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1. Inference of trajectory presence by tree dimension and subset specificity by subtree cover

   https://doi.org/10.1371/journal.pcbi.1009829  

   Tenha, Lovemore ; Song, Mingzhou ( February 2022 , PLOS Computational Biology)

   Iakoucheva, Lilia M. (Ed.)

   The complexity of biological processes such as cell differentiation is reflected in dynamic transitions between cellular states. Trajectory inference arranges the states into a progression using methodologies propelled by single-cell biology. However, current methods, all returning a best trajectory, do not adequately assess statistical significance of noisy patterns, leading to uncertainty in inferred trajectories. We introduce a tree dimension test for trajectory presence in multivariate data by a dimension measure of Euclidean minimum spanning tree, a test statistic, and a null distribution. Computable in linear time to tree size, the tree dimension measure summarizes the extent of branching more effectively than globally insensitive number of leaves or tree diameter indifferent to secondary branches. The test statistic quantifies trajectory presence and its null distribution is estimated under the null hypothesis of no trajectory in data. On simulated and real single-cell datasets, the test outperformed the intuitive number of leaves and tree diameter statistics. Next, we developed a measure for the tissue specificity of the dynamics of a subset, based on the minimum subtree cover of the subset in a minimum spanning tree. We found that tissue specificity of pathway gene expression dynamics is conserved in human and mouse development: several signal transduction pathways including calcium and Wnt signaling are most tissue specific, while genetic information processing pathways such as ribosome and mismatch repair are least so. Neither the tree dimension test nor the subset specificity measure has any user parameter to tune. Our work opens a window to prioritize cellular dynamics and pathways in development and other multivariate dynamical systems. 

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2. CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics

   https://doi.org/10.1093/bioinformatics/btad269  

   Bafna, Mihir ; Li, Hechen ; Zhang, Xiuwei ( June 2023 , Bioinformatics)

   Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell–cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model.

   We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks.

   The source code and data is available at https://github.com/MihirBafna/CLARIFY.

    

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3. Overcoming biases in causal inference of molecular interactions

   https://doi.org/10.1093/bioinformatics/btac206  

   Kumar, Sajal ; Song, Mingzhou ; Robinson, ed., Peter ( April 2022 , Bioinformatics)

   Computer inference of biological mechanisms is increasingly approachable due to dynamically rich data sources such as single-cell genomics. Inferred molecular interactions can prioritize hypotheses for wet-lab experiments to expedite biological discovery. However, complex data often come with unwanted biological or technical variations, exposing biases over marginal distribution and sample size in current methods to favor spurious causal relationships.

   Considering function direction and strength as evidence for causality, we present an adapted functional chi-squared test (AdpFunChisq) that rewards functional patterns over non-functional or independent patterns. On synthetic and three biology datasets, we demonstrate the advantages of AdpFunChisq over 10 methods on overcoming biases that give rise to wide fluctuations in the performance of alternative approaches. On single-cell multiomics data of multiple phenotype acute leukemia, we found that the T-cell surface glycoprotein CD3 delta chain may causally mediate specific genes in the viral carcinogenesis pathway. Using the causality-by-functionality principle, AdpFunChisq offers a viable option for robust causal inference in dynamical systems.

   The AdpFunChisq test is implemented in the R package ‘FunChisq’ (2.5.2 or above) at https://cran.r-project.org/package=FunChisq. All other source code along with pre-processed data is available at Code Ocean https://doi.org/10.24433/CO.2907738.v1

   Supplementary materials are available at Bioinformatics online.

    

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4. High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0

   https://doi.org/10.1093/bioinformatics/btac117  

   Skok Gibbs, Claudia ; Jackson, Christopher A. ; Saldi, Giuseppe-Antonio ; Tjärnberg, Andreas ; Shah, Aashna ; Watters, Aaron ; De Veaux, Nicholas ; Tchourine, Konstantine ; Yi, Ren ; Hamamsy, Tymor ; et al ( February 2022 , Bioinformatics)

   Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above.

   In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data.

   The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/).

   Supplementary data are available at Bioinformatics online.

    

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5. Genome‐scale data resolves the timing of divergence in Joshua trees

   https://doi.org/10.1002/ajb2.1633  

   Smith, Christopher Irwin ; McKain, Michael R. ; Guimond, Austin ; Flatz, Ramona ( April 2021 , American Journal of Botany)

   Joshua trees (Yucca brevifoliaandY. jaegeriana) and their yucca moth pollinators (Tegeticula syntheticaandT. antithetica) are a model system for studies of plant–pollinator coevolution and, they are thought to be one of the only cases in which there is compelling evidence for cospeciation driven by coevolution. Previous work attempted to evaluate whether divergence between the plant and their pollinators was contemporaneous. That work concluded that the trees diverged more than 5 million years ago—well before the pollinators. However, clear inferences were hampered by a lack of data from the nuclear genome and low genetic variation in chloroplast genes. As a result, divergence times in the trees could not be confidently estimated.

   We present an analysis of whole chloroplast genome sequence data and RADseq data from >5000 loci in the nuclear genome. We developed a molecular clock for the Asparagales and the Agavoideae using multiple fossil calibration points. Using Bayesian inference, we produced new estimates for the age of the genusYuccaand for Joshua trees. We used calculated summary statistics describing genetic variation and used coalescent‐based methods to estimate population genetic parameters.

   We find that the Joshua trees are moderately genetically differentiated, but that they diverged quite recently (~100–200 kya), and much more recently than their pollinators.

   The results argue against the notion that coevolution directly contributed to speciation in this system, suggesting instead that coevolution with pollinators may have reinforced reproductive isolation following initial divergence in allopatry.

    

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