About

Dr. Stephanie Hicks is an Associate Professor in the Department of Biomedical Engineering and the Department of Biostatistics at Johns Hopkins University. She also has affiliations with the Malone Center for Engineering in Healthcare, the Center for Computational Biology, the Department of Genetic Medicine, and the Department of Biochemistry and Molecular Biology. Her research focuses on developing scalable computational methods and open-source software for biomedical data analysis, particularly in the areas of single-cell and spatial transcriptomics genomics data. Her work aims to lead to an improved understanding of human health and disease. Dr. Hicks has been recognized for her professional contributions, leadership, and commitment to the field of statistics, including being named a fellow of the American Statistical Association and being honored as a member of the AIMBE College of Fellows. She has also been invited to the NAM Emerging Leaders Forum, reflecting her status as an outstanding early- and mid-career professional in biomedical science and engineering.

Research topics

• Computer Science
• Biology
• Artificial Intelligence
• Machine Learning
• Data Mining
• Genetics
• Computational biology
• Neuroscience
• Psychology
• Psychiatry
• Medicine
• Data science

Selected publications

• Editorial: Statistical Aspects of Trustworthy Machine Learning

  Journal of Data Science · 2026-01-01

  articleOpen access1st authorCorresponding

  Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Editorial - Statistical Aspects of Trustworthy Machine Learning, Authors: Stephanie C. Hicks, Keegan Korthauer, Xiaotong Shen

  PublisherOA PDFDOI

• A dorsal hippocampus-prodynorphinergic dorsolateral septum-to-lateral hypothalamus circuit mediates contextual gating of feeding

  Neuron · 2026-02-01 · 1 citations

  article
  PublisherDOI

• Deconvolved tumor adipocyte proportions and high grade serous ovarian carcinoma survival

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-19

  articleOpen access

  Background: Single-cell-based analyses of high-grade serous ovarian carcinoma (HGSOC) survival have largely ignored adipocytes, which are fragile and under-represented in single-cell references. Adipocytes are known active components of the tumor microenvironment in many cancers, and HGSOC tumors frequently metastasize to the omentum, a lining of adipose tissue. Methods: cohorts. We used stage-stratified Cox models to quantify the association between intratumoural adipocyte fractions and overall survival while adjusting for age, body mass index (BMI), race, and residual disease. We also evaluated associations with deconvolved immune, stromal, and epithelial cell groups. Results: A 10% increase in estimated tumor adipocyte content was associated with a 41% increase in the hazard of death (HR = 1.41, 95% CI 1.18-1.70, p = 0.0002) after adjusting for age, BMI and race (n=566). A 10% increase in immune cell proportion was associated with favorable survival (HR = 0.82, 95% CI 0.69-0.97, p = 0.024). Stromal and epithelial macro-fractions were not associated with survival. Associations with adipocyte and immune cell type proportions were unchanged in models additionally controlling the other cell type proportions. Results were similar after additionally adjusting for residual disease after debulking surgery. Conclusions: Adipocytes may be a tumor-intrinsic factor associated with adverse outcomes in HGSOC. Quantifying adipocyte burden using bulk RNA-seq could enhance risk stratification and guide the development of adipocyte-targeted therapies.

  PublisherOA PDFDOI

• Spatially resolved molecular sex differences at single-cell resolution in the adult human ventromedial and arcuate hypothalamus

  Cell Reports · 2026-01-29 · 1 citations

  articleOpen access

  The hypothalamus contains multiple regions, including the ventromedial hypothalamus (VMH) and arcuate (ARC), which are responsible for sex-differentiated functions such as endocrine signaling, metabolism, and reproductive behaviors. While molecular, anatomic, and sex-differentiated features of the rodent hypothalamus are well established, much less is known about these regions in humans. Here, we provide a spatially resolved single-cell atlas of sex-differentially expressed (sex-DE) genes in the human ARC and VMH. We identify neuronal populations governing hypothalamus-specific functions, define their spatial distributions, and show enrichment of sex-DE in retinoid metabolism- and retinoid receptor-regulated genes. Within the ARC and VMH, we find correlated autosomal expression differences localized to ESR1/TAC3-expressing and corticotropin-releasing hormone receptor 2 (CRHR2)-expressing neurons and extensive sex-DE genes linked to sex-biased disorders, including autism, depression, and schizophrenia. Our molecular mapping of disease associations to hypothalamic cell types with established roles in sex-divergent physiology and behavior provides insights into the mechanistic bases of sex bias in neurodevelopmental and neuropsychiatric disorders.

