Jean Fan

STARIT converts spatial transcriptomics data into image tensors for deep learning analysis, enabling subcellular heterogeneity-based cell state identification.

Serval is a modular framework for decoding spatial transcriptomics data that introduces Cosine, a method improving transcript recovery and correlation with expression references.

The paper shows new cerebellar nuclei evolve through diversification of excitatory progenitors and spatial segregation, with inhibitory neurons incorporated later.

STcompare identifies differentially spatially patterned genes by comparing spatial correlation and fold-change across structurally matched tissues in spatial transcriptomics data.

This study examines how data quality affects deep learning predictions of spatial gene expression from histology images, showing that improving data quality can enhance predictive performance.

The paper develops a spatial transcriptomic method to analyze hair follicle development in 3D, revealing molecular stages and structural changes over time.

The paper uses spatial transcriptomics to analyze cold ischemia injury in murine kidneys, identifying compartment-specific metabolic changes and providing an interactive browser for results.

The paper uses spatial transcriptomics to analyze cardiomyocyte states in pressure-overloaded mouse hearts, finding pro-fibrotic states linked to microenvironmental changes.

The paper identifies off-target probe binding in 10x Genomics Xenium gene panels and develops a tool to assess its impact on spatial transcriptomic accuracy.

The paper analyzes endothelial cell environments across organs using spatially resolved omics data and provides an interactive tool for visualizing cell distances and neighborhoods.

This study identifies functional senescence subtypes in aging human dermal fibroblasts using single-cell morphology and machine learning, showing subtype-specific responses to senotherapies.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

The paper introduces scatterbar, an R package for visualizing proportional data across spatial coordinates, demonstrating its use on spatial transcriptomics data.

The paper introduces CRAWDAD, a tool for analyzing cell-type spatial relationships across scales in spatially resolved omics data, and demonstrates its application on simulated and real datasets.

SEraster reduces computational resource needs for spatial omics analysis by aggregating cell data into spatial pixels, enabling efficient analysis of large datasets.

This study evaluates how different normalization methods affect the analysis of spatially resolved transcriptomics data, showing that gene count-based normalization can introduce biases in downstream analyses.

This paper introduces STalign, a method for aligning spatial transcriptomics data using diffeomorphic metric mapping, improving alignment accuracy over affine methods.

This paper introduces STdeconvolve, a reference-free method for cell-type deconvolution of spatially resolved transcriptomics data at multi-cellular pixel resolution, showing effective recovery of cell-type profiles without external references.

VeloViz creates RNA-velocity-informed 2D and 3D embeddings to visualize cellular trajectories from single-cell transcriptomics data, showing consistent results across diverse topologies.

ExSeq enables multiplexed RNA mapping at nanoscale resolution by expanding biological specimens for in situ sequencing.

Harmony alternates diversity-penalized soft clustering with cluster-specific linear correction of PCA embeddings, integrating single-cell datasets over multiple covariates and handling 500,000 cells in 7.2GB of memory.

Unspliced versus spliced mRNA in standard scRNA-seq gives RNA velocity, a per-cell vector predicting future state over hours, validated in neural crest and used to trace mouse hippocampal lineages.

The paper develops two single-cell methods to analyze human brain cells, identifies neuronal subtypes and non-neuronal cell types, and links genetic risk factors to specific cell types.

UBiT2 is a client-side web application for analyzing RNA-seq and qPCR data, offering offline analysis and visualization with interactive tools and exportable figures.

This study compares PCA and t-SNE with distance metric modifications for visualizing and classifying single-cell RNA-seq data, finding that variable gene selection is critical for reliable classification.

This study introduces PAGODA, a method to detect transcriptional heterogeneity by analyzing pathway and gene set overdispersion, and applies it to characterize neuronal progenitors in the developing mouse cortex.