Stephanie C. Hicks

Learning interpretable compositional features associated with continuous covariates

This paper introduces IsoVita, a framework for learning interpretable compositional features from continuous covariates, showing improved detection of temporal isoform variation in human brain RNA-seq data.

Transcript quantification using context-aware MAPQ modeling

CALMeR improves transcript quantification by context-aware MAPQ modeling, recovering more spike-in transcripts and better matching known concentrations.

Overlapping yet distinct spatiomolecular profiles across mood disorders and biological sex in the human prefrontal cortex

The paper uses spatial transcriptomics to analyze molecular differences in mood disorders and biological sex in the human prefrontal cortex, identifying sex- and disorder-specific gene expression patterns.

ALBIS: simulation of multi-resolution and multi-dimensional spatial transcriptomics data

ALBIS generates synthetic spatial transcriptomics data across multiple resolutions and dimensions to enable benchmarking of computational methods.

Pretrained gene representations transfer mean expression more broadly than spatial patterns in virtual spatial transcriptomics

This study evaluates how pretrained gene representations contribute to virtual spatial transcriptomics by separating their effects on mean expression and spatial patterns across multiple datasets.

Three dimensional reconstruction of the human nucleus accumbens reveals topographic organization and molecular heterogeneity of D1-islands across the anterior posterior axis

This paper develops a 3D spatial genomics framework to reconstruct the molecular and cellular organization of the human nucleus accumbens across anatomical axes.

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

ClustSIGNAL improves spatial transcriptomics clustering by adapting smoothing based on neighbourhood complexity, enabling scalable analysis of large datasets.

Interpretable spherical geometry of single-cell state transitions from dominant principal components

The paper introduces SPHERE-PCA, a method to interpret single-cell state transitions using spherical geometry, revealing structured patterns in developmental and disease datasets.

SCG: Spatially Co-Expressed Gene Identification through Spatially Varying Networks

This paper introduces SCR, a Bayesian framework for estimating spatially-resolved gene co-expression networks to identify spatially co-expressed genes as potential biomarkers and therapeutic targets.

Multiscale spatial transcriptomics resolves the cellular and molecular architecture of the human amygdala

This study creates a spatial transcriptomic atlas of the human amygdala, revealing distinct cell types and genetic risk localization across its subnuclei.

Mind the Alignment Gap: A Spatial Transcriptomics Benchmark for Scientific Coding Agents

The paper introduces a benchmark for scientific coding agents in spatial transcriptomics alignment, showing that richer context improves tool exploration but reduces alignment accuracy.

Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens

This study creates a spatiomolecular atlas of the human nucleus accumbens using spatial transcriptomics and single-nucleus RNA sequencing, revealing spatial gradients and conserved features across species.

BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection

BatchSVG identifies batch-biased genes in spatially variable gene detection to improve downstream analyses by removing technical artifacts.

Perturbation of genes linked to common schizophrenia risk variants identifies cilia programs

The paper perturbs schizophrenia risk genes in mice to identify ciliary transcriptional programs as a potential downstream mechanism of disease.

Spatially-resolved integration of microglia morphological diversity and gene expression using Visium with protein co-detection

The paper integrates microglia morphology and gene expression using spatial transcriptomics and proteogenomics to identify morphology-related genes in the human brain.

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

The paper integrates spatial transcriptomics and single-nucleus RNA-seq data from the human dACC to define molecular profiles, spatial domains, and functional insights.

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

This study maps molecular and genetic signatures of schizophrenia in human prefrontal cortex across spatial scales, revealing altered BDNF-TrkB signaling and inhibitory circuit dysfunction.

SpatialArtifacts: a computational framework for tissue artifact detection in spatial transcriptomics data

SpatialArtifacts identifies and classifies tissue artifacts in spatial transcriptomics data using MAD-based outlier detection and mathematical morphology operations.

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

The paper identifies a circuit involving DLS Pdyn neurons that links contextual cues to feeding regulation through inhibition of LHA GABAergic neurons.

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

The paper creates a single-cell atlas of sex-differentially expressed genes in human hypothalamus regions, revealing molecular sex differences and their links to neurodevelopmental disorders.

Deconvolved tumor adipocyte proportions and high grade serous ovarian carcinoma survival

The paper deconvolves adipocyte proportions from bulk RNA-seq data of HGSOC tumors and finds higher adipocyte content is associated with worse survival.

Spotsweeper-py: spatially-aware quality control metrics for spatial omics data in the Python ecosystem

SpotSweeper-py provides spatially-aware quality control metrics for spatial omics data in Python, improving accuracy by reducing false positives and preserving tissue-specific architecture.

spaTransfer: transfer learning for single-cell and spatial transcriptomics data using non-negative matrix factorization

spaTransfer transfers cell type or spatial domain labels between single-cell and spatial transcriptomics datasets using non-negative matrix factorization and a multinomial model.

Dynamic changes in mRNA isoform usage during human retinal organoid development

The paper uses long-read RNA sequencing to study dynamic mRNA isoform changes during human retinal organoid development, revealing splicing patterns and allelic imbalance.

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

The paper provides an open-source online book for analyzing spatial transcriptomics data using Bioconductor in R, with reproducible code and datasets.

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

The paper analyzes the transcriptomic landscape of the human locus coeruleus in relation to Alzheimer's disease risk factors and neuromelanin content using spatially-resolved transcriptomics.

Transcriptomic diversity of amygdalar subdivisions across humans and nonhuman primates

The paper analyzes transcriptomic diversity in amygdalar subdivisions across humans and nonhuman primates using single-nucleus RNA sequencing and identifies distinct neuronal subtypes and transcriptional specializations.

Characterizing intra- and inter-tumor heterogeneity in ovarian high-grade serous carcinoma subtypes using single-cell and spatial transcriptomics

This study uses single-cell and spatial transcriptomics to analyze intra- and inter-tumor heterogeneity in ovarian HGSC subtypes, identifying spatial patterns and cell-cell communication differences.

An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus

The paper integrates single-nucleus and spatial transcriptomics data to map the molecular landscape of the human hippocampus, identifying cell types and spatial domains.

Spatial mutual nearest neighbors for spatial transcriptomics data

The paper introduces spatialMNN, an algorithm that integrates spatial transcriptomics data to identify spatial domains by incorporating 2D spatial information into mutual nearest neighbors analysis.