  PublisherDOI

• Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-17

  articleOpen access

  The dorsolateral prefrontal cortex (dlPFC) is central to cognitive dysfunction in schizophrenia (SCZ), yet how molecular changes are organized across cortex remains unclear. Here, we applied complementary spatial transcriptomic approaches spanning laminar domains, microenvironments, and cell types in postmortem human dlPFC. At the laminar level, SCZ-associated transcriptional changes were strongest in glia-enriched domains (layer 1/meninges and white matter), including down-regulation of microglia-associated genes, whereas genetic risk localized to neuronal-rich gray matter. We next analyzed SCZ-linked microenvironments including neuropil, neuronal, perineuronal net, and vascular compartments. Within these, neuronal and synaptic transcriptional changes were most prominent in neuropil, showing down-regulation of activity-dependent synaptic genes and inhibitory neuron markers. At the cellular level, these signals reflected intrinsic alterations localized to cell types. Across analyses, patterns converged on altered BDNF-TrkB signaling and inhibitory circuit dysfunction. Together, our findings highlight spatial scale as a key determinant in resolving neuronal and non-neuronal aspects of SCZ-associated biology.

  PublisherOA PDFDOI

• Impact of Alzheimer’s disease risk factors and local neuromelanin content on the transcriptomic landscape of the human locus coeruleus

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-31

  preprintOpen access

  Abstract The locus coeruleus (LC) is a small noradrenergic nucleus in the dorsal pons that sends projections across the brain regulating sleep, arousal, attention, stress responses, and some forms of cognition. LC neurons show pathology in the earliest stages of Alzheimer’s disease (AD), including age-related accumulation of hyperphosphorylated tau (pTau) and accelerated loss of neuromelanin (NM) pigmentation. NM-sensitive neuroimaging of the LC predicts previous cognitive decline, clinical severity, and future AD progression. While these findings suggest that the LC plays an etiologic role in AD, the molecular landscape of the LC prior to clinical manifestation of sporadic AD remains largely uncharacterized. This information is critical for developing interventions that preserve LC integrity and function. We performed spatially-resolved transcriptomics on 85 sections of human postmortem LC from N =33 neurotypical middle-aged donors, balanced for epidemiologic AD risk factors including sex, African or European ancestry, and APOE genotype (carriers of the E4/risk or E2/protective alleles). Comparing across APOE genotypes, we find astrocytic gene expression differences proximal to LC neurons. Associating NM content with local gene expression, we show that higher overall APOE gene expression correlates with reduced NM content and an enrichment of NM-associated genes in aging pathways. Unexpectedly, we find enriched LC expression of cholesterol synthesis genes, alongside evidence for lipid synthesis gene regulatory network activity in NM-containing LC specifically, revealing a potential intersection between intrinsic lipid metabolism in LC neurons, NM, and the role of APOE-mediated lipid biology in AD. Together, these data illuminate the molecular features of the human LC at spatial resolution with unprecedented sampling depth, revealing how AD risk factors and NM content influence resilience and susceptibility of this critical brain nucleus to pathology accumulation and degeneration.

  PublisherOA PDFDOI

• Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-17 · 2 citations

  preprintOpen accessCorresponding

  In the human brain, the dorsal anterior cingulate cortex (dACC) plays key roles in various components of cognitive control, and is particularly relevant for reward processing and conflict monitoring. The dACC regulates expression of fear and pain, and its dysfunction is implicated in a number of neuropsychiatric disorders. Compared to more recently specialized neocortical areas, such as the dorsolateral prefrontal cortex (dlPFC), the dACC is evolutionarily older. The region's agranular structure, and other evolutionary specializations, such as the presence of von Economo neurons (VENs), contribute to its specialized roles in cognitive and emotional processing. Here, we generated paired spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the dACC in ten adult neurotypical donors to define molecular profiles for dACC cell types and spatial domains. Using non-negative matrix factorization (NMF), we integrated these data by identifying gene expression patterns within the snRNA-seq data, which were projected onto the SRT data to infer the spatial localization. Combining these data with publicly available resources, we revealed insights about molecular profiles, spatial topography, enrichment of disease risk, and putative connectivity of spatially-localized dACC cell types, including VENs. Utilizing published dlPFC snRNA-seq and SRT data collected in the same neurotypical brain donors used here, we deployed cross-region comparison analyses between dACC and dlPFC to understand spatio-molecular specializations and laminar organization across human brain evolution. To make this comprehensive molecular resource accessible to the scientific community, we made both raw and processed data freely available, including through interactive web applications.

