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Open Access Publications from the University of California

Scholarly Works (5 results)

  • Article
  • Peer Reviewed
UC Berkeley Previously Published Works (2016)

Background

A key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates.

Results

To aid in the analysis and interpretation of single-cell RNA-Seq data, we have developed FastProject, a software tool which analyzes a gene expression matrix and produces a dynamic output report in which two-dimensional projections of the data can be explored. Annotated gene sets (referred to as gene 'signatures') are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms.

Conclusions

Here we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.
    Cover page: FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
    • Thesis
    • Peer Reviewed
    UC Berkeley Electronic Theses and Dissertations (2020)

    Methods for genome-wide gene expression characterization have proven transformative in the study of biology, facilitating the identification of new types of cells, the unraveling of regulatory and signaling pathways, and the discovery of novel pharmaceutical targets for the treatment of human diseases. Beginning with microarrays and accelerated by the introduction of next-generation sequencing, the technology has progressively increased in its scale, sensitivity, and accuracy. Now, with the recent advent of single-cell RNA-seq (scRNA-seq), it is possible to survey the full heterogeneity of complex tissue. This work deals with the analysis of scRNA-seq data and the development of computational methods to extract new biological insights from single-cell experiments.

    Chapter 1 provides some background on the experimental developments enabling single-cell RNA-seq as well as existing methods for the analysis of single-cell data products. Chapter 2 develops an algorithm for the functional interpretation of scRNA-seq data through the use of previous high-throughput experimental results. As a departure from existing methods, this procedure can be utilized with both linear and non-linear dimensionality reduction methods and does not require a pre-partitioning of cells into discrete clusters. In Chapter 3 this approach is further extended for the systematic extraction of informative features (genes) that underlie the heterogeneity of scRNA-seq experiments and the organization of these features into co-regulated gene modules. We additionally demonstrate that this approach can be used with multi-modal datasets to identify genes that vary in accordance with a similarity map specified by a different modality - such as spatial or lineage measurements. Chapter 4 details the development of open source computational tools which make these methods accessible to the larger research community and provide additional capabilities in data exploration, visualization, and collaboration. Finally, in Chapter 5, a case study is described in which single-cell RNA-seq is used to investigate the relevance of the Interleukin 23 receptor in a model of T helper cell type 1 driven autoimmunity.

      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2019)
      We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration.
        Cover page: Functional interpretation of single cell similarity maps
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2019)
        Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.
          Cover page: Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2021)
          Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing and flux balance analysis. We applied Compass to associate metabolic states with T helper 17 (Th17) functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/regulatory T cell (Treg) differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzyme expression was enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells toward a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in CNS autoimmunity.
            Cover page: Metabolic modeling of single Th17 cells reveals regulators of autoimmunity
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