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The Chen Laboratory is currently interested in the following research areas:

Single-cell/spatial transcriptomics, multiomics and statistics, such as

• : germline and somatic single nucleotide variant (SNV) calling from scRNA-seq, scATAC-seq, etc.
• : integrating datasets obtained from different single-cell technologies including scRNA-seq, scATAC-seq, spatial omics and CyTOF
• : pathway/geneset-centric single-cell/spatial transcriptomic data analysis
• : selecting most informative features/genes/proteins in a single-cell dataset.

Cancer metabolism modeling

• : Estimate metabolic fluxes from transcriptomic data at tissue and single-cell resolution and model metabolic interaction of cells/cell-types in the tissue microenvironment.

Cell phenotypical plasticity and cellular dynamics

• : identify cells transitioning out of steady-states (attractor basin) based on dynamical systems therapy.

Cell Therapeutic modeling/simulation

• : modeling cellular dynamics in adoptive cell therapy for personalized therapy using cellular mechanistic models (e.g., agent based models).

Clonal heterogeneity and evolution modeling:

• : allele-specific copy number and RNA expression deconvolution of autologous DNA/RNA-seq data
• : single-cell phylogenetic tree from somatic mutations detected in single cell sequencing data
• : single-cell phylogenetic tree from somatic copy number alterations detected in single cell sequencing data

Tumor-Immune systems biology (particularly, adoptive cell therapy)

• , HPV+ H&N cancer checkpoint therapy
• , CAR-NK cell signaling
• , CAR-NK cell metabolism
  
•  therapy trial
  
• , , 

Artificial intelligence in cancer research:

• Knowledge graph construction using LLM/NLP from literature (e.g., , and )
• Cancer prognosis (e.g., )
• Protein structural-modeling (e.g., AlphaFold2)
  
• Algorithms for big biomedical data analysis
  
• Early detection and longitudinal predictive modeling (e.g., )
• Regulatory network, e.g., DNA->RNA, RNA->protein
• Relation extraction from biomedical literature

Structural and copy number alterations in human genomes, including:

• : structural variant detection from NGS data
• : de novo breakpoint assembly from NGS data

Systems/network biological approaches for molecular target discovery

Clinical applications (including trial data analysis) and decision support:

• : alignment-free, targeted genotyping/clonality characterization of clinical mutations
• : trans-level annotation of mutations across DNA, RNA and proteins.

VISIT our github: