. Consistency properties are rigorously established, and an effective computational algorithm is developed. In simulation, the assisted construction outperforms the separation construction of GeO‐GGM and GeR‐GGM. Two The Cancer Genome Atlas data sets are analyzed, leading to findings different from the direct competitors. 

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3. Gene regulatory network state estimation from arbitrary correlated measurements

   https://doi.org/10.1186/s13634-018-0543-y  

   Imani, Mahdi; Braga-Neto, Ulisses (December 2018, EURASIP Journal on Advances in Signal Processing)
4. Gene tree quality affects empirical coalescent branch length estimation

   https://doi.org/10.1111/zsc.12512  

   Forthman, Michael; Braun, Edward L.; Kimball, Rebecca T. (September 2021, Zoologica Scripta)

   Abstract Assessing effects of gene tree error in coalescent analyses have widely ignored coalescent branch lengths (CBLs) despite their potential utility in estimating ancestral population demographics and detecting species tree anomaly zones. However, the ability of coalescent methods to obtain accurate estimates remains largely unexplored. Errors in gene trees should lead to underestimates of the true CBL, and for a given set of comparisons, longer CBLs should be more accurate. Here, we furthered our empirical understanding of how error in gene tree quality (i.e., locus informativeness and gene tree resolution) affect CBLs using four datasets comprised of ultraconserved elements (UCE) or exons for clades that exhibit wide ranges of branch lengths. For each dataset, we compared the impact of locus informativeness (assessed using number of parsimony‐informative sites) and gene tree resolution on CBL estimates. Our results, in general, showed that CBLs were drastically shorter when estimates included low informative loci. Gene tree resolution also had an impact on UCE datasets, with polytomous gene trees producing longer branches than randomly resolved gene trees. However, resolution did not appear to affect CBL estimates from the more informative exon datasets. Thus, as expected, gene tree quality affects CBL estimates, though this can generally be minimized by using moderate filtering to select more informative loci and/or by allowing polytomies in gene trees. These approaches, as well as additional contributions to improve CBL estimation, should lead to CBLs that are useful for addressing evolutionary and biological questions. 

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5. Human Age Estimation from Gene Expression Data using Artificial Neural Networks

   https://doi.org/10.1109/BIBM52615.2021.9669893  

   Mohamadi, Salman; Nasrabadi, Nasser M.; Doretto, Gianfranco; Adjeroh, Donald A. (December 2021, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))