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Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2021)

Over the last two decades, technological improvements have led to a tremendous reduction in the cost and speed of DNA sequencing. This has opened the door to many new applications, including the quantification of transcriptomes at the resolution of single cells (scRNA-Seq), and the discovery of genetic features associated with phenotypes of interest, also known as genome-wide association study (GWAS).

scRNA-Seq has emerged in just 10 years as a major tool to investigate biological diversity. The ability to assess gene expression for individual cells enables the study of a range of biological processes at new levels of resolution. This can have many interesting applications, including the identification of novel cell types, defined by their unique transcriptomic signatures. Drawing from the clustering literature, many methods have been developed to group cells together based on their gene expression characteristics. However, all those algorithms require the tuning of hyper-parameters based on typically ad hoc recommendations. Moreover, the direct validation of the discovered cell types is generally difficult, if not impossible. The grouping of cells is also not unique for a given biological system, and there often exists a hierarchy of cell types, with ever-finer levels of resolutions. Because of all this, the discovery of reliable, replicable cell types remains a major challenge. In Chapter 2, we will delve deeper into this issue and introduce a new method called Dune that tackles the resolution-replicability trade-off in clustering.

scRNA-Seq data also enable the tracking of continuous developmental changes, without the need for arbitrary discretization that stemmed purely from the data collection protocol. This allows to investigate processes such as the cell cycle, the differentiation of stem cells into different cell types, or the cellular response to a drug over time. In Chapter 3, we will investigate how to characterize patterns of gene expression along such developmental trajectories, to identify dynamic genes and drivers of differentiation, using the tradeSeq method. In Chapter 4, we will provide a general workflow called condiments for analyzing such dynamic systems in the presence of multiple conditions, such as treatment/control.

GWAS represent another field that has gained major attention following the emergence of cheaper high-throughput sequencing technologies. In human populations, the problem has been extensively studied, mainly in the context of diseases such as diabetes. However, GWAS can also be applied to bacterial genomes, especially in the context of antibiotic resistance. Some concepts from the human GWAS literature are applicable in bacteria. However, characteristics of bacterial genome mean that other concepts, such as that of a reference genome, are inappropriate and irrelevant. New methods need to be developed for this specific problem. In Chapter 5, we present a new subgraph enumeration method named CALDERA that leverages the structure of the data to provides more robust analyses and facilitate the interpretation of bacterial GWAS data.

    Cover page: Inference in high dimensions with applications to the analysis of single-cell transcriptomic and bacterial genetic data
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2024)
    In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cells conditions along the trajectorys path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.
      Cover page: Trajectory inference across multiple conditions with condiments.
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2022)

      Motivation

      Genome-wide association studies (GWAS), aiming to find genetic variants associated with a trait, have widely been used on bacteria to identify genetic determinants of drug resistance or hypervirulence. Recent bacterial GWAS methods usually rely on k-mers, whose presence in a genome can denote variants ranging from single-nucleotide polymorphisms to mobile genetic elements. This approach does not require a reference genome, making it easier to account for accessory genes. However, a same gene can exist in slightly different versions across different strains, leading to diluted effects.

      Results

      Here, we overcome this issue by testing covariates built from closed connected subgraphs (CCSs) of the de Bruijn graph defined over genomic k-mers. These covariates capture polymorphic genes as a single entity, improving k-mer-based GWAS both in terms of power and interpretability. However, a method naively testing all possible subgraphs would be powerless due to multiple testing corrections, and the mere exploration of these subgraphs would quickly become computationally intractable. The concept of testable hypothesis has successfully been used to address both problems in similar contexts. We leverage this concept to test all CCSs by proposing a novel enumeration scheme for these objects which fully exploits the pruning opportunity offered by testability, resulting in drastic improvements in computational efficiency. Our method integrates with existing visual tools to facilitate interpretation.

      Availability and implementation

      We provide an implementation of our method, as well as code to reproduce all results at https://github.com/HectorRDB/Caldera_ISMB.

      Supplementary information

      Supplementary data are available at Bioinformatics online.
        Cover page: CALDERA: finding all significant de Bruijn subgraphs for bacterial GWAS
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2024)
        BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.
          Cover page: Improving replicability in single-cell RNA-Seq cell type discovery with Dune.
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