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Creators/Authors contains: "Chen, Jun"

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  1. Abstract

    Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.

  2. Abstract Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control challenging. Here, we show that the compositional effects can be addressed by a simple, yet highly flexible and scalable, approach. The proposed method, LinDA, only requires fitting linear regression models on the centered log-ratio transformed data, and correcting the bias due to compositional effects. We show that LinDA enjoys asymptotic FDR control and can be extended to mixed-effect models for correlated microbiome data. Using simulations and real examples, we demonstrate the effectiveness of LinDA.
    Free, publicly-accessible full text available December 1, 2023
  3. Free, publicly-accessible full text available October 1, 2023
  4. Free, publicly-accessible full text available October 1, 2023
  5. Abstract

    Spinal motor neurons (MNs) integrate sensory stimuli and brain commands to generate movements. In vertebrates, the molecular identities of the cardinal MN types such as those innervating limb versus trunk muscles are well elucidated. Yet the identities of finer subtypes within these cell populations that innervate individual muscle groups remain enigmatic. Here we investigate heterogeneity in mouse MNs using single-cell transcriptomics. Among limb-innervating MNs, we reveal a diverse neuropeptide code for delineating putative motor pool identities. Additionally, we uncover that axial MNs are subdivided into three molecularly distinct subtypes, defined by mediolaterally-biased Satb2, Nr2f2 or Bcl11b expression patterns with different axon guidance signatures. These three subtypes are present in chicken and human embryos, suggesting a conserved axial MN expression pattern across higher vertebrates. Overall, our study provides a molecular resource of spinal MN types and paves the way towards deciphering how neuronal subtypes evolved to accommodate vertebrate motor behaviors.

  6. Abstract Summary

    Due to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify the degree of reproducibility for a univariate measurement using technical replicates. In this work, we extend the traditional ICC to distance measures and propose a distance-based ICC (dICC). We derive the asymptotic distribution of the sample-based dICC to facilitate statistical inference. We illustrate dICC using a real dataset from a metagenomic reproducibility study.

    Availability and implementation

    dICC is implemented in the R CRAN package GUniFrac.

    Supplementary information

    Supplementary data are available at Bioinformatics online.

  7. Free, publicly-accessible full text available August 1, 2023
  8. Free, publicly-accessible full text available June 20, 2023
  9. Crystallization is a universal phenomenon underpinning many industrial and natural processes and is fundamental to chemistry and materials science. However, microscopic crystallization pathways of nanoparticle superlattices have been seldom studied mainly owing to the difficulty of real-time observation of individual self-assembling nanoparticles in solution. Here, using in situ electron microscopy, we directly image the full self-assembly pathway from dispersed nanoparticles into ordered superlattices in nonaqueous solution. We show that electron-beam irradiation controls nanoparticle mobility, and the solvent composition largely dictates interparticle interactions and assembly behaviors. We uncover a multistep crystallization pathway consisting of four distinct stages through multi-order-parameter analysis and visualize the formation, migration, and annihilation of multiple types of defects in nanoparticle superlattices. These findings open the door for achieving independent control over imaging conditions and nanoparticle assembly conditions and will enable further study of the microscopic kinetics of assembly and phase transition in nanocolloidal systems.
    Free, publicly-accessible full text available August 5, 2023
  10. Free, publicly-accessible full text available August 3, 2023