  PublisherDOI

• ClustSIGNAL identifies cell types and subtypes using an adaptive smoothing approach for scalable spatial clustering

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-02

  articleOpen accessCorresponding

  Abstract The increased uptake of high-resolution spatially-resolved transcriptomics (SRT) technologies demands the development of unsupervised methods to extract cell types and their spatial distribution from biological tissues. However, unsupervised clustering is challenging due to the sparsity of the data and the differences in cell arrangement within tissues. Here, we introduce ClustSIGNAL, a spatial clustering method that adaptively uses neighbourhood information to overcome data sparsity and perform cell type clustering. ClustSIGNAL first defines initial clusters and sub-clusters of cells with similar gene expression patterns. For each cell, a fixed neighbourhood size is defined, and entropy is calculated based on the proportion of initial subclusters in the neighbourhood to capture its composition. Cell-specific weights, generated from entropy values, are used to embed spatial information into the gene expression through adaptive smoothing. The transformed gene expression is then used for clustering cell types. We compared our adaptive smoothing approach with other smoothing scenarios on four simulated datasets of varying spatial complexity. We also evaluated our clustering method on four publicly available high-resolution SRT datasets and compared its performance to that of three other spatial clustering methods. We showed that ClustSIGNAL performs multi-sample clustering with high accuracy and can identify subtle cell types and subtypes of biological relevance. It is also robust to changes in spatial structure of tissues, segmentation errors, and sparsity. Overall, ClustSIGNAL stabilises gene expression of cells in homogeneous neighbourhoods and preserves distinct gene expression of cells in heterogeneous regions, effectively balancing the use of neighbouring cells as prior knowledge for downstream analysis. The ClustSIGNAL R/Bioconductor package is available from bioconductor.org/packages/clustSIGNAL .

  PublisherDOI

• Orchestrating Spatial Transcriptomics Analysis with Bioconductor

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-21 · 3 citations

  preprintOpen access

  Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

  PublisherOA PDFDOI

• Quantifying the Alignment of a Data Analysis Between Analyst and Audience

  Journal of Data Science · 2025-06-12 · 2 citations

  articleOpen accessSenior author

  A challenge that data scientists face is building an analytic product that is useful and trustworthy for a given audience. Previously, a set of principles for describing data analyses were defined that can be used to create a data analysis and to characterize the variation between analyses. Here, we introduce a concept called the alignment of a data analysis, which is between the data analyst and an audience. We define an aligned data analysis as the matching of principles between the analyst and the audience for whom the analysis is developed. In this paper, we propose a model for evaluating the alignment of a data analysis and describe some of its properties. We argue that more generally, this framework provides a language for characterizing alignment and can be used as a guide for practicing data scientists to building better data products.

  PublisherOA PDFDOI

Recent grants

• Statistical Methods for the Normalization and Quantification of Single-Cell RNA-Sequencing Data

  NIH · $124k · 2016–2018

• Statistical Methods for the Normalization and Quantification of Single-Cell RNA-Sequencing Data

  NIH · $725k · 2018–2021

Frequent coauthors

• Keri Martinowich

  122 shared

• Kristen R. Maynard

  Lieber Institute for Brain Development

  107 shared

• Thomas M. Hyde

  Johns Hopkins University

  81 shared

• Leonardo Collado‐Torres

  72 shared

• Stephanie C. Page

  Lieber Institute for Brain Development

  68 shared

• Joel E. Kleinman

  Lieber Institute for Brain Development

  57 shared

• Madhavi Tippani

  Lieber Institute for Brain Development

  51 shared

• Heena R. Divecha

  Lieber Institute for Brain Development

  49 shared

Labs

• Stephanie Hicks LabPI

Education

• MA, PhD, Statistics

  Rice University

  2013

• BS, Mathematics

  Louisiana State University

  2007

Awards & honors

• Hicks, Spangler named to AIMBE College of Fellows (2026)
• Stephanie Hicks named fellow of the American Statistical Ass…
• Stephanie Hicks invited to NAM Emerging Leaders Forum (2024)