A5078625226- Single-cell and spatial transcriptomics
- Neuroinflammation and Neurodegeneration Mechanisms
- Gene expression and cancer classification
- Stochastic processes and statistical mechanics
- Immune cells in cancer
- Lymphoma Diagnosis and Treatment
- Cell Image Analysis Techniques
- Theoretical and Computational Physics
- Genomics and Rare Diseases
- Genetic Associations and Epidemiology
- Stochastic processes and financial applications
- interferon and immune responses
- Cancer-related molecular mechanisms research
- Neurogenesis and neuroplasticity mechanisms
- Financial Risk and Volatility Modeling
- Visual perception and processing mechanisms
- Neutrophil, Myeloperoxidase and Oxidative Mechanisms
- Statistical Methods and Inference
- Functional Brain Connectivity Studies
- Cancer Immunotherapy and Biomarkers
- Cancer Genomics and Diagnostics
- Neural dynamics and brain function
- Genomics and Chromatin Dynamics
- Genetics and Neurodevelopmental Disorders
- Gene Regulatory Network Analysis
University of Michigan–Ann Arbor
2025
Dana-Farber Cancer Institute
2020-2024
Broad Institute
2020-2023
Dimagi (United States)
2023
Massachusetts Institute of Technology
2022-2023
IIT@MIT
2021
Stanford University
2017
Abstract The function of the mammalian brain relies upon specification and spatial positioning diversely specialized cell types. Yet, molecular identities types their positions within individual anatomical structures remain incompletely known. To construct a comprehensive atlas in each structure, we paired high-throughput single-nucleus RNA sequencing with Slide-seq 1,2 —a recently developed transcriptomics method near-cellular resolution—across entire mouse brain. Integration these datasets...
The function of the mammalian brain relies upon specification and spatial positioning diversely specialized cell types. Yet, molecular identities types, their positions within individual anatomical structures, remain incompletely known. To construct a comprehensive atlas types in each structure, we paired high-throughput single-nucleus RNA-seq with Slide-seq, recently developed transcriptomics method near-cellular resolution, across entire mouse brain. Integration these datasets revealed...
We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases observed that rare, penetrant mutations in implicated by genome-wide association studies confer ~10-fold larger effects than variants the same genes. Consequently, an individual at phenotypic extreme greatest risk severe, early-onset disease is better identified few rare collective action many weak effects. By combining across phenotype-associated into unified genetic model, we...
Abstract Spatial transcriptomic technologies measure gene expression at increasing spatial resolution, approaching individual cells. However, a limitation of current is that measurements may contain contributions from multiple cells, hindering the discovery cell type-specific patterns localization and expression. Here, we develop Robust Cell Type Decomposition (RCTD, https://github.com/dmcable/RCTD ), computational method leverages type profiles learned single-cell RNA sequencing data to...
Channel-encoding models offer the ability to bridge different scales of neuronal measurement by interpreting population responses, typically measured with BOLD imaging in humans, as linear sums groups neurons (channels) tuned for visual stimulus properties. Inverting these form predicted channel responses from measurements humans seemingly offers potential infer tuning Here, we test make inferences about neural width inverted encoding models. We examined contrast invariance orientation...
Abstract Spatial transcriptomics technologies permit the study of spatial distribution RNA at near-single-cell resolution genome-wide. However, feasibility studying allele-specific expression (ASE) from these data remains uncharacterized. Here, we introduce spASE, a computational framework for detecting and estimating ASE. To tackle challenges presented by cell type mixtures low signal to noise ratio, implement hierarchical model involving additive smoothing splines. We apply our method...
A key challenge in cancer research is to identify the secreted factors that contribute tumor cell survival. Nowhere this more evident than Hodgkin lymphoma, where malignant Reed Sternberg (HRS) cells comprise only 1-5% of mass, remainder being infiltrating immune presumably are required for survival HRS cells. Until now, there has been no way characterize complex lymphoma microenvironment at genome scale. Here, we performed genome-wide transcriptional profiling with spatial and single-cell...
Abstract Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed (DE) within specific cell types across tissue context. We show current approaches cannot learn type-specific DE due changes in type composition space and fact measurement units often detect transcripts from more than one type. Here, we introduce statistical...
We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases observed that rare, penetrant mutations in implicated by genome-wide association studies confer ∼10-fold larger effects than variants the same genes. Consequently, an individual at phenotypic extreme greatest risk severe, early-onset disease is better identified few rare collective action many weak effects. By combining across phenotype-associated into unified genetic model, we...
We consider a stochastic process that describes several particles interacting by either merging or annihilation. When two merge, they combine their masses; when annihilation occurs, only the particle of smallest mass survives. Particles start at bottom binary tree depth N and move towards root. Assuming happens independently random, we determine limit law final system in large limit.
Abstract Allele-specific expression (ASE), or the preferential of one allele, can be observed in transcriptomics data from early development throughout lifespan. However, prevalence spatial and cell type-specific ASE variation remains unclear. Spatial technologies permit study patterns genome-wide at near-single-cell resolution. are highly sparse, confounding between type location present further statistical challenges. Here, we introduce spASE ( https://github.com/lulizou/spase ), a...
Abstract Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. The detection of genes that are differentially expressed across tissue context for cell types interest is an essential challenge dissecting pathological mechanisms. However, changes in type composition space and measurement contamination from other introduce complex statistical challenges. Here, we a method, Generalized Linear Admixture Models Differential Expression...