Lujia Chen
University of Pittsburgh, PA·Computer Science
Cells in a tissue constantly signal to one another, and those conversations shape how each cell behaves. No two tissue samples are alike.
- Fields
Computer Science
Mathematics & Statistics
Genetics
- Topics
cell-cell communication
causal discovery
causal Bayesian network
- Grants
- 1 known grant, through Feb 2030
What they work on
- Develop a nested hierarchical Dirichlet process model that finds gene expression modules, groups of genes likely driven by one signaling pathway, in single-cell transcriptome data.
- Identify ligand-receptor pairs that transmit specific signals to cells expressing each gene expression module, linking cell-to-cell signaling to downstream gene programs.
- Create an individualized causal Bayesian network (iCBN) method that learns the cell-cell communication network within each bulk RNA-seq sample or spatial transcriptomic region.
- Compare summarized communication networks between groups such as normal versus disease, young versus old, and treatment responders versus non-responders to find subgroup-specific patterns.
More about the research
A tumor, an inflamed lung, or a developing embryo each contains its own mix of cell types and states. Understanding which cells influence which, and how this differs between people, could explain disease and guide personalized treatment. It is hard to work out from data, however.
Why it matters
Mapping how cells communicate within an individual patient's tissue could reveal why tumors or inflamed tissues differ between people. It may point to new drug targets and biomarkers for specific patient subgroups, such as those who respond or fail to respond to treatment. The tools could eventually support precision medicine guided by each patient's cell communication network.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Yang Ni
University of Texas at Austin, TX·Mathematics & Statistics
Develop a Bayesian joint model of treatment, outcome, covariates, and hidden confounders that identifies causal effects from observational data without instrumental or proxy variables.
Wenpin Hou
Duke University, NC·Genetics
Develop statistical methods that infer static and dynamic gene regulatory networks, including time-lagged regulator effects, from single-cell multiomics data that combine gene activity and epigenomic measurements across many samples.
Scott Atwood
University of California-Irvine, CA·Biology
Define the stem cell states and transition states human epidermal progenitors pass through as they form layered skin, using single-cell RNA sequencing of organoids grown on engineered substrates.
The school
University of Pittsburgh: 83 labs on PI Pages, $1.5B a year on research, 403 research doctorates a year.
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