About

Elizabeth Purdom is an Associate Professor of Statistics at the Center for Computational Biology. Her research interests lie in developing statistical methods for high-dimensional data arising in the field of biology and genetics. She focuses on questions of robust estimation and hypothesis testing for high-throughput biological experiments, particularly gene expression microarrays and next generation sequencing. Additionally, she is interested in the integration of heterogeneous sources of data, which can include multiple experimental platforms or various forms of preexisting biological knowledge such as networks or trees. Her work addresses high-dimensional inference and multivariate analysis, aiming to create a unified understanding of complex biological data.

Research topics

• Biology
• Medicine
• Neuroscience
• Genetics
• Anatomy
• Computational biology
• Virology
• Cell biology
• Immunology
• Cartography
• Geography
• Environmental health
• Pathology

Selected publications

• Holo‐omics disentangle drought response and biotic interactions among plant, endophyte and pathogen

  New Phytologist · 2025-04-18 · 9 citations

  articleOpen access

  Holo-omics provide a novel opportunity to study the interactions among fungi from different functional guilds in host plants in field conditions. We address the entangled responses of plant pathogenic and endophytic fungi associated with sorghum when droughted through the assembly of the most abundant fungal, endophyte genome from rhizospheric metagenomic sequences followed by a comparison of its metatranscriptome with the host plant metabolome and transcriptome. The rise in relative abundance of endophytic Acremonium persicinum (operational taxonomic unit 5 (OTU5)) in drought co-occurs with a rise in fungal membrane dynamics and plant metabolites, led by ethanolamine, a key phospholipid membrane component. The negative association between endophytic A. persicinum (OTU5) and plant pathogenic fungi co-occurs with a rise in expression of the endophyte's biosynthetic gene clusters coding for secondary compounds. Endophytic A. persicinum (OTU5) and plant pathogenic fungi are negatively associated under preflowering drought but not under postflowering drought, likely a consequence of variation in fungal fitness responses to changes in the availability of water and niche space caused by plant maturation over the growing season. Our findings suggest that the dynamic biotic interactions among host, beneficial and harmful microbiota in a changing environment can be disentangled by a blending of field observation, laboratory validation, holo-omics and ecological modelling.

  PublisherOA PDFDOI

• Multi-season analysis reveals hundreds of drought-responsive genes in sorghum

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-02 · 1 citations

  preprintOpen access

  Abstract Persistent drought affects global crop production and is becoming more severe in many parts of the world in recent decades. Deciphering how plants respond to drought will facilitate the development of flexible mitigation strategies. Sorghum bicolor L. Moench (sorghum), a major cereal crop and an emerging bioenergy crop, exhibits remarkable resilience to drought. To better understand the molecular traits that underlie sorghum’s remarkable drought tolerance, we undertook a large-scale sorghum gene expression profiling effort, totaling nearly 1,500 transcriptome profiles, across a 3-year field study with replicated plots in California’s Central Valley. This study included time-resolved gene expression data from roots and leaves of two sorghum genotypes, BTx642 and RTx430, with different pre-flowering and post-flowering drought-tolerance adaptations under control and drought conditions. Quantification of genotype-specific drought tolerance effects was enabled by de novo sequencing, assembly, and annotation of both BTx642 and RTx430 genomes. These reference-quality genomes were used to construct a pan-gene set for characterizing conserved and genotype-specific expression. By integrating time-resolved transcriptomic responses to drought in the field across three consecutive years, we identified a set of drought-responsive genes that responded similarly in all three years of our field study. This expansive dataset represents a unique resource for sorghum and drought research communities and provides a methodological framework for the integration of multi-faceted time-resolved transcriptomic datasets.

  PublisherOA PDFDOI

• Visualizing scRNA-Seq data at population scale with GloScope

  Genome biology · 2024-10-08 · 5 citations

  articleOpen accessSenior author

  Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

  PublisherOA PDFDOI

• Improving replicability in single-cell RNA-Seq cell type discovery with Dune

  BMC Bioinformatics · 2024-05-24 · 1 citations

  articleOpen access

  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.

  PublisherOA PDFDOI

• A single-cell atlas of IL-23 inhibition in cutaneous psoriasis distinguishes clinical response

  Science Immunology · 2024-01-26 · 36 citations

  article

  Psoriasis vulgaris and other chronic inflammatory diseases improve markedly with therapeutic blockade of interleukin-23 (IL-23) signaling, but the genetic mechanisms underlying clinical responses remain poorly understood. Using single-cell transcriptomics, we profiled immune cells isolated from lesional psoriatic skin before and during IL-23 blockade. In clinically responsive patients, a psoriatic transcriptional signature in skin-resident memory T cells was strongly attenuated. In contrast, poorly responsive patients were distinguished by persistent activation of IL-17-producing T (T17) cells, a mechanism distinct from alternative cytokine signaling or resistance isolated to epidermal keratinocytes. Even in IL-23 blockade-responsive patients, we detected a recurring set of recalcitrant, disease-specific transcriptional abnormalities. This irreversible immunological state may necessitate ongoing IL-23 inhibition. Spatial transcriptomic analyses also suggested that successful IL-23 blockade requires dampening of >90% of IL-17-induced response in lymphocyte-adjacent keratinocytes, an unexpectedly high threshold. Collectively, our data establish a patient-level paradigm for dissecting responses to immunomodulatory treatments.

  PublisherDOI

• Supplementary Table 2 from Temporal Dissection of Tumorigenesis in Primary Cancers

  2023-04-03

  supplementary-materialsOpen access

  Supplementary Table 2 from Temporal Dissection of Tumorigenesis in Primary Cancers

  PublisherDOI

• Supplementary Figure Legends 1-4, Methods from Temporal Dissection of Tumorigenesis in Primary Cancers

  2023-04-03

  preprintOpen access

  Supplementary Figure Legends 1-4, Methods from Temporal Dissection of Tumorigenesis in Primary Cancers

  PublisherDOI

• ISID1544 - Disruption of a pathologic, skin-resident T17 cell identity in clinically effective IL23 blockade of psoriasis

  2023-05-08

  preprint
  PublisherDOI

• Supplementary Data from Exon-Level Microarray Analyses Identify Alternative Splicing Programs in Breast Cancer

  2023-04-03

  preprintOpen access

  <p>Supplementary Figures S1-S2 and Supplementary Tables S2-S4.</p>

  PublisherOA PDFDOI

• Supplementary Figure 1 from Temporal Dissection of Tumorigenesis in Primary Cancers

  2023-04-03

  preprintOpen access

  Supplementary Figure 1 from Temporal Dissection of Tumorigenesis in Primary Cancers

  PublisherOA PDFDOI

Recent grants

• PostDoctoral Research Fellowship in Biological Informatics FY2006

  NSF · $120k · 2007–2009

Frequent coauthors

• Paul T. Spellman

  UCLA Health

  104 shared

• Joe W. Gray

  94 shared

• Lakshmi R. Jakkula

  91 shared

• Raymond J. Cho

  University of California, San Francisco

  88 shared

• Steffen Durinck

  87 shared

• Theodora M. Mauro

  San Francisco VA Health Care System

  71 shared

• Gad Getz

  70 shared

• Roy C. Grekin

  University of California, San Francisco

  68 shared

Labs

• Center for Computational BiologyPI

Education

• PhD, Statistics

  Stanford University

  2006

• B.S.

  Yale University

  